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• Y. Saad, Da Gao, T. Ngo, S. Bobbitt, J. R. Chelikowsky and W. Andreoni, Phys. Rev. B85, 104104(2012)[Data mining][AB compounds]
• L. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl and M. Scheffler, Phys. Rev. Lett., Vol. 114, No. 10, 105503(2015)[Big data][Critical role][Descriptor]
• S. Bhattacharya and G. K. H. Madsen, Phys. Rev. B92, 085205(2015)[High-throughput exploration][Alloying][Design strategy][Thermoelectrics]
• M. Nakayama, M. Kimura, R. Jalem and T. Kasuga, Japanese Journal of Applied Physics, Vol. 55, Number 1S, 01AH05(2016)[Efficient automatic screening][Bond valence pathway model][Percolation algorithm]
• Atsuto Seko, Atsushi Togo, Hiroyuki Hayashi, Koji Tsuda, Laurent Chaput, and Isao Tanaka, Phys. Rev. Lett., 115, No. 20, 205901(2015)(http://arXiv.org/abs/1506.06439)[Low thermal conductivity][Anharmonic lattice dynamics][Bayesian optimization]
• Yuji Umeda, Hiroyuki Hayashi, Hiroki Moriwake and Isao Tanaka, Jpn. J. Appl. Phys. 57 11UB01 (2018)[Materials informatics][Dielectric material]
• D. M. Packwood and T. Hitosugi, Appl. Phys. Express 10, 065502(2017)[Rapid prediction][Molecule arrangement][Metal surface][Bayesian optimization]
• S. Miyagawa, K. Gotoh, K. Kutsukake, Y. Kurokawa and N. Usami, Appl. Phys. Express 14, 025503(2021)[Bayesian optimization][Improved passivation performance][Heterostructure][Hydrogen plasma treatment]
• K. Tada, Appl. Phys. Express 14, 046502(2021)[Bayesian estimation][Equivalent circuit parameter][Photovoltaiccell]
• Shin Kiyohara, Hiromi Oda, Koji Tsuda and Teruyasu Mizoguchi, Jpn. J. Appl. Phys. 55 (2016) 045502[Acceleration][Stable interface structure][Searching][kriging approach]
• ¡Ê¥¤¥ó¥Õ¥©¥Þ¥Æ¥£¥¯¥¹¤È¤½¤ì¤Ë´ØÏ¢¤·¤½¤¦¤Ê¾ðÊó¡Ë
• https://arxiv.org/abs/2405.11404[How big is Big Data?]
• https://arxiv.org/abs/2312.04600[Haldane bundles][Dataset for learning][Predict][Chern number][Line bundle][Torus]
• https://arxiv.org/abs/2502.14234[OBELiX][Curated dataset][Crystal structure][Experimentally measured ionic conductivities][Lithium solid-state electrolyte]
• https://arxiv.org/abs/2401.00035[Learning about structural errors][Models of complex dynamical systems]
• https://arxiv.org/abs/2309.07844[Predicting][Mechanical properties][Spring network]
• https://arxiv.org/abs/2409.05556[SciAgents][Automating scientific discovery][Multi-agent intelligent graph reasoning]
• https://arxiv.org/abs/2407.06053[Learning local equivariant representation][Quantum operator]
• https://arxiv.org/abs/2409.06498[Learning local and semi-local density functionals][Exact exchange-correlation potentials and energies]
• https://arxiv.org/abs/2409.04737[CrysAtom][Distributed representation of Atoms][Crystal property prediction]
• https://arxiv.org/abs/2412.04670[Lattice Lingo][Effect][Textual detail][Multimodal learning][Property prediction of crystals]
• https://arxiv.org/abs/2307.05390[CrysMMNet][Multimodal representation][Crystal property prediction]
• https://arxiv.org/abs/2403.11996[Accelerating scientific discovery][Generative knowledge extraction][Graph-based representation][Multimodal intelligent graph reasoning]
• https://arxiv.org/abs/2411.08414[Material property prediction][Element attribute knowledge graph][Multimodal representation learning]
• https://arxiv.org/abs/2411.16955[Probing][Limitation][Multimodal language model][Chemistry and materials research]
• https://arxiv.org/abs/2503.00152[Invariant tokenization][Crystalline material][Language model][Enabled generation]
• https://arxiv.org/abs/2411.08664[UniMat][Unifying material][Embedding][Multi-modal learning]
• https://arxiv.org/abs/2307.05380[Optimized crystallographic graph generation][Material science]
• https://arxiv.org/abs/2305.16486[Disorder-driven localization][Electron interaction][Bi_xTeI thin film]
• https://arxiv.org/abs/2302.14102[Connectivity optimized nested graph network][Crystal structure]
• https://arxiv.org/abs/2406.13265[Molecule graph network][Many-body equivariant interaction]
• https://arxiv.org/abs/2404.06628[Quantum graph model][Transport][Filamentary switching]
• https://arxiv.org/abs/2501.16886[Unconventional resistive switching][Dense Ag-based nanowire network][Brain-inspired perspective]
• https://arxiv.org/abs/2304.02354[Neural cellular automata][Solidification microstructure modelling]
• https://arxiv.org/abs/2309.16195[Reconstructing microstructure][Statistical descriptor][Neural cellular automata]
• https://arxiv.org/abs/2305.19856[Cellular automata][Inspired][Multistable origami metamaterial][Mechanical learning]
• https://arxiv.org/abs/2203.02160[Reservoir computing][Spin wave][Skyrmion crystal]
• https://arxiv.org/abs/2209.06962[Task-adaptive physical reservoir computing]
• https://arxiv.org/abs/2412.06964[Magneto-ionic physical reservoir computing]
• https://arxiv.org/abs/2301.02193[Universal scaling][Wave speed and size][Nanoscale high-performance reservoir computing][Propagating spin-wave]
• https://arxiv.org/abs/2307.09138[Few-molecule reservoir computing][Experimentally demonstrated][Surface enhanced Raman scattering][Ion-gating stimulation]
• https://arxiv.org/abs/2310.06497[Video recognition][Physical reservoir computing][Magnetic material]
• https://arxiv.org/abs/2310.11743[Moire synaptic transistor][Homogeneous-architecture reservoir computing][Chin. Phys. Lett. 40(11), 117201 (2023)]
• https://arxiv.org/abs/2401.15067[Universality condition][Unified classical][Quantum reservoir computing]
• https://arxiv.org/abs/2409.09886[Memory-augmented quantum reservoir computing]
• https://arxiv.org/abs/2404.04023[Phase binarization][Mutually synchronized][Bias field-free spin Hall nano-oscillator][Reservoir computing]
• https://arxiv.org/abs/2406.13825[Low frequency noise][Nanoparticle-molecule network][Implication][In-materio reservoir computing]
• https://arxiv.org/abs/2412.09800[Infinite-dimensional][Next-generation][Reservoir computing]
• https://arxiv.org/abs/2412.13212[Introduction][Reservoir computing]
• https://arxiv.org/abs/2412.20308[Chaotic proliferation][Relativistic domain wall][Reservoir computing]
• https://arxiv.org/abs/1902.07495[Beyond scaling relation][Description of catalytic materials]
• https://arxiv.org/abs/1802.06900[Exploring][High-pressure][Materials genome]
• https://arxiv.org/abs/1905.00601[Exploring inorganic and nontoxic double perovskites][Material selection][Device design][Material genome approach]
• https://arxiv.org/abs/1809.09202[Map][Inorganic ternary metal nitride]
• https://arxiv.org/abs/2111.10874[Dataset][Solution-based inorganic materials synthesis recipe][Extracted][Scientific literature]
• https://arxiv.org/abs/2206.08917[Open catalyst 2022 (OC22) dataset][Challenge][Oxide electrocatalysis]
• https://arxiv.org/abs/2008.07016[Contact map][Crystal structure prediction][Global optimization]
• https://arxiv.org/abs/1705.08613[Crystal structure prediction][Incomplete experimental data]
• https://arxiv.org/abs/2309.04475[Crystal structure prediction][Joint equivariant diffusion]
• https://arxiv.org/abs/2502.02026[ContinuouSP][Generative model][Crystal structure prediction][Invariance and continuity]
• https://arxiv.org/abs/2503.10471[Siamese foundation model][Crystal structure prediction]
• https://arxiv.org/abs/2306.03099[CrystalGPT][Enhancing system-to-system transferability][Crystallization prediction][Control][Time-series-transformer]
• https://arxiv.org/abs/2201.11203[AI-aided mapping][Structure-composition-conductivity relationship][Glass-ceramic lithium thiophosphate electrolyte]
• https://arxiv.org/abs/2206.14634[AI powered][Automated discovery][Polymer membrane][Carbon capture]
• https://arxiv.org/abs/2309.03398[Automated discovery][Wurtzite solid solution][Enhanced piezoelectric response]
• https://arxiv.org/abs/2311.13808[Automated model training][(AMT) GUI][Opportunity][Integrating AI][Laboratory experiment]
• https://arxiv.org/abs/2303.09453[Knowledge discovery][Atomic structure][Feature importance]
• https://arxiv.org/abs/2311.10205[Case study][Multi-modal][Multi-institutional data management][Combinatorial materials science community]
• https://arxiv.org/abs/2501.18064[Learning metal microstructural heterogeneity][Spatial mapping][Diffraction latent space feature]
• http://arXiv.org/abs/1509.02515[Dimensionless mapping][Combinatorial algorithm][Design invisible dopants]
• http://arxiv.org/abs/1607.07826[High throughput combinatorial method][Fast and robust prediction][Lattice thermal conductivity]
• https://arxiv.org/abs/2112.06649[Hypothesis learning][Automated experiment][Application][Combinatorial materials libraries]
• https://arxiv.org/abs/2310.15124[Mixed-variable global sensitivity analysis][Knowledge discovery][Efficient combinatorial materials design]
• https://arxiv.org/abs/2412.17283[Emerging microelectronic material][Design][Navigating combinatorial design space][Scarce and dispersed data]
• https://arxiv.org/abs/2502.08696[Scalable discrete diffusion sampler][Combinatorial optimization][Statistical physics]
• https://arxiv.org/abs/2106.01616[Data management plan][Importance of data management][BIG-MAP project]
• http://arXiv.org/abs/1602.06402[Band structure diagram paths][Crystallography]
• http://arxiv.org/abs/1501.00691[Distribution of phonon lifetime]
• http://arxiv.org/abs/1503.07503[Motif-based fingerprints]
• http://arxiv.org/abs/1503.07327[Global structural prediction]
• https://arxiv.org/abs/2108.13590[Mining insight][Metal-organic framework synthesis][Scientific literature text]
• http://arxiv.org/abs/0808.2125[Data mining]
• https://arxiv.org/abs/1709.03151[Novel organic high-T_c superconductors][Data mining][Density of states similarity search]
• https://arxiv.org/abs/1710.07721[Data mining][Better material synthesis][Pulsed laser deposition][complex oxides]
• https://arxiv.org/abs/2006.14128[Identifying candidate host][Quantum defect][Data mining]
• https://arxiv.org/abs/2103.03199[Data mining][Dashboard][Statistical analysis][Powerful framework][Chemical design][Molecular nanomagnet]
• https://arxiv.org/abs/2109.04929[Data mining][Terahertz generation crystal]
• https://arxiv.org/abs/2111.07892[Data privacy protection][Microscopic image analysis][Material data mining]
• https://arxiv.org/abs/2502.18522[Rewards-based image analysis][Microscopy]
• https://arxiv.org/abs/2206.00076[Exploring][Two-dimensional van der Waals heavy-fermion material][Data mining theoretical approach]
• https://arxiv.org/abs/2301.05691[Structural phase transition][Perovskite][BaCeO₃][Revisited][Data mining-cum-first-principles theoretical approache]
• https://arxiv.org/abs/2502.14904[Towards an automated workflow][Materials science][Combining multi-modal simulative][Experimental information][Data mining][Large language model]
• https://arxiv.org/abs/2007.02085[Data-mining][Element Charge][Inorganic material]
• https://arxiv.org/abs/2012.14815[Data-mining][Dislocation microstructure][Coarse-graining][Internal energy]
• https://arxiv.org/abs/2206.11355[Data-mining][In-Situ TEM experiment][Towards understanding][Nanoscale fracture]
• https://arxiv.org/abs/2210.00478[Data-mining][In-situ TEM Experiment][Dynamics][Dislocation][CoCrFeMnNi Alloy]
• https://arxiv.org/abs/2306.00363[Database mining][First-principles assessment][Organic proton-transfer][Ferroelectric]
• https://arxiv.org/abs/2405.20217[Data-efficient fine-tuning][Foundational model][First-principles quality sublimation enthalpies]
• https://arxiv.org/abs/2306.07138[Discovering ferroelectric plastic (ionic) crystal][Cambridge structural database][Database mining][Computational assessment]
• https://arxiv.org/abs/2409.12182[LifeGPT][Topology-agnostic generative pretrained transformer model][Cellular automata]
• https://arxiv.org/abs/2405.19076[Cephalo][Multi-modal vision-language model][Bio-inspired materials analysis and design]
• https://arxiv.org/abs/2406.17295[MatText][Language model][Text & scale][Materials modeling]
• https://arxiv.org/abs/2306.11296[ChatGPT chemistry assistant][Text mining][Prediction][MOF synthesis]
• https://arxiv.org/abs/2109.15290[MatSciBERT][Materials domain language model][Text mining][Information extraction]
• https://arxiv.org/abs/2311.06303[MatNexus][Comprehensive text mining][Analysis suite][Materials discover]
• https://arxiv.org/abs/2502.20860[Electrocatalyst discovery][Text mining][Multi-objective optimization]
• https://arxiv.org/abs/2408.04661[MaterioMiner][Ontology-based text mining dataset][Extraction][Process-structure-property entities]
• https://arxiv.org/abs/2410.21317[MatExpert][Decomposing materials discovery][Mimicking human expert]
• https://arxiv.org/abs/2502.05576[Human-AI collaboration][Modeling heat conduction][Nanostructure]
• https://arxiv.org/abs/1806.05772[Network analysis][Synthesizable materials discovery]
• https://arxiv.org/abs/2503.08104[Functional unit][New perspective][Materials science research paradigm]
• https://arxiv.org/abs/2409.14572[Evaluating][Performance][Robustness][LLM][Materials science Q&A][Property prediction]
• https://arxiv.org/abs/2503.08537[Chemical reasoning][LLMs unlocks steerable synthesis planning][Reaction mechanism elucidation]
• https://arxiv.org/abs/2501.16358[OpenLAM][Challenge]
• https://arxiv.org/abs/2501.10385[Autonomous microscopy experiment][Large language model agent]
• https://arxiv.org/abs/2306.06283[14 Examples][How LLMs can transform materials science and chemistry][Reflection][Large language model hackathon]
• https://arxiv.org/abs/2411.15221[Reflection][2024 large language model (LLM) hackathon][Application][Materials science and chemistry]
• https://arxiv.org/abs/2411.03484[Automated][LLM][Enabled extraction][Synthesis detail][Reticular material][Scientific literature]
• https://arxiv.org/abs/2310.07197[MatChat][Large language model][Application service platform][Materials science]
• https://arxiv.org/abs/2310.08511[HoneyBee][Progressive instruction finetuning][Large language model][Materials Science]
• https://arxiv.org/abs/2310.19998[Generative retrieval-augmented ontologic graph][Multi-agent strategies][Interpretive large language model-based materials design]
• https://arxiv.org/abs/2311.08166[MechAgents][Large language model][Multi-agent collaboration][Mechanics problem][Generate new data][Integrate knowledge]
• https://arxiv.org/abs/2311.13778[Accurate prediction][Experimental band gap][Large language model-based data extraction]
• https://arxiv.org/abs/2407.16867[From text to insight][Large language model][Materials science data extraction]
• https://arxiv.org/abs/2503.12326[Leveraging vision capabilities][Multimodal LLMs][Automated data extraction][Plot]
• https://arxiv.org/abs/2410.20976[Large language model-guided prediction][Quantum materials synthesis]
• https://arxiv.org/abs/2412.03831[Large language model-type architecture][High-dimensional molecular potential energy surface]
• https://arxiv.org/abs/2312.01291[Opportunities][Retrieval][Tool augmented large language model][Scientific facilities]
• https://arxiv.org/abs/2403.03154[Quantum many-body physics calculation][Large language model]
• https://arxiv.org/abs/2403.06949[Materials science][Era][Large language model][Perspective]
• https://arxiv.org/abs/2403.19783[AlloyBERT][Alloy property prediction][Large language model]
• https://arxiv.org/abs/2406.13163[LLMatDesign][Autonomous materials discovery][Large language model]
• https://arxiv.org/abs/2406.15499[Exploring large language model][Microstructure evolution][Materials][Materials Today Communications, 40, 109583 (2024)]
• https://arxiv.org/abs/2407.07016[Large language model][Predict][Synthesizability][Precursors of crystal structures]
• https://arxiv.org/abs/2409.03444[Fine-tuning large language model][Domain adaptation][Exploration][Training strategies][Scaling][Model merging][Synergistic capabilities]
• https://arxiv.org/abs/2409.06080[Regression][Large language model][Materials and molecular property Prediction]
• https://arxiv.org/abs/2409.06756[Beyond designer's knowledge][Generating materials design hypotheses][Large language model]
• https://arxiv.org/abs/2409.13732[TopoChat][Enhancing topological materials retrieval][Large language model][Multi-source knowledge]
• https://arxiv.org/abs/2410.23405[FlowLLM][Flow matching][Material generation][Large language model][Base distribution]
• https://arxiv.org/abs/2411.05421[Learning the rules][Peptide self-assembly][Data mining][Large language model]
• https://arxiv.org/abs/2411.12280[Large language model][Material property prediction][Elastic constant tensor prediction][Materials design]
• https://arxiv.org/abs/2409.03161[MaterialBENCH][Evaluating college-level materials science][Problem-solving abilities][Large language model]
• https://arxiv.org/abs/2412.09560[Foundational large language model][Materials research]
• https://arxiv.org/abs/2412.10477[Benchmarking large language model][Materials synthesis][Atomic layer deposition]
• https://arxiv.org/abs/2501.00564[Large language model-driven database][Thermoelectric material]
• https://arxiv.org/abs/2501.04277[Exploring][Expertise][Large language model][Materials science][Metallurgical engineering]
• https://arxiv.org/abs/2501.17279[Engineering point defect][MoS₂][Tailored material properties][Large language model]
• https://arxiv.org/abs/2502.20933[Large language model][Innate crystal structure generator]
• https://arxiv.org/abs/2503.13833[Causal discovery][Data assisted by large language model]
• https://arxiv.org/abs/2305.08264[MatSci-NLP][Evaluating scientific language model][Materials science language task][Text-to-schema modeling]
• https://arxiv.org/abs/2310.00475[Generative design][Inorganic compound][Deep diffusion language model]
• https://arxiv.org/abs/2204.11953[Crystal transformer][Self-learning neural language model][Generative][Tinkering design]
• https://arxiv.org/abs/2206.13578[Materials transformer][Language model][Generative materials design][Benchmark study]
• https://arxiv.org/abs/2209.14803[polyBERT][Chemical language model][Enable fully machine-driven ultrafast polymer informatics]
• https://arxiv.org/abs/2410.12375[PRefLexOR][Preference-based recursive language modeling][Exploratory optimization][Reasoning and agentic thinking]
• https://arxiv.org/abs/2501.08120[In-situ graph reasoning][Knowledge expansion][Graph-PReFLexOR]
• https://arxiv.org/abs/2209.08203[ChemNLP][Natural language processing based library][Materials chemistry text data]
• https://arxiv.org/abs/2310.10445[MechGPT][Language-based strategy][Mechanics][Materials modeling][Connect][Knowledge across scale][Discipline][Modalities]
• https://arxiv.org/abs/2310.11458[GPT-4][Interface][Researcher][Computational software][Improving usability][Reproducibility]
• https://arxiv.org/abs/2312.05468[Image and data mining][Reticular chemistry][GPT-4V]
• http://arxiv.org/abs/1109.6935[Possible high-temperature superconductors][Data-filtering algorithms]
• http://arxiv.org/abs/1007.4838[Strong topological insulators]
• http://arxiv.org/abs/1504.01163 ¢«AiiDA´ØÏ¢
• AiiDA(Automated Interactive Infrastructure and Database for Atomistic simulations)
• https://arxiv.org/abs/2003.12476[AiiDA 1.0][Scalable computational infrastructure][Automated reproducible workflow][Data provenance]
• https://arxiv.org/abs/2007.10312[Workflow][AiiDA][Engineering][High-throughput][Event-based engine][Robust][Modular computational workflow]
• https://arxiv.org/abs/2010.02731[AiiDAlab][Ecosystem][Developing][Executing][Sharing][Scientific workflow]
• https://arxiv.org/abs/2303.12465[AiiDA-defects][Automated][Fully reproducible workflow][Complete characterization][Defect chemistry][Functional material]
• https://arxiv.org/abs/2301.11689[Rule-free workflow][Automated generation][Database][Scientific literature]
• https://arxiv.org/abs/2309.10923[Semi-automatic staging area][High-quality structured data extraction][Scientific literature]
• https://arxiv.org/abs/2302.04397[Designing workflow][Materials characterization]
• https://arxiv.org/abs/2408.12732[Segment anything model][Grain characterization][Hard drive design]
• https://arxiv.org/abs/1706.08704[Seamlessly share computed materials properties][Full provenance][Integration][AiiDA][TCOD]
• https://arxiv.org/abs/2107.09619[Open-science platform][Computational materials science][AiiDA][Materials cloud][Andreoni W., Yip S. (eds.), Handbook of Materials Modeling. Springer, Cham (2018)]
• https://arxiv.org/abs/2304.03949[Capturing dynamical correlations][Implicit neural representation]
• https://arxiv.org/abs/2502.00550[Muti-fidelity prediction][Uncertainty quantification][Laplace neural operator][Parametric partial differential equation]
• https://arxiv.org/abs/2408.15852[chemtrain][Learning deep potential model][Automatic differentiation][Statistical physics]
• https://arxiv.org/abs/2203.08205[Learning deep implicit Fourier neural operators][IFNOs][Application][Heterogeneous material modeling]
• https://arxiv.org/abs/2207.03239[Learning][Stress-strain field][Digital composite][Fourier neural operator]
• https://arxiv.org/abs/2305.00478[Domain agnostic][Fourier neural operator]
• https://arxiv.org/abs/2503.14568[Teaching artificial intelligence][Perform rapid][Resolution-invariant grain growth modeling][Fourier neural operator]
• https://arxiv.org/abs/2403.18597[Heterogeneous peridynamic neural operator][Discover biotissue constitutive law][Microstructure][Digital Image Correlation Measurement]
• https://arxiv.org/abs/2411.06529[Thermodynamically-informed iterative neural operator][Heterogeneous elastic localization]
• https://arxiv.org/abs/2503.13695[Mitigating spectral bias][Neural operator][High-frequency scaling][Physical system]
• https://arxiv.org/abs/2003.08315[AiiDA-KKR plugin][Application to high-throughput impurity embedding][Topological insulator]
• https://arxiv.org/abs/2111.15229[AiiDA-spirit plugin][Automated spin-dynamics simulation][Multi-scale modelling][First-principles calculation]
• https://arxiv.org/abs/2003.12510[Materials Cloud][Platform][Open computational science]
• https://arxiv.org/abs/2210.11301[Novel materials][Materials cloud 2D database]
• HP5 - Materials Informatics(NCCR-MARVEL)
• http://arxiv.org/abs/1504.01219[VdW]
• http://arxiv.org/abs/1505.03994[First-principles interatomic potentials]
• https://arxiv.org/abs/2503.10492[Meta-learning characteristics][Dynamics][Quantum system]
• https://arxiv.org/abs/2111.11120[Meta-learning][Interatomic potential model][Accelerated materials simulation]
• https://arxiv.org/abs/2205.06643[Design space][E(3)-equivariant][Atom-centered interatomic potential]
• https://arxiv.org/abs/1801.09897[Open material property library][Native simulation tool integration][MASTO]
• https://arxiv.org/abs/2410.12771[Open Materials 2024][OMat24][Inorganic materials dataset and models]
• https://arxiv.org/abs/2502.02582[Open materials generation][Stochastic interpolant]
• https://arxiv.org/abs/2503.00522[Periodic materials generation][Text-guided joint diffusion model]
• https://arxiv.org/abs/2107.03017[Self-learning hybrid Monte Carlo method][Isothermal-isobaric ensemble][Application to liquid silica]
• https://arxiv.org/abs/2305.05060[Genomic materials design][CALculation of PHAse Dynamics]
• https://arxiv.org/abs/1809.04785[Tracking atomic structure evolution][Directed electron beam induced Si-atom motion][Graphene][Deep machine learning]
• https://arxiv.org/abs/2110.14006[Deep machine learning potential][Multicomponent metallic melt][Development][Predictability][Compositional transferability]
• https://arxiv.org/abs/2201.11835[Deep machine learning potential][Atomistic simulation][Fe-Si-O system][Earth's outer core condition]
• https://arxiv.org/abs/2208.04119[Deep machine learning][Reconstructing lattice topology][Strong thermal fluctuation]
• https://arxiv.org/abs/2404.17852[Melting temperature][Iron under the Earth's inner core condition][Deep machine learning]
• https://arxiv.org/abs/2203.00393[Deep potential][Materials science]
• https://arxiv.org/abs/2201.04243[Two wrongs can make a right][Transfer learning approach][Chemical discovery][Chemical accuracy]
• https://arxiv.org/abs/2303.03000[Transfer learning][Large dataset][Accurate prediction][Material properties]
• https://arxiv.org/abs/1909.11234[Exploring diamond-like lattice thermal conductivity crystal][Feature-based transfer learning]
• https://arxiv.org/abs/2107.13841[Addressing materials' microstructure diversity][Transfer learning]
• https://arxiv.org/abs/2308.13096[Electronic structure prediction][Multi-million atom system][Uncertainty quantification][Enabled transfer learning]
• https://arxiv.org/abs/2308.13917[Transfer learning][Microstructure segmentation][CS-UNet][Hybrid algorithm][Transformer][CNN Encoder]
• https://arxiv.org/abs/2310.11423[Predicting polymerization reaction][Transfer learning][Chemical language model]
• https://arxiv.org/abs/2311.06179[Cluster expansion][Transfer learning][Empirical potential]
• https://arxiv.org/abs/2403.07526[Physics-transfer learning][Material strength screening]
• https://arxiv.org/abs/2403.12982[Knowledge-reuse transfer learning method][Molecular and material science]
• https://arxiv.org/abs/2407.20975[Transfer learning][Multi-material classification][Transition metal dichalcogenide][Atomic force microscopy]
• https://arxiv.org/abs/2208.13294[Leveraging low-fidelity data][Improve machine learning][Sparse high-fidelity][Thermal conductivity data][Transfer learning]
• https://arxiv.org/abs/2408.04042[Scaling law][Sim2Real transfer learning][Expanding computational materials database][Real-world prediction]
• https://arxiv.org/abs/2408.11322[Transfer learning][Early estimation][Single-photon source quality][Machine learning method]
• https://arxiv.org/abs/2410.15120[Generalizable prediction model][Molten salt mixture density][Chemistry-informed transfer learning]
• https://arxiv.org/abs/2501.09505[Transfer learning][Many-body electronic correlation entropy][Local measurement]
• https://arxiv.org/abs/2501.12452[Transfer learning electronic structure][Millielectron volt accuracy][Sub-million-atom moire semiconductor]
• https://arxiv.org/abs/2503.16784[Multi-property directed generative design][Inorganic material][Wyckoff-augmented transfer learning]
• https://arxiv.org/abs/2409.15675[Northeast materials database (NEMAD)][Enabling discovery of high transition temperature magnetic compound]
• https://arxiv.org/abs/2304.13076[Redundancy][Large material dataset][Efficient and robust learning][Less data]
• https://arxiv.org/abs/2312.00038[Physics-constrained NeuralODE approach][Robust learning][Stiff chemical kinetics]
• https://arxiv.org/abs/2502.03638[SymmCD][Symmetry-preserving crystal generation][Diffusion model]
• https://arxiv.org/abs/2310.15994[Training model][Force][Stochastic electronic structure method]
• https://arxiv.org/abs/1611.05481[Predicting][Lattice thermal conductivity][Solving the Boltzmann transport equation][AFLOW][AAPL an automated][Accurate and effcient framework]
• https://arxiv.org/abs/2312.14936[PerCNet][Periodic complete representation][Crystal graph]
• https://arxiv.org/abs/1907.00907[Self-learning projective quantum Monte Carlo simulation][Restricted Boltzmann machine]
• https://arxiv.org/abs/1810.02352[Efficient representation][Topologically ordered state][Restricted Boltzmann machine]
• https://arxiv.org/abs/1809.08631[Efficient algorithmic way][Construct Boltzmann machine representation][Arbitrary stabilizer code]
• https://arxiv.org/abs/1812.08171[Restricted Boltzmann machine][Matrix product state][1D translational invariant stabilizer Codes]
• https://arxiv.org/abs/1903.03076[Ground state phase diagram][One-dimensional Bose-Hubbard model][Restricted Boltzmann machine]
• https://arxiv.org/abs/1910.13454[Multi-layer restricted Boltzmann machine representation][1D quantum many-body wave function]
• https://arxiv.org/abs/2003.07280[Restricted Boltzmann machine representation][Groundstate and excited states][Kitaev Honeycomb model]
• https://arxiv.org/abs/2003.09765[Simulating disordered quantum system][Dense and sparse restricted Boltzmann machine]
• https://arxiv.org/abs/2009.14777[Helping restricted Boltzmann machine][Quantum-state representation][Restoring symmetry]
• https://arxiv.org/abs/2402.02794[Convolutional restricted Boltzmann machine (CRBM)][Correlated variational wave function][Hubbard model][Square lattice][Mott metal-insulator transition]
• https://arxiv.org/abs/2103.08804[Chebyshev expansion][Spectral function][Restricted Boltzmann machine]
• https://arxiv.org/abs/2110.03676[Pruning][Restricted Boltzmann machine][Quantum state reconstruction]
• https://arxiv.org/abs/2202.07576[Accuracy][Restricted Boltzmann Machine][One-dimensional J₁¡ÝJ₂ Heisenberg model]
• https://arxiv.org/abs/2404.11229[Mean field initialization][Annealed importance sampling algorithm][Efficient evaluation][Partition function][Restricted Boltzmann machine]
• https://arxiv.org/abs/2502.04575[Complexity analysis][Normalizing constant estimation][Jarzynski equality][Annealed importance sampling and beyond]
• https://arxiv.org/abs/2407.01451[Representing arbitrary ground state][Toric code][Restricted Boltzmann machine]
• https://arxiv.org/abs/2407.11165[Restricted Boltzmann machine][Modeling complex physical system][Case study][Artificial spin ice]
• https://arxiv.org/abs/2409.06146[Configuration interaction guided sampling][Interpretable restricted Boltzmann machine]
• https://arxiv.org/abs/2412.04103[Restricted Boltzmann machine network][Jastrow correlated wave function][Two-dimensional Hubbard model]
• https://arxiv.org/abs/2005.01547[Atomic Boltzmann machine][Capable][On-chip learning]
• https://arxiv.org/abs/2102.05137[Hardware-aware][In-situ Boltzmann machine learning][Stochastic magnetic tunnel junction]
• https://arxiv.org/abs/2412.12783[Noise-based local learning][Stochastic magnetic tunnel junction]
• https://arxiv.org/abs/2103.04791[Purifying deep Boltzmann machine][Thermal quantum state][Phys. Rev. Lett. 127, 060601 (2021)]
• https://arxiv.org/abs/2302.03212[Statistical approach][Topological entanglement][Boltzmann machine representation][Higher-order irreducible correlation]
• https://arxiv.org/abs/2306.16877[Boltzmann machine][Quantum many-body problem]
• https://arxiv.org/abs/2410.16915[Automatic extraction][Compensation][P-Bit device variation][Large array utilizing Boltzmann machine training]
• https://arxiv.org/abs/2411.00203[Toffoli gadget][Magnetic tunnel junction][Boltzmann machine]
• http://arxiv.org/abs/1506.00303[AFLOW]
• http://arxiv.org/abs/1607.02532[AFLOW][Library of Crystallographic Prototypes]
• https://arxiv.org/abs/1611.05714[Combining the AFLOW GIBBS][Elastic libraries][Efficiently and robustly screening thermo-mechanical properties]
• https://arxiv.org/abs/1711.10744[AFLOW-ML][RESTful API][Machine-learning prediction]
• https://arxiv.org/abs/1909.02255[Self-learning hybrid Monte Carlo][First-principles Approach]
• https://arxiv.org/abs/1612.05130[AFLUX][LUX materials search API][AFLOW data repositories]
• https://arxiv.org/abs/1701.06921[AFLOW¦Ð][A minimalist approach][High-throughput ab initio calculation][Generation of tight-binding hamiltonians]
• https://arxiv.org/abs/1712.00422[AFLOW][Fleet for Materials Discovery]
• https://arxiv.org/abs/1802.07977[AFLOW-SYM][Platform][Complete][Automatic][Self-consistent][Symmetry analysis of crystals]
• https://arxiv.org/abs/1806.06901[AFLOW-CHULL][Cloud-oriented platform][Autonomous phase stability analysis]
• https://arxiv.org/abs/2111.07478[Physics in the Machine][Integrating physical knowledge][Autonomous phase-mapping][CAMEO][AFLOW]
• https://arxiv.org/abs/1806.07864[AFLOW library][Crystallographic prototypes][Part 2]
• https://arxiv.org/abs/1807.04669[AFLOW-QHA3P][Robust and automated method][Compute thermodynamic properties of solids]
• https://arxiv.org/abs/1902.00485[Metallic glass][Biodegradable implant][AFLOW]
• https://arxiv.org/abs/2007.01988[Reproducibility][High-throughput density functional theory][Comparison][AFLOW][Materials Project][OQMD]
• https://arxiv.org/abs/2010.04222[AFLOW-XtalFinder][Reliable choice][Identify crystalline prototypes]
• https://arxiv.org/abs/2012.05961[AFLOW library][Crystallographic prototype][Part 3]
• https://arxiv.org/abs/2101.02724[Automated coordination][Corrected enthalpy][AFLOW-CCE]
• https://arxiv.org/abs/2207.09842[aflow.org][Web ecosystem][Database][Software][Tool]
• https://arxiv.org/abs/2208.03052[aflow++][C++ framework][Autonomous materials design]
• https://arxiv.org/abs/2310.16769[AFLOW for alloys]
• https://arxiv.org/abs/2401.01852[Machine learned interatomic potential][Ternary carbide][AFLOW Database]
• https://arxiv.org/abs/2401.06875[AFLOW library][Crystallographic prototypes][Part 4]
• https://arxiv.org/abs/1805.05309[Automated computation][Materials property]
• https://arxiv.org/abs/2309.02646[Benchmarking inverse optimization algorithm][Molecular materials discovery]
• https://arxiv.org/abs/2005.00707[Benchmarking materials property prediction method][Matbench test set][Automatminer reference algorithm]
• https://arxiv.org/abs/2405.10448[Dynamic in-context learning][Conversational model][Data extraction][Materials property prediction]
• https://arxiv.org/abs/2502.06836[CAST][Cross attention based multimodal fusion][Structure and text][Materials property prediction]
• https://arxiv.org/abs/2503.01022[LLM-Fusion][Novel multimodal fusion model][Accelerated material discovery]
• https://arxiv.org/abs/1710.11611[Online search tool][Graphical patterns][Electronic band structure]
• https://arxiv.org/abs/1808.03383[Pattern learning][Electronic density of states]
• https://arxiv.org/abs/2006.11803[Learning the electronic density of states][Condensed matter]
• https://arxiv.org/abs/1901.09487[2DMatPedia][Open computational database][Two-dimensional materials][Top-down and bottom-up approaches]
• https://arxiv.org/abs/1801.10560[Benchmark database][Transition metal surface][Adsorption energy][Many-body perturbation theory]
• https://arxiv.org/abs/1802.05192[Charge transfer network][Alternative topological understanding][Electronic structure][Proteins Database]
• https://arxiv.org/abs/2111.02081[Molecular bond engineering][Feature learning][Design][Hybrid organic-inorganic perovskites solar cell][Strong non-covalent halogen-cation interaction][SISSO]
• https://arxiv.org/abs/1901.00948[Simultaneous learning][Several materials properties][Incomplete databases][Multi-Task SISSO]
• https://arxiv.org/abs/2305.01242[Recent advances][SISSO method][Implementation][SISSO++ code]
• https://arxiv.org/abs/1902.05241[Database of novel magnetic materials][High-performance][Permanent magnet development]
• https://arxiv.org/abs/2008.10402[Numerical quality control][DFT-based materials database]
• https://arxiv.org/abs/2106.07987[Database construction][Two-dimensional material-substrate interface]
• https://arxiv.org/abs/2311.09891[Some elusive aspects][Databases hindering AI based discovery][Case study][Superconducting material]
• https://arxiv.org/abs/2411.10125[Energy-GNoME][Living database][Selected materials][Energy application]
• https://arxiv.org/abs/2010.15166[Polymer informatics][Multi-Task learning]
• https://arxiv.org/abs/2403.10042[Accurate][Data-efficient micro-XRD phase identification][Multi-task learning][Application][Hydrothermal fluid]
• https://arxiv.org/abs/2405.12229[Multi-task learning][Molecular electronic structure][Approaching coupled-cluster accuracy]
• https://arxiv.org/abs/2205.13757[Representing polymer][Periodic graph][Learned descriptor][Accurate polymer property prediction]
• https://arxiv.org/abs/2306.05261[Representing][Learning function][Invariant][Crystallographic group]
• https://arxiv.org/abs/2303.14046[Update][DScribe library][New descriptors and derivatives]
• https://arxiv.org/abs/2303.14872[Local concentration-based descriptor predicting][Stacking fault energy][Refractory high entropy alloy]
• https://arxiv.org/abs/1902.10838[Data-centric online ecosystem][Digital materials science]
• http://arXiv.org/abs/1511.04373[Modeling Disordered Materials][High Throughput ab-initio Approach]
• http://arXiv.org/abs/1603.06924[High-Throughput prediction][Finite-temperature properties][Quasi-harmonic approximation]
• http://arxiv.org/abs/1605.05188[Topologically close-packed phases][Binary transition-metal compounds][Matching high-throughput][Empirical structure map]
• http://arxiv.org/abs/1602.01725[High-Throughput ab-initio Dilute Solute Diffusion Database]
• https://arxiv.org/abs/1610.04279[High-throughput first principles search][New ferroelectrics]
• https://arxiv.org/abs/1611.06246[Mapping and classifying molecules][High-throughput structural database]
• https://arxiv.org/abs/1711.03426[Search for high entropy alloys][High-throughput][Ab-initio approach]
• https://arxiv.org/abs/1801.02948[High-throughput][Identification][Electrides][All Known Inorganic Materials]
• https://arxiv.org/abs/1811.11702[Surface structure search][Coarse graining][Statistical learning]
• https://arxiv.org/abs/1702.02734[First-principles high-throughput screening][Shape-memory alloy][Energetic][Dynamical][Structural]
• https://arxiv.org/abs/1806.09733[High-throughput screening algorithm][Relativistic Density Functional Theory][Find chelating agent][Separation][Radioactive waste]
• https://arxiv.org/abs/1812.06222[Inorganic electride][High-throughput screening]
• https://arxiv.org/abs/1909.04195[High dielectric ternary Oxide][Crystal structure prediction][High-throughput screening]
• https://arxiv.org/abs/1910.02984[Accurate high-throughput screening][I-II-V 8-electron Half-Heusler compound][Renewable-energy application]
• https://arxiv.org/abs/2004.07991[Band gap engineering][Sublattice mixing][High-throughput screening][From first-principles]
• https://arxiv.org/abs/2004.09489[High-throughput screening][Weyl semimetal][S4 symmetry]
• https://arxiv.org/abs/2006.00183[High-throughput screening][Band gap engineering][Sublattice mixing][Cs₂AgBiCl₆]
• https://arxiv.org/abs/2310.00118[Transforming materials discovery][Artificial photosynthesis][High-throughput screening][Earth-abundant semiconductor]
• https://arxiv.org/abs/2011.05225[High-throughput screening][Thermoelastic][Ultra-high temperature ceramics]
• https://arxiv.org/abs/2103.09652[Identification of materials][Strong magneto-structural coupling][Computational high-throughput screening]
• https://arxiv.org/abs/2104.10590[First-principles-based approach][High-throughput screening][Corrosion-resistant high entropy alloy]
• https://arxiv.org/abs/2205.11085[High-throughput screening][Piezo-photocatalytic material][Hydrogen production]
• https://arxiv.org/abs/2207.03365[High-throughput screening][Half-antiperovskite][Stacked kagome lattice]
• https://arxiv.org/abs/2207.10134[High-throughput screening][Strong electron-phonon coupling][Ternary metal diboride]
• https://arxiv.org/abs/2207.05283[High-throughput screening][Transition metal single-atom catalyst][Nitrogen reduction reaction]
• https://arxiv.org/abs/2212.06289[High-throughput screening assisted discovery][Stable layered anti-ferromagnetic semiconductor][CdFeP₂Se₆]
• https://arxiv.org/abs/2308.01663[High-throughput screening][Weyl semimetal]
• https://arxiv.org/abs/2308.14439[High-throughput screening][Heterogeneous transition metal dual-atom catalyst][Synergistic effect][Nitrate reduction][Ammonia][Adv. Funct. Mater. 33, 2301493 (2023)]
• https://arxiv.org/abs/2309.10274[Prediction][Superconductivity][Metallic boron-carbon compound][From 0 to 100 GPa][High-throughput screening]
• https://arxiv.org/abs/2401.13211[High-throughput screening][Boride superconductor]
• https://arxiv.org/abs/2411.04481[High-throughput screening][Ferrimagnetic semiconductor][Ultrahigh Ne'el temperature]
• https://arxiv.org/abs/2502.20410[Advancing excited-state simulation][TADF emitter][Extended tight-binding framework][High-throughput screening and design]
• https://arxiv.org/abs/1807.05623[Accessible computational materials design][High fidelity][High throughput]
• https://arxiv.org/abs/1807.07821[Looking for new thermoelectric materials][TMX intermetallics][High-throughput calculation]
• https://arxiv.org/abs/2501.11242[High-throughput calculation][Two-dimensional auxetic M₄X₈][Magnetism][Electrocatalysis][Alkali metal battery application]
• https://arxiv.org/abs/1808.02684[High throughput screening][Spin-gapless semiconductor][Quaternary Heusler compound]
• https://arxiv.org/abs/1808.04733[High-throughput descriptor][Predicting potential topological insulators][Tetradymite family]
• https://arxiv.org/abs/1808.05325[Electronic properties of binary compounds][High fidelity][High throughput]
• https://arxiv.org/abs/1809.01132[Designing materials][High refractive index][Wide band gap][High-throughput]
• https://arxiv.org/abs/1809.04751[High-throughput calculation][Thermal conductivity][Nanoporous material][Half-Heusler compound]
• https://arxiv.org/abs/1810.09402[Monopole mining method][High throughput screening][Weyl semimetal]
• https://arxiv.org/abs/1810.11775[ADAIS: Automatic Derivation of Anisotropic Ideal Strength][High-throughput][First-principles]
• https://arxiv.org/abs/1811.05390[Computationally-driven][High throughput identification][CaTe][Li₃Sb][Promising candidate][High mobility][p-type][Transparent conducting]
• https://arxiv.org/abs/1901.05121[Discovery of hidden classes][Layered electride][Extensive high-throughput materials screening]
• https://arxiv.org/abs/1901.02261[First-principles study][Structural transition][LiNiO₂][High throughput screening][Long life battery]
• https://arxiv.org/abs/1903.01286[Determining the optimal phase-change Material][High-throughput calculation]
• https://arxiv.org/abs/1903.11466[High throughput computational screening][2D ferromagnetic material][Critical role of anisotropy][Local correlation]
• https://arxiv.org/abs/1904.06047[Structure map of AB₂ type 2D materials][High-throughput DFT calculation]
• https://arxiv.org/abs/1906.06317[freud][Software suite][High throughput analysis][Particle simulation data]
• https://arxiv.org/abs/2009.06268[High throughput screening][Transition metal binuclear site][N2 fixation]
• https://arxiv.org/abs/1909.10189[High throughput production][Transparent conductive single-walled carbon nanotube film][Advanced floating catalyst chemical vapor deposition]
• https://arxiv.org/abs/2206.09943[High throughput investigation][Emergent][Naturally abundant 2D material][Clinochlore]
• https://arxiv.org/abs/1907.03811[High-throughput extraction][Anisotropic interdiffusion coefficient][hcp Mg-Al alloy]
• https://arxiv.org/abs/1909.00433[Automated][High-throughput][Wannierisation]
• https://arxiv.org/abs/2011.10905[Discovery][Extreme work function][High-throughput density functional theory][Machine learning]
• https://arxiv.org/abs/1909.09605[Spin-orbitronic material][Record spin-charge conversion][High-throughput ab initio calculation]
• https://arxiv.org/abs/1909.10395[Accelerated discovery][Two-dimensional optoelectronic octahedral oxyhalide][High-throughput Ab Initio Calculation][Machine learning]
• https://arxiv.org/abs/2406.04537[HTESP][High-throughput electronic structure package][High-throughput ab initio calculation]
• https://arxiv.org/abs/2104.02106[Efficient determination][True magnetic structure][High-throughput ab initio screening][MDMC method]
• https://arxiv.org/abs/2211.14688[High-throughput ab initio reaction mechanism exploration][Cloud][Automated multi-reference validation]
• https://arxiv.org/abs/2311.15430[Application][Batch learning][Boosting high-throughput ab initio success rate][Reducing computational effort][Data-driven processes]
• https://arxiv.org/abs/2402.04726[Phase stability][Mechanical property trend][MAB phase][High-throughput ab initio calculation]
• https://arxiv.org/abs/2503.05640[High-throughput ab initio study][Elemental segregation][Cohesion][Ferritic-iron grain boundaries]
• https://arxiv.org/abs/1909.13297[High-throughput detection][Manipulation][Single nitrogen-vacancy center's charge][Nanodiamond]
• https://arxiv.org/abs/1910.00548[High-throughput][Data-mining][Predict new rare-earth free permanent magnets]
• https://arxiv.org/abs/2410.15948[Automated workflow][Accurate high-throughput][GW Calculation]
• https://arxiv.org/abs/1911.08071[SHRY][Suite for High-throughput generation][Atomic substitution][Implemented by python]
• https://arxiv.org/abs/2101.08814[Efficient implementation][Atom-density representation]
• https://arxiv.org/abs/2007.13012[PyXtal FF][Python library][Automated force field generation]
• https://arxiv.org/abs/2503.08207[To use or not to use][Universal force field]
• https://arxiv.org/abs/1912.09330[High-throughput structural and electrochemical study][Metallic glass formation][Ni-Ti-Al]
• https://arxiv.org/abs/1912.09006[Recent advances][High-throughput superconductivity research]
• https://arxiv.org/abs/2003.00012[High-throughput calculation][Antiferromagnetic topological material][Magnetic topological quantum chemistry]
• https://arxiv.org/abs/2003.03457[Direct and high-throughput fabrication][Mie-resonant metasurface][Single-pulse laser interference]
• https://arxiv.org/abs/2004.14460[High throughput computational screening][Two-dimensional magnetic material][Experimental database][Three-dimensional compound]
• https://arxiv.org/abs/2005.14092[New high-throughput method][Additive manufacturing][Materials design][Processing optimization]
• http://arXiv.org/abs/1603.05967[High-throughput search][New ternary superalloys]
• https://arxiv.org/abs/2006.01075[High-throughput search][Magnetic and topological order][Transition metal oxide]
• https://arxiv.org/abs/2102.00237[High-throughput search][Magnetic topological material][Spin-orbit spillage][Machine-learning][Experiment]
• https://arxiv.org/abs/2306.03095[High-throughput search][Triplet point defect][Narrow emission line][2D material]
• https://arxiv.org/abs/2407.02785[Identifying direct bandgap silicon structures][High-throughput search][Machine learning method]
• https://arxiv.org/abs/2408.11044[High-throughput search][Photostrictive material][Thermodynamic descriptor]
• https://arxiv.org/abs/2409.04632[High-throughput search][Prediction][Layered 4f-material]
• https://arxiv.org/abs/2410.18873[High-throughput search][Topological magnon material]
• https://arxiv.org/abs/2412.10356[High-throughput search][Metallic altermagnet][Embedded dynamical mean field theory]
• https://arxiv.org/abs/2007.01205[Database][Wannier tight-binding Hamiltonian][High-throughput density functional theory]
• https://arxiv.org/abs/2007.14250[High-throughput production][Cheap mineral-based 2D electrocatalyst][High-current-density hydrogen evolution]
• https://arxiv.org/abs/2007.14047[Good practice guide][Electrical characterization][Graphene][Non-contact][High-throughput]
• https://arxiv.org/abs/2008.00283[Crystallography companion agent][High-throughput materials discovery]
• https://arxiv.org/abs/2403.05952[New direction][Thermoelectrics][Roadmap][High-throughput materials discovery][Advanced device manufacturing]
• https://arxiv.org/abs/2008.09784[High-throughput ensemble characterization][Individual core-shell nanoparticle][Quantitative 3D density][XFEL single-particle imaging]
• https://arxiv.org/abs/2103.02039[Demonstration][Laser powder bed fusion combinatorial sample][High-throughput microstructure][Indentation characterization]
• https://arxiv.org/abs/2103.15415[Direct growth][Hexagonal boron nitride][Photonic chip][High-throughput characterization]
• https://arxiv.org/abs/2204.11223[High-throughput characterization][Transition metal dichalcogenide alloy][Thermodynamic stability][Electronic band alignment]
• https://arxiv.org/abs/2212.04804[Computationally accelerated experimental materials characterization][Drawing inspiration][High-throughput simulation workflow]
• https://arxiv.org/abs/2306.17277[Speeding up high-throughput characterization][Materials libraries][Active learning][Autonomous electrical resistance measurement]
• https://arxiv.org/abs/2311.14497[Advancing high-throughput combinatorial aging studies][Hybrid perovskite thin-film][Precise automated characterization method][Machine learning assisted analysis]
• https://arxiv.org/abs/2008.12907[High-throughput][Design][Magnetic material]
• https://arxiv.org/abs/2009.00735[Open][Strong-scaling Tool][Atom probe crystallography][High-throughput method][Indexing crystal structure and orientation]
• https://arxiv.org/abs/2009.01638[MAELAS][Magneto-elastic property][Computational high-throughput approach]
• https://arxiv.org/abs/2010.00660[Thermochemical database][High-throughput][First-principles calculation][Application][Analyzing phase evolution][AM-fabricated IN718]
• https://arxiv.org/abs/2010.03543[High-throughput technique][Measuring][Spin Hall effect]
• https://arxiv.org/abs/2010.07168[Tin pest problem][Test of density functionals][High-throughput calculation]
• https://arxiv.org/abs/2102.05604[Scalable multicomponent spectral analysis][High-throughput data annotation]
• https://arxiv.org/abs/2011.08134[High throughput search][Efficient thermoelectric][Half-Heusler compound]
• https://arxiv.org/abs/2009.12519[High-throughput design][Peierls][Charge density wave][Q1D organometallic material]
• https://arxiv.org/abs/2102.06180[High-throughput][Rapid experimental alloy development][HT-READ]
• https://arxiv.org/abs/1901.09323[Prediction][Silicate glasses' stiffness][High-throughput MD][Machine learning]
• https://arxiv.org/abs/1910.02336[Database-driven high-throughput calculation][Machine learning model][Materials Design]
• https://arxiv.org/abs/2502.04984[FF7][Code package][High-throughput calculation][Constructing materials database]
• https://arxiv.org/abs/2011.10382[Machine learning][High-throughput][Robust design][P3HT-CNT composite thin film][High electrical conductivity]
• https://arxiv.org/abs/2106.02946[Training][Machine-learning driven Gaussian approximation potential][Si-H Interaction]
• https://arxiv.org/abs/2212.04811[Machine-learning driven synthesis][TiZrNbHfTaC₅][High-entropy carbide]
• https://arxiv.org/abs/2406.17676[Understanding phase transition][alpha-quartz][Dynamic compression condition][Machine-learning driven atomistic simulation]
• https://arxiv.org/abs/2102.12785[High-throughput nanoindentation mapping][Cast IN718 nickel-based superalloy][Nb concentration]
• https://arxiv.org/abs/2103.00513[Linear-superelastic][Ti-Nb nanocomposite alloy][Ultralow modulus][High-throughput][Phase field simulation and design]
• https://arxiv.org/abs/1909.03511[Beyond ternary OPV][High-throughput experimentation][Self-driving laboratory][Optimize multi-component system]
• https://arxiv.org/abs/2407.08270[SciQu][Accelerating materials properties prediction][Automated literature mining][Self-driving laboratories]
• https://arxiv.org/abs/2412.12347[AutoSciLab][Self-driving laboratory][Interpretable scientific discovery]
• https://arxiv.org/abs/2009.08529[Complex solid solution][Electrocatalyst][Discovery][Prediction][High-throughput experimentation]
• https://arxiv.org/abs/2104.10235[Accelerated discovery][Molten salt corrosion-resistant alloy][High-throughput experimental and modeling method][Coupled to data analytics]
• https://arxiv.org/abs/2104.00864[Constrained non-negative matrix factorization][Enabling real-time insight][In situ][High-throughput experiment]
• https://arxiv.org/abs/2105.05160[Research data infrastructure][High-throughput experimental materials science]
• https://arxiv.org/abs/2104.04173[High-throughput imaging measurement][Thermoelectric figure of merit]
• https://arxiv.org/abs/2104.11150[Towards high-throughput superconductor discovery][Machine learning]
• https://arxiv.org/abs/2105.01329[Microstructural][Compositional design][Mo-V-Nb-Ti-Zr multi-principal element alloy][High-throughput first-principles study]
• https://arxiv.org/abs/1810.10640[High-throughput discovery][Topological material][Spin-orbit spillage]
• https://arxiv.org/abs/2105.02296[High-throughput discovery][High-temperature conventional superconductor]
• https://arxiv.org/abs/2202.05962[High-throughput discovery][Chemical structure-polarity relationship][Combining][Automation][Machine learning]
• https://arxiv.org/abs/2404.19613[High-throughput discovery][Metal oxides][High thermoelectric performance][Interpretable feature engineering][Small data]
• https://arxiv.org/abs/2102.01880[High-throughput discovery][Novel cubic crystal material][Deep generative neural network]
• https://arxiv.org/abs/2410.08501[High-throughput discovery][Kagome material][Transition metal oxide monolayer]
• https://arxiv.org/abs/2412.13431[High-throughput discovery][Robust room-temperature superconductor][Complex ternary clathrate hydride]
• https://arxiv.org/abs/2503.15782[High-throughput discovery][Anti-gap semiconductor]
• https://arxiv.org/abs/2307.10728[Sampling][Whole materials space][Conventional superconducting material]
• https://arxiv.org/abs/1807.01273[High-throughput investigation][Tunable][Superconductivity][FeSe film]
• https://arxiv.org/abs/2106.11985[High-throughput investigation][Topological][Nodal][Superconductor][Phys. Rev. Lett. 129, 027001 (2022)]
• https://arxiv.org/abs/2412.01280[Realization][Hopf-link structure][Phonon spectra][Symmetry guidance][High-throughput investigation]
• https://arxiv.org/abs/2107.03966[DFTTK][DFT tool kit][High-throughput calculation][Thermodynamic][Finite temperature]
• https://arxiv.org/abs/2112.03900[Computational synthesis][2D Material][High-throughput approach][Materials Design]
• https://arxiv.org/abs/2112.09543[Exploring][Cs-Te phase space][High-throughput density-functional theory calculation][beyond the generalized-gradient approximation]
• https://arxiv.org/abs/2201.04213[High-throughput determination][Hubbard U][Hund J][Transition metal oxide][Linear response formalism]
• https://arxiv.org/abs/2201.09059[High-throughput calculation][Combining machine learning][Corrosion properties][Binary Mg alloy]
• https://arxiv.org/abs/2501.02110[High-throughput calculation][Spin Hall conductivity][Non-magnetic 2D material]
• https://arxiv.org/abs/2208.09647[Combining machine learning][Many-body calculation][Coverage-dependent adsorption][CO on Rh(111)][Phys. Rev. Lett. 130, 078001 (2023)]
• https://arxiv.org/abs/2204.07534[Ultra-fast spectroscopy][High-throughput][Interactive quantum chemistry]
• https://arxiv.org/abs/2204.08963[Rapid screening][High-throughput ground state prediction]
• https://arxiv.org/abs/2204.14065[Holistic determination][Optoelectronic properties][High-throughput spectroscopy][Surface-guided CsPbBr₃ nanowire]
• https://arxiv.org/abs/2205.13510[Strong-scaling][Open-source tool][High-throughput quantification][Material point cloud data][Composition gradient][Microstructural object reconstruction][Spatial correlation]
• https://arxiv.org/abs/2403.13627[Efficient exploration][High-Tc superconductor][Gradient-based composition design]
• https://arxiv.org/abs/2205.13661[High-throughput nanopore fabrication][Classification][FIB irradiation][Automated pore edge analysis]
• https://arxiv.org/abs/2206.00078[Magnetic hourglass fermion][Exhaustive symmetry condition][High-throughput materials prediction]
• https://arxiv.org/abs/2206.01969[Doped graphene quantum dot][UV-vis absorption spectrum][High-throughput TDDFT study]
• https://arxiv.org/abs/2206.07108[Framework][Optimal selection][High-throughput data collection workflow][Autonomous experimentation system]
• https://arxiv.org/abs/2502.10828[Vendiscope][Algorithmic microscope][Data collection]
• https://arxiv.org/abs/2207.00364[High-throughput analysis][Fröhlich-type polaron model]
• https://arxiv.org/abs/2207.09569[Indirect band gap semiconductor][Thin-film photovoltaics][High-throughput calculation][Phonon-assisted absorption]
• https://arxiv.org/abs/2208.06097[High-throughput][Condensed-phase hybrid density functional theory][Large-scale finite-gap system][SeA approach]
• https://arxiv.org/abs/2209.02918[High-throughput][Optical absorption spectra][Inorganic semiconductor]
• https://arxiv.org/abs/2210.01367[MyElas][Automatized tool-kit][High-throughput calculation][Post-processing][Visualization][Elasticity][Related properties][Solid]
• https://arxiv.org/abs/2210.03486[Ultrasonic delamination][Adhesion testing][High-throughput][van der Waals heteroepitaxy]
• https://arxiv.org/abs/2210.05654[Influence][Chemistry][Structure][Interfacial segregation][NbMoTaW][High-throughput atomistic simulation]
• https://arxiv.org/abs/2210.17428[EC-MOF/Phase-I][Computationally ready database][Electrically conductive metal-organic framework][High-throughput structural and electronic properties]
• https://arxiv.org/abs/2211.05254[High-throughput DFT-based discovery][Next generation][Two-dimensional (2D) superconductor]
• https://arxiv.org/abs/2211.16685[High-throughput][Ab Initio design][Atomic interface][InterMatch]
• https://arxiv.org/abs/2301.06407[High-throughput many-body perturbation theory][Efficient algorithm][Automated workflow]
• https://arxiv.org/abs/2401.05875[GPTArticleExtractor][Automated workflow][Magnetic material database construction]
• https://arxiv.org/abs/2301.08841[mkite][Distributed computing platform][High-throughput][Materials simulation]
• https://arxiv.org/abs/2301.10839[Sub-picosecond carrier dynamics][Automated high-throughput studies][Doping inhomogeneity][Bayesian framework]
• https://arxiv.org/abs/2408.08843[Automated high-throughput organic crystal structure prediction][Population-based sampling]
• https://arxiv.org/abs/2302.09966[High-throughput-based examination][Density functional effect][Intrinsic properties][SnS]
• https://arxiv.org/abs/2303.01594[High-throughput identification][Spin-photon interface][Silicon]
• https://arxiv.org/abs/2303.07542[Asynchronous parallel high-throughput model][Calibration framework][Crystal plasticity finite element constitutive model]
• https://arxiv.org/abs/2303.09226[Magnetic electride][High-throughput material screening][Intriguing properties][Application]
• https://arxiv.org/abs/2304.04149[High-throughput][Alloy and process design][Metal additive manufacturing]
• https://arxiv.org/abs/2305.07867[Integrated system built][Small-molecule semiconductor][High-throughput approaches]
• https://arxiv.org/abs/2306.09300[Exploring magnetism][Lead-free halide double perovskite][High-throughput first-principles study]
• https://arxiv.org/abs/2306.17092[High-throughput design][All-d-metal Heusler alloy][Magnetocaloric application]
• https://arxiv.org/abs/2306.16508[Surface chemical modification][Adhesion][Metallic interface][High-throughput analysis]
• https://arxiv.org/abs/2307.14250[High-throughput density functional theory screening][Double transition metal MXene precursor]
• https://arxiv.org/abs/2308.16174[High-throughput assessment][Microstructural stability][Segregation-engineered nanocrystalline][Al-Ni-Y alloy]
• https://arxiv.org/abs/2309.04822[High-throughput first-principles calculation][Screening][Coherent topologically close-packed precipitate][Hexagonal close-packed metallic system]
• https://arxiv.org/abs/2012.04790[High-throughput computational search][Two-dimensional binary alloy][Energetic stability][Synthesizability][Three-dimensional counterpart]
• https://arxiv.org/abs/2202.02021[Mutual modulation][Charge transfer][Unpaired electrons][Catalytic site][Superior intrinsic activity][N₂ reduction][High-throughput computation][Machine learning perspective]
• https://arxiv.org/abs/2203.08699[High-throughput computation][Li-based battery material database][Chemistry-processing-property relationship]
• https://arxiv.org/abs/2204.03628[High-throughput computational discovery][40 ultralow thermal conductivity][20 highly anisotropic crystalline materials]
• https://arxiv.org/abs/2205.14286[High-throughput computation][Structure prototype analysis][Two-dimensional ferromagnetic material]
• https://arxiv.org/abs/2207.05353[Absorption][Adsorption][High-throughput computation][Impurities][2D material]
• https://arxiv.org/abs/2209.15423[High-throughput computation][Raman spectra][First principles]
• https://arxiv.org/abs/2302.04896[High-throughput computational dataset][Halide perovskite alloy]
• https://arxiv.org/abs/2306.11116[Na in Diamond][High spin defect][Revealed][ADAQ high-throughput computational database]
• https://arxiv.org/abs/1912.10593[Screening promising thermoelectric material][Binary chalcogenide][High-throughput Computation]
• https://arxiv.org/abs/2002.05803[Genuine correlation][Piezoelectric][Two-dimensional material][High-throughput computational study]
• https://arxiv.org/abs/2008.06838[High-throughput computational characterization][Two-dimensional][Compositionally complex][Transition-metal chalcogenide alloy]
• https://arxiv.org/abs/2009.08048[High-throughput computational-experimental screening protocol][Discovery][Bimetallic catalyst]
• http://arXiv.org/abs/1602.07784[MPInterfaces][Materials project][Python tool][High-throughput Computational Screening][Interfacial system]
• https://arxiv.org/abs/1806.04285[High-throughput computational screening][Two-dimensional semiconductor]
• https://arxiv.org/abs/1909.00623[High-throughput computational screening][Solid-state Li-ion conductor]
• https://arxiv.org/abs/2112.05421[High-throughput computational screening][Bipolar magnetic semiconductor]
• https://arxiv.org/abs/2202.09886[High-throughput computational screening][Nanoporous material][Targeted application]
• https://arxiv.org/abs/2309.08032[Substitutional quantum defect][WS₂][Discovered][High-throughput computational screening][Fabricated][Site-selective STM manipulation]
• https://arxiv.org/abs/2405.04239[Orbital magnetization][Two-dimensional material][High-throughput computational screening]
• https://arxiv.org/abs/2412.06449[High-throughput computational screening][Small][Eco-friendly][Molecular crystal][Sustainable piezoelectric material]
• https://arxiv.org/abs/2502.17946[High-throughput computational screening][Heusler compound][Phonon consideration][Enhanced material discovery]
• https://arxiv.org/abs/2503.08249[High-throughput computation][Machine learning modeling][Magnetic moment][Mössbauer parameter][Fe-based intermetallics]
• https://arxiv.org/abs/2310.00823[Unveiling][Regulatory factor][Phase transition][Zeolitic imidazolate framework][High-Throughout calculation][Data mining]
• https://arxiv.org/abs/2310.02597[TurboGenius][Python suite][High-throughput calculation][Ab initio quantum Monte Carlo method]
• https://arxiv.org/abs/2310.12434[Detailed][High-throughput measurement][Composition dependence][Magnetoresistance][Spin-transfer torque][Composition-gradient film][Application][Co_xFe_1¡Ýx (0 x 1) system]
• https://arxiv.org/abs/2311.03163[SurfFlow][High-throughput surface energy calculation][Arbitrary crystal]
• https://arxiv.org/abs/1805.09199[High-throughput study][Static dielectric constant][High temperatures][Oxide and fluoride cubic perovskites]
• https://arxiv.org/abs/2106.03312[High-throughput study][Antisolvent][Stability][Multicomponent metal halide perovskite][Robotics-based synthesis][Machine learning]
• https://arxiv.org/abs/2205.14907[High-throughput study][Anomalous Hall effect]
• https://arxiv.org/abs/2212.07845[Exploration][All-3d Heusler alloy][Permanent magnet][Ab initio][High-throughput study]
• https://arxiv.org/abs/2304.14367[TribChem][Software][First-principles][High-throughput study][Solid interface][Tribological properties]
• https://arxiv.org/abs/2310.16591[Intrinsic piezoelectric anisotropy][Tetragonal ABO₃ perovskite][High-throughput study]
• https://arxiv.org/abs/2409.04418[Charting new regions][Cobalt's chemical space][Maximally large magnetic anisotropy][Computational high-throughput study]
• https://arxiv.org/abs/2411.10790[Effect of Hubbard U correction][Electronic and magnetic properties][2D material][High-throughput study]
• https://arxiv.org/abs/2312.04146[High-throughput study][Phase constitution][Thin film system][Mg-Mn-Al-O][Li recovery from slags]
• https://arxiv.org/abs/2503.17124[High-throughput study][Kagome compound][AV3Sb5 family]
• https://arxiv.org/abs/2312.17715[High-throughput combinatorial approach][Synthesis][Lead-free relaxor ferroelectric system]
• https://arxiv.org/abs/2401.01147[Automated segmentation][Large image dataset][Artificial intelligence][Microstructure characterisation][Damage analysis][High-throughput modelling input]
• https://arxiv.org/abs/2404.02437[High-throughput calculation][Antiferromagnets hosting anomalous transport phenomena][Phys. Rev. B 109, 094435 (2024)]
• https://arxiv.org/abs/2405.02519[Computationally efficient method][Calculating electron-phonon coupling][High-throughput superconductivity search]
• https://arxiv.org/abs/2406.15630[High-throughput][Data-driven computational framework][Novel quantum material]
• https://arxiv.org/abs/2407.04413[High-throughput magnetic co-doping][Design][Exchange interaction][Topological insulator]
• https://arxiv.org/abs/2407.09228[High-throughput identification][Statistical analysis][Atomically thin semiconductor]
• https://arxiv.org/abs/2408.05594[Emission spectrum][Doped graphene quantum dot][High-throughput TDDFT analysis]
• https://arxiv.org/abs/2408.16851[New magnetic topological material][High-throughput search]
• https://arxiv.org/abs/2410.13982[Design of amine-functionalized materials][Direct air capture][Integrated high-throughput calculation][Machine learning]
• https://arxiv.org/abs/2501.02503[Materials discovery][Combinatorial and high-throughput synthesis][Processing][New frontier for SPM]
• https://arxiv.org/abs/2501.04274[High-throughput studies][Novel magnetic material][Boride]
• https://arxiv.org/abs/2502.13159[Accelerated fatigue strength prediction][Additive manufactured functionally graded material][High-throughput plasticity quantification]
• https://arxiv.org/abs/2205.05053[High throughput generative vector autoregression model][Stochastic synapses]
• https://arxiv.org/abs/2503.16278[Uni-3DAR][Unified 3D generation][Understanding][Autoregression][Compressed spatial token]
• https://arxiv.org/abs/2203.11794[Strategic high throughput search][Identifying stable Li based half Heusler alloy][Spintronics application]
• https://arxiv.org/abs/2203.08697[New generalized informatics framework][Development][Large scale virtual battery material database]
• https://arxiv.org/abs/2311.13944[Investigating microstructure-property relationship][Nonwoven][Model-based virtual materials testing]
• https://arxiv.org/abs/2203.06821[SpectroLab][Open source matlab based toolbox][High throughput spectroscopy analysis]
• https://arxiv.org/abs/2201.09977[High throughput inverse design][Bayesian optimization][Functionalities][Spin splitting][Two-dimensional compound]
• https://arxiv.org/abs/2111.00366[High throughput study][Compositionally graded][Homogeneous][Fe-Pt thin film]
• https://arxiv.org/abs/2107.06128[Pathway][High throughput QMC][Alloy][2D]
• https://arxiv.org/abs/2105.05096[Exploring][Correlation][Solvent diffusion][Creep resistance][Mg-Ga HCP Alloy][High throughput liquid-solid diffusion couple][Materials and Design 197, 109243 (2021)]
• https://arxiv.org/abs/2104.09817[High throughput optimization][Hard and tough TiN/Ni nanocomposite coating][Reactive magnetron sputter deposition]
• https://arxiv.org/abs/2208.01692[Cloud platform][Automating][Sharing analysis][Raw simulation data][High throughput polymer molecular dynamics simulation]
• https://arxiv.org/abs/2209.13911[Two-dimensional ferroelectrics][High throughput computational screening]
• https://arxiv.org/abs/2210.00152[pyGWBS][High throughput workflow package][GW-BSE calculation]
• https://arxiv.org/abs/2212.02110[Unveiling][Complex structure-property correlation][Defect][2D material][High throughput dataset]
• https://arxiv.org/abs/2302.07988[High throughput discovery][Lightweight corrosion-resistant compositionally complex alloys]
• https://arxiv.org/abs/2302.09804[High throughput aqueous passivation testing methodology][Compositionally complex alloy][Scanning droplet cell]
• https://arxiv.org/abs/2305.11354[Pyrovskite][Software package][High throughput construction][Analysis][Featurization][Two- and three-dimensional perovskite systems]
• https://arxiv.org/abs/2306.08695[GHP-MOFassemble][Diffusion modeling][High throughput screening][Molecular dynamics][Rational discovery][Novel metal-organic framework][Carbon capture]
• https://arxiv.org/abs/2310.10605[ForceGen][End-to-end de novo protein generation][Nonlinear mechanical unfolding response][Protein language diffusion model]
• https://arxiv.org/abs/2309.06712[High throughput sampling][Phase space][Deep learning potential][delta-AlOOH][Geophysical condition]
• http://arXiv.org/abs/1506.02841[Computational 2D materials database]
• https://arxiv.org/abs/1806.03173[Computational 2D materials database][High-throughput modeling][Discovery][Atomically thin crystal]
• https://arxiv.org/abs/2309.11945[Magnetic order][Computational 2D materials database (C2DB)][High throughput spin spiral calculation]
• https://arxiv.org/abs/2401.05992[Two-dimensional altermagnet][High throughput computational screening][Symmetry requirement][Chiral magnon][Spin-orbit effect]
• https://arxiv.org/abs/2405.15226[Procedural construction][Atomistic polyurethane block copolymer model][High throughput simulation]
• https://arxiv.org/abs/2403.18987[Reconfigurable multiplex setup][High throughput electrical characterisation][Cryogenic temperature]
• https://arxiv.org/abs/2407.08957[High throughput screening][Crystal structure prediction][Carrier mobility calculation][Organic molecular semiconductor][Hole transport layer material][Perovskite solar cell]
• https://arxiv.org/abs/2407.09850[Enhancement][Piezoelectric properties][Narrow cerium doping range][Ba_1¡ÝxCa_xTi_1¡ÝyZr_yO₃][High throughput experiment]
• https://arxiv.org/abs/2407.19614[Multi-GPU RI-HF energies][Analytic gradient][High throughput ab initio molecular dynamics]
• https://arxiv.org/abs/2501.01607[Unveiling potential candidates][Rare-earth-free permanent magnet][Magnetocaloric effect application][High throughput screening][Fe-N alloy]
• https://arxiv.org/abs/2503.10535[Automation][High throughput modeling][Adsorption][Porous zeolytic imidazolate framework]
• https://arxiv.org/abs/1811.06807[Ab initio search][Polymer crystal][High thermal conductivity]
• https://arxiv.org/abs/1811.06744[Ab initio thermodynamic property][Certain compound][Nd-Fe-B system]
• https://arxiv.org/abs/1902.10501[Atomistic structure learning]
• https://arxiv.org/abs/2302.00100[Physics-informed][Reduced-order learning][First principles][Simulation][Quantum nanostructure]
• https://arxiv.org/abs/2109.11897[Adaptive clustering-based][Reduced-order modeling framework][Fast and accurate modeling][Localized history-dependent phenomena]
• https://arxiv.org/abs/2206.09002[Cluster generation][Deep energy-based model]
• https://arxiv.org/abs/2502.04406[Calibrated][Physics-informed uncertainty quantification]
• https://arxiv.org/abs/2411.03671[Energy-based physics-informed neural network][Frictionless contact problem][Large deformation]
• https://arxiv.org/abs/2412.09009[Physics-informed transformer neural operator][Learning generalized solution][Initial boundary value problem]
• https://arxiv.org/abs/2302.09184[Rapid design][Top-performing metal-organic framework][Qualitative representation][Building block]
• https://arxiv.org/abs/2312.09991[Extrapolation][Polaron properties][Low phonon frequencies][Bayesian machine learning]
• https://arxiv.org/abs/2407.20848[BMach][Bayesian machine][Optimizing Hubbard U parameter][DFT+U][Machine learning]
• https://arxiv.org/abs/2005.08418[Hardware implementation][Bayesian network building block][Stochastic spintronic device]
• https://arxiv.org/abs/1803.05273[Self-optimized construction][Transition rate matrices][Accelerated atomistic simulation][Bayesian uncertainty quantification]
• https://arxiv.org/abs/2502.03727[Microstructure-aware][Bayesian materials design]
• https://arxiv.org/abs/1807.06868[Multi-objective Bayesian Materials Discovery][Application][Discovery][Precipitation][Strengthened NiTi][Shape memory alloy][Micromechanical modeling]
• https://arxiv.org/abs/1808.02817[Stochastic unfolding][Nanoconfined DNA][Experiment][Model][Bayesian analysis]
• https://arxiv.org/abs/2502.21165[Bayesian selection][Efficient MLIP dataset selection]
• https://arxiv.org/abs/1809.07365[Bayesian strategy][Uncertainty quantification][Thermodynamic property]
• https://arxiv.org/abs/1810.07616[Bayesian determination][Deep eutectic solvent][Lipid monolayer]
• https://arxiv.org/abs/1812.01205[Bayesian Hamiltonian selection][X-ray photoelectron spectroscopy]
• https://arxiv.org/abs/1905.01802[Bayesian framework][Estimation][Single crystal elastic parameter][Spherical indentation stress-strain measurement]
• https://arxiv.org/abs/1906.03396[Bayesian parametric analytic continuation][Green's function]
• https://arxiv.org/abs/1912.07636[Scalable Bayesian Hamiltonian learning]
• https://arxiv.org/abs/2308.11400[Hamiltonian learning][Real-space impurity tomography][Topological moire superconductor]
• https://arxiv.org/abs/2412.07666[Hamiltonian learning quantum magnet][Non-local impurity tomography]
• https://arxiv.org/abs/2002.04977[Critical temperature prediction][Superconductor][Variational Bayesian neural network approach]
• https://arxiv.org/abs/2311.02495[Uncertainty quantification][Multivariable regression][Material property prediction][Bayesian neural network]
• https://arxiv.org/abs/2406.14838[Bayesian neural network][Predicting uncertainty][Full-field material response]
• https://arxiv.org/abs/2501.00952[Active and transfer learning][Partially Bayesian neural network][Materials and chemicals]
• https://arxiv.org/abs/1811.00421[Bayesim][Tool for adaptive grid model fitting][Bayesian inference]
• https://arxiv.org/abs/1708.09274[Efficient Bayesian inference][Atomistic structure][Complex functional materials]
• https://arxiv.org/abs/2002.08391[Bayesian inference][Band excitation][Scanning probe microscopy][Optimal dynamic model selection][Imaging]
• https://arxiv.org/abs/2003.02151[Versatile atomic magnetometry assisted][Bayesian inference]
• https://arxiv.org/abs/2008.03595[Bayesian inference framework][Compression][Prediction][Quantum state]
• https://arxiv.org/abs/2012.04765[Bayesian inference][Polycrystalline material]
• https://arxiv.org/abs/2104.05302[Integrating Bayesian inference][Scanning probe experiment][Robust identification][Surface adsorbate configuration]
• https://arxiv.org/abs/2104.08114[AI-driven Bayesian inference][Statistical microstructure descriptor][Finite-frequency wave]
• https://arxiv.org/abs/2402.05799[Recent breakthrough][AI-driven Materials Science][Tech giant][Introduce groundbreaking model]
• https://arxiv.org/abs/2410.10885[Adaptive AI-driven material synthesis][Towards autonomous 2D materials growth]
• https://arxiv.org/abs/2412.11032[AI-driven accelerated discovery][Intercalation-type cathode material][Magnesium batteries]
• https://arxiv.org/abs/2502.02905[AI-driven materials design][Mini-review]
• https://arxiv.org/abs/2502.13899[AI-driven discovery][High performance polymer electrode][Next-generation batteries]
• https://arxiv.org/abs/2503.14679[AI-driven][Uncertainty quantification][Multi-physics approach][Evaluate cladding materials][Microreactor]
• https://arxiv.org/abs/2201.04865[Bayesian inference][Microstructural][Hyperelastic model][Tendon deformation]
• https://arxiv.org/abs/2202.12881[Bayesian inference][Fiber orientation][Polymer][Short fiber-reinforced polymer composite]
• https://arxiv.org/abs/2205.09188[Bayesian inference][Hamiltonian selection][Mössbauer spectroscopy][J. Phys. Soc. Jpn. 91, 104002 (2022)]
• https://arxiv.org/abs/2305.05170[Efficient NMR measurement][Data analysis][Bayesian inference][Heavy fermion compound YbCo₂Zn₂₀]
• https://arxiv.org/abs/2309.14785[Bayesian inference][Identify crystalline structure][XRD]
• https://arxiv.org/abs/2309.01271[Bayesian inference][Composition-dependent phase diagram]
• https://arxiv.org/abs/2406.08981[Bayesian inference][General noise model parameter][Surface code's syndrome statistics]
• https://arxiv.org/abs/2410.16420[BI-EqNO][Generalized approximate Bayesian inference][Equivariant neural operator framework]
• https://arxiv.org/abs/2003.13393[Uncertainty quantification][First principles computational phase diagram][Prediction][Li-Si system][Bayesian sampling]
• https://arxiv.org/abs/2004.09814[Exploring physics][Ferroelectric domain wall][Bayesian analysis][Atomically resolved STEM data]
• https://arxiv.org/abs/2004.11526[Bayesian][Non-parametric Bragg-edge fitting][Neutron transmission strain imaging]
• https://arxiv.org/abs/2401.05848[Pushing][Pareto][Front of band gap][Permittivity][ML-guided search][Dielectric material]
• https://arxiv.org/abs/2005.09325[Benefit][Bayesian analysis][Characterization][Magnetic nanoparticle]
• https://arxiv.org/abs/2006.06125[Bayesian modelling][Pattern formation][One snapshot]
• https://arxiv.org/abs/1904.02042[On-the-fly Bayesian active learning][Interpretable force-field][Atomistic rare event]
• https://arxiv.org/abs/2006.06141[On-the-fly closed-loop autonomous materials discovery][Bayesian active learning]
• https://arxiv.org/abs/2108.08918[On-the-fly autonomous control of neutron diffraction][Physics-informed Bayesian active learning]
• https://arxiv.org/abs/2307.12452[Characterizing non-Markovian quantum processes][Fast Bayesian tomography]
• https://arxiv.org/abs/2008.11796[Fast Bayesian force field][Active learning][Inter-dimensional transformation][Stanene]
• https://arxiv.org/abs/2106.01949[Active learning][Reactive Bayesian force field][Application][Heterogeneous hydrogen-platinum catalysis dynamics]
• https://arxiv.org/abs/2308.07311[Stability][Mechanism][Kinetics][Emergence][Au surface reconstruction][Bayesian force field]
• https://arxiv.org/abs/2401.04359[Unbiased atomistic prediction][Crystal dislocation dynamics][Bayesian force field]
• https://arxiv.org/abs/2010.13241[Bayesian surrogate constitutive model][Estimate][Failure probability][Rubber-like material]
• https://arxiv.org/abs/2011.06086[Bayesian committee approach][Computational physics problem]
• https://arxiv.org/abs/2012.10694[Bayesian unsupervised learning][Hidden structure][Concentrated electrolyte]
• https://arxiv.org/abs/2012.12463[Bayesian learning][Adatom interaction][Atomically-resolved imaging data]
• https://arxiv.org/abs/2102.10976[Highly efficient][Accurate][Ab initio Bayesian active learning method][Accelerating discovery][Advanced functional material]
• https://arxiv.org/abs/2104.09479[Bayesian learning][Thermodynamic integration][Numerical convergence][Accurate phase diagram]
• https://arxiv.org/abs/2103.09777[Robust recognition][Exploratory analysis][Crystal structure][Bayesian deep learning]
• https://arxiv.org/abs/2105.05562[Procedure][3D atomic resolution reconstruction][Atom-counting][Bayesian genetic algorithm]
• https://arxiv.org/abs/2109.07617[Exploring DFT+U parameter space][Bayesian calibration][Markov chain Monte Carlo sampling]
• https://arxiv.org/abs/2110.02046[Beating][Thermal limit][Qubit initialization][Bayesian `Maxwell's demon']
• https://arxiv.org/abs/2112.10851[Bayesian][Frequentist][Information geometry approache][Parametric uncertainty quantification][Classical empirical interatomic potential]
• https://arxiv.org/abs/2202.12218[Robust spin relaxometry][Fast adaptive Bayesian estimation]
• https://arxiv.org/abs/2404.11162[AI-equipped scanning probe microscopy][Autonomous site-specific atomic-level characterization][Room temperature]
• https://arxiv.org/abs/2203.09895[Bayesian spectral deconvolution][X-ray absorption near edge structure][Discriminating][High- and low-energy domain][J. Phys. Soc. Jpn. 91, 074009 (2022)]
• https://arxiv.org/abs/2205.03611[Bayesian estimation][Correlation function]
• https://arxiv.org/abs/2205.15458[Bayesian active learning][Scanning probe microscopy][Gaussian process][Hypothesis learning]
• https://arxiv.org/abs/2207.10406[Advice][Describing Bayesian analysis][Neutron and X-ray reflectometry]
• https://arxiv.org/abs/2208.02374[Bayesian calibration][Interatomic potential][Binary alloy]
• https://arxiv.org/abs/2412.10405[Elucidating microstructural influence][Fatigue behavior][Additively manufactured Hastelloy X][Bayesian-calibrated crystal plasticity model]
• https://arxiv.org/abs/2210.03077[Bayesian autotuning][Hubbard model][Quantum simulator]
• https://arxiv.org/abs/2211.01330[Bayesian nonlocal operator regression (BNOR)][Data-driven learning framework][Nonlocal model][Uncertainty quantification]
• https://arxiv.org/abs/2109.02255[Data-driven learning][3-Point correlation function][Microstructure representation]
• https://arxiv.org/abs/2310.18582[Data-driven learning][Generalized Langevin equation][State-dependent memory][Phys. Rev. Lett. 133, 077301 (2024)]
• https://arxiv.org/abs/2211.03103[Highest melting point material][Searched][Bayesian global optimization][Deep potential molecular dynamics]
• https://arxiv.org/abs/2411.14508[Multi-objective Bayesian optimisation][Spinodoid cellular structure][Crush energy absorption]
• http://arxiv.org/abs/1609.04972[Designing nanostructure][Interfacial phonon transport][Bayesian optimization]
• https://arxiv.org/abs/2003.09036[Computational design][Stable and highly ion-conductive material][Multi-objective Bayesian optimization][Diffusion of oxygen and lithium]
• https://arxiv.org/abs/2006.15006[Efficient cysteine conformer search][Bayesian optimization]
• https://arxiv.org/abs/2007.08757[Ply-drop design][Non-conventional composite][Bayesian optimization]
• https://arxiv.org/abs/2208.13771[Fast Bayesian optimization][Needle-in-a-Haystack problem][Zooming memory-based initialization]
• https://arxiv.org/abs/1903.09385[Bayesian optimization][Chemical composition][Comprehensive framework][RFe₁₂-type magnet compound]
• https://arxiv.org/abs/1907.02577[Data-centric mixed-variable Bayesian optimization][Materials Design]
• https://arxiv.org/abs/1910.01688[Bayesian optimization][Materials Design][Mixed quantitative and qualitative variables]
• https://arxiv.org/abs/2001.09312[Prediction of perovskite-related structure][ACuO_3¡Ýx (A = Ca, Sr, Ba, Sc, Y, La)][DFT][Bayesian optimization]
• https://arxiv.org/abs/2002.05598[Detecting stable adsorbate][(1S)-camphor on Cu(111)][Bayesian optimization]
• https://arxiv.org/abs/2110.07900[Bayesian optimization package][PHYSBO]
• https://arxiv.org/abs/2110.12642[Exploration][New reconstructed structure][GaN(0001) surface][Bayesian optimization]
• https://arxiv.org/abs/2202.04242[Finding][Stable structure][2D hexagonal material][Bayesian optimization][Beyond the structural relationship][3D crystal][Weakly-bonded binary system]
• https://arxiv.org/abs/2203.07892[Autonomous atomic Hamiltonian construction][Active sampling][x-ray absorption spectroscopy][Adversarial Bayesian optimization]
• https://arxiv.org/abs/2407.04457[Kalman filter][Adversarial Bayesian optimization][Active sampling][Inelastic neutron scattering]
• https://arxiv.org/abs/2011.10968[Crystal structure prediction][Combining graph network][Bayesian optimization]
• https://arxiv.org/abs/2106.01309[Benchmarking][Performance of Bayesian optimization][Multiple experimental materials science domain]
• https://arxiv.org/abs/2106.08212[Bayesian optimization][High-entropy alloy composition][Electrocatalytic oxygen reduction]
• https://arxiv.org/abs/2107.02685[Bayesian optimised collection strategy][Fatigue testing][Constant life testing]
• https://arxiv.org/abs/2107.14005[Commun Phys 4, 170 (2021)][Unveiling quasiparticle dynamics][Topological insulator][Bayesian modelling]
• https://arxiv.org/abs/2108.00002[Bayesian optimization][Materials Science][Survey]
• https://arxiv.org/abs/2108.12889[Multi-objective Bayesian optimization][Ferroelectric material][Interfacial control][Memory and energy storage]
• https://arxiv.org/abs/2203.12597[High-dimensional Bayesian optimization][Hyperparameter][Attention-based network][Predict Materials Property][Case study][CrabNet using Ax and SAASBO]
• https://arxiv.org/abs/2203.17241[Bayesian optimization][Known experimental and design constraints][Chemistry application]
• https://arxiv.org/abs/2204.05452[Bayesian optimization][Experimental failure][High-throughput materials growth]
• https://arxiv.org/abs/2204.11815[Automatic parameter selection][Electron ptychography][Bayesian optimization]
• https://arxiv.org/abs/2206.12435[Bayesian optimization][Continuous space][Virtual process embedding]
• https://arxiv.org/abs/2208.11919[Pareto front analysis][Multi-objective Bayesian optimization][(R, Z)(Fe,Co,Ti)₁₂ (R = Y, Nd, Sm; Z = Zr, Dy)][J. Phys. Soc. Jpn. 92, 014801 (2023)]
• https://arxiv.org/abs/2210.02242[Bayesian optimization][Discrete dislocation plasticity][Two-dimensional precipitation hardened crystal]
• https://arxiv.org/abs/2302.00710[Exploring energy-composition relationship][Bayesian optimization][Accelerated discovery][Inorganic material]
• https://arxiv.org/abs/2303.00929[Stoichiometric growth][SrTiO₃ film][Bayesian optimization][Adaptive prior mean]
• https://arxiv.org/abs/2303.08426[Structural disorder][Octahedral tilting][Inorganic halide perovskite][New insight][Bayesian optimization]
• https://arxiv.org/abs/2303.13754[Bayesian optimization][Metastable nickel formation][Spontaneous crystallization][Extreme condition]
• https://arxiv.org/abs/2309.04168[Bayesian optimization][Active learning][Ta-Nb-Hf-Zr-Ti system][Spin transport properties]
• https://arxiv.org/abs/2309.12464[Bayesian optimisation approach][Quantify][Input parameter uncertainty][Prediction][Numerical physics simulation]
• https://arxiv.org/abs/2310.17765[Autonomous convergence][STM control parameter][Bayesian optimization]
• https://arxiv.org/abs/2402.02198[Multimodal Co-orchestration][Exploring structure-property relationship][Combinatorial libraries][Multi-task Bayesian optimization]
• https://arxiv.org/abs/2412.18067[Automated materials discovery platform][Realized][Scanning probe microscopy][Combinatorial libraries]
• https://arxiv.org/abs/2402.11101[Physics-based material parameters extraction][Perovskite experiment][Bayesian optimization]
• https://arxiv.org/abs/2311.09591[Accelerating material discovery][Threshold-driven hybrid acquisition][Policy-based Bayesian optimization]
• https://arxiv.org/abs/2404.18234[Bayesian optimization][State engineering][Quantum gases]
• https://arxiv.org/abs/2405.03092[Bayesian optimization][Stable properties amid processing fluctuations][Sputter deposition][J. Vac. Sci. Technol. A 42, 033408 (2024)]
• https://arxiv.org/abs/2405.08900[Interoperable multi objective batch Bayesian optimization framework][High throughput materials discovery]
• https://arxiv.org/abs/2405.16230[Active oversight][Quality control][Standard Bayesian optimization][Autonomous experiment]
• https://arxiv.org/abs/2406.14627[Bayesian optimization priors][Efficient variational quantum algorithm]
• https://arxiv.org/abs/2407.07963[Efficient quantum computation][Molecular ground state energies][Bayesian optimization][Priors over surface topology]
• https://arxiv.org/abs/2408.15590[Bayesian optimization][Atomic structure][Prior probabilities][Universal interatomic potential]
• https://arxiv.org/abs/2503.14118[PET-MAD][Universal interatomic potential][Advanced materials modeling]
• https://arxiv.org/abs/2409.07190[Applying multi-fidelity Bayesian optimization][Chemistry][Open challenge][Major consideration]
• https://arxiv.org/abs/2409.09192[Automated design][Nonreciprocal thermal emitter][Bayesian optimization]
• https://arxiv.org/abs/2410.02717[Measurement][Noise][Bayesian optimization][Co-optimizing noise][Property discovery][Automated experiment]
• https://arxiv.org/abs/2410.04314[Hierarchical Gaussian process-based Bayesian optimization][Materials discovery][High entropy alloy space]
• https://arxiv.org/abs/2410.14873[BEACON][Automated aberration correction][Scanning transmission electron microscopy][Bayesian optimization]
• https://arxiv.org/abs/2411.04758[Exploration][Stable atomic configuration][Graphene-like BCN system][Bayesian optimization]
• https://arxiv.org/abs/2411.06891[Autonomous robotic mechanical exfoliation][Two-dimensional semiconductor][Combined with Bayesian optimization]
• https://arxiv.org/abs/2412.16433[Exploring noncollinear magnetic energy landscape][Bayesian optimization]
• https://arxiv.org/abs/2501.02707[Refining coarse-grained molecular topologies][Bayesian optimization approach]
• https://arxiv.org/abs/2502.08292[Dendrite suppression][Fast-charging high-energy metal-ion batteries][Bayesian optimization approach]
• https://arxiv.org/abs/2502.06815[Honegumi][Interface][Accelerating][Adoption][Bayesian optimization][Experimental science]
• https://arxiv.org/abs/2502.16241[Accelerating combinatorial electrocatalyst discovery][Bayesian optimization][Case study][Quaternary system Ni-Pd-Pt-Ru][Oxygen evolution reaction]
• https://arxiv.org/abs/2304.03573[Bayesian][Seebeck coefficient][Liquid water][Equilibrium molecular dynamics]
• https://arxiv.org/abs/2305.08574[Bayesian learning design][Characterizes porous metamaterial][Biofilm transport and control]
• https://arxiv.org/abs/2306.03575[Quantifying physical insight][Cooperatively][Exhaustive search][Bayesian spectroscopy][X-ray photoelectron spectra]
• https://arxiv.org/abs/2306.10406[Human-in-the-loop][Bayesian autonomous materials phase mapping]
• https://arxiv.org/abs/2308.07669[Bayesian modelling approaches][Quantum state][Ultimate Gaussian process state][Handbook]
• https://arxiv.org/abs/2309.10812[Bond breaking kinetics][Mechanically controlled break junction experiment][Bayesian approach]
• https://arxiv.org/abs/2406.05133[Hierarchical Bayesian approach][Adaptive integration][Bragg peak][Time-of-flight neutron scattering data]
• https://arxiv.org/abs/2408.03110[Charge state estimation][Quantum dot][Bayesian approach]
• https://arxiv.org/abs/2501.02934[Bayesian approach][Discovering time- delayed differential equation][Data]
• https://arxiv.org/abs/2309.14302[Bayesian parameter estimation][Characterising mobile ion vacancies][Perovskite solar cell]
• https://arxiv.org/abs/2502.07473[Multiscale Bayesian approach][Quantification and denoising][Energy-dispersive x-ray data]
• https://arxiv.org/abs/2309.15014[Efficient adaptive Bayesian estimation][Slowly fluctuating Overhauser field gradient]
• https://arxiv.org/abs/2311.00763[Quantifying intuition][Bayesian approach][Figures of merit][EXAFS analysis][Magic size cluster]
• https://arxiv.org/abs/2311.06228[Learning material synthesis-structure-property relationship][Data fusion][Bayesian][Co-regionalization][N-Dimensional piecewise function learning]
• https://arxiv.org/abs/2312.16078[Targeted materials discovery][Bayesian algorithm execution]
• https://arxiv.org/abs/2401.00018[Combining Bayesian reconstruction entropy][Maximum entropy method][Analytic continuation][Matrix-valued Green's function]
• https://arxiv.org/abs/2411.05496[Combining Bayesian optimization][SVD][Machine learning][Advanced optical design]
• https://arxiv.org/abs/2401.06106[Accelerated development][Multicomponent alloy][Discrete design space][Bayesian multi-objective optimisation]
• https://arxiv.org/abs/2401.10466[Quantitative selection][Sample structures][Small-angle scattering][Bayesian method]
• https://arxiv.org/abs/2402.04873[Seebeck coefficient][Ionic conductor][Bayesian regression analysis]
• https://arxiv.org/abs/2501.15872[Probing glass formation][Perylene derivatives][Atomic scale simulation][Bayesian regression]
• https://arxiv.org/abs/2402.06256[Bayesian committee machine potential][Isothermal-isobaric molecular dynamics simulation]
• https://arxiv.org/abs/2402.14497[Sparse Bayesian committee machine potential][Hydrocarbon]
• https://arxiv.org/abs/2402.17132[Bayesian committee machine potential][Organic nitrogen compound]
• https://arxiv.org/abs/2403.01158[Bayesian committee machine potential][Oxygen-containing organic compound]
• https://arxiv.org/abs/2403.04645[Accelerating][Convergence][Coupled cluster calculation][Homogeneous electron gas][Bayesian ridge regression]
• https://arxiv.org/abs/2403.09677[Rapid and robust construction][ML-ready peak feature table][X-ray diffraction data][Bayesian peak-top fitting]
• https://arxiv.org/abs/2403.18391[Bayesian electron density determination][Sparse and noisy single-molecule X-ray scattering images]
• https://arxiv.org/abs/2404.12899[Bayesian Co-navigation][Dynamic designing][Materials digital twins][Active learning]
• https://arxiv.org/abs/1910.10581[Using Bayesian model selection][Advise neutron reflectometry analysis][Langmuir-Blodgett monolayer]
• https://arxiv.org/abs/2206.11343[Bayesian model calibration][Block copolymer self-assembly][Likelihood-free inference][Expected information][Gain computation][Measure transport]
• https://arxiv.org/abs/2305.04841[AutoEIS][Automated Bayesian model selection][Analysis][Electrochemical impedance spectroscopy]
• https://arxiv.org/abs/2306.05398[Bayesian model calibration][Diblock copolymer][Film self-assembly][Power spectrum][Microscopy data]
• https://arxiv.org/abs/2404.13092[Scale-bridging][Complex model hierarchy][Investigation][Metal-fueled circular energy economy][Bayesian model calibration][Model error quantification]
• https://arxiv.org/abs/2406.15223[Thermodynamic modeling][LiCl-KCl-LaCl₃ system][Bayesian model selection][Uncertainty quantification]
• https://arxiv.org/abs/2503.17314[Numerical investigation][Preferential flow path][Enzymatically induced calcite precipitation][Bayesian model analysis]
• https://arxiv.org/abs/2409.15307[Adaptive Gaussian process method][Multi-modal Bayesian inverse problem]
• https://arxiv.org/abs/2408.09889[Field][Bayesian evidence inference][Nested sampling data]
• https://arxiv.org/abs/2409.10381[J-UBIK][JAX-accelerated universal Bayesian imaging kit]
• https://arxiv.org/abs/2410.23895[Bayesian state estimation][Unlocks real-time control][Thin film synthesis]
• https://arxiv.org/abs/2411.05529[Bayesian exploration][Composition space][CuZrAl metallic glasses][Mechanical properties]
• https://arxiv.org/abs/2501.09308[Rapid][Comprehensive search][Crystalline phase][X-ray diffraction][Seconds via GPU-Accelerated Bayesian variational inference]
• https://arxiv.org/abs/2502.03146[Symmetry-aware Bayesian flow network][Crystal generation]
• https://arxiv.org/abs/2405.01243[Multimodal reconstruction][TbCo thin film structure][Basyeian analysis][Polarised neutron reflectivity]
• https://arxiv.org/abs/2201.03156[m^*][Two-dimensional electron gas][Neural canonical transformation study]
• https://arxiv.org/abs/2402.11103[Toward Learning][Latent-variable representation][Microstructure][Optimizing][Spatial statistics space]
• https://arxiv.org/abs/2503.01709[Can machines learn density functionals?][Past][Present][Future][ML in DFT]
• https://arxiv.org/abs/1907.08473[Static local field correction][Warm dense electron gas][Ab initio path integral Monte Carlo study][Machine learning representation]
• https://arxiv.org/abs/2403.19039[Expanding density-correlation machine learning representation][Anisotropic coarse-grained particle]
• http://arXiv.org/abs/1512.09110[Machine learning method][Interatomic potential][Application to boron carbide]
• http://arxiv.org/abs/1512.03502[Prediction][Grain boundary structure and energy][Machine learning]
• http://arxiv.org/abs/1509.00973[Prediction model of band-gap][AX binary compounds][Combination of density functional theory calculations and machine learning technique]
• http://arXiv.org/abs/1506.08858[Machine learning][Dynamical mean-field theory]
• http://arXiv.org/abs/1508.05315[Machine Learning Energies][2 M Elpasolite (ABC₂D₆) Crystals]
• https://arxiv.org/abs/1812.02306[Efficient construction method][Phase diagram][Uncertainty sampling]
• http://arXiv.org/abs/1605.01735[Machine learning phases of matter]
• http://arxiv.org/abs/1607.06789[Machine learning][Identify factors][Govern amorphization][Irradiated pyrochlore]
• http://arxiv.org/abs/1608.07374[Machine learning][Atomic forces in a Crystalline Solid][Transferability to Various Temperatures]
• http://arxiv.org/abs/1609.02552[Machine learning phases][Strongly correlated Fermions]
• http://arxiv.org/abs/1609.03705[Pure density functional][Strong correlations][Thermodynamic limit][Machine learning]
• https://arxiv.org/abs/1611.08645[Representation of crystalline compounds][Machine-learning prediction]
• https://arxiv.org/abs/2412.16294[Machine-learnt potential highlight][Melting and freezing][Aluminium nanoparticle]
• https://arxiv.org/abs/1704.06439[Unified Representation][Machine learning][Molecule][Crystal]
• https://arxiv.org/abs/1705.00565[Machine learning meets quantum state preparation][Phase diagram][Quantum control]
• https://arxiv.org/abs/1808.10869[Phase diagram][All inorganic materials]
• https://arxiv.org/abs/1705.08798[Robust FCC solute diffusion predictions][Ab-initio machine learning]
• https://arxiv.org/abs/1706.00192[Materials screening][Discovery of new half-Heuslers][Machine learning versus Ab initio methods]
• https://arxiv.org/abs/1706.00179[Machine learning][Unifies the modelling of materials and molecules]
• https://arxiv.org/abs/1706.02012[What can a machine learn from a single first-principles calculation?]
• https://arxiv.org/abs/1706.06293[Efficient and accurate machine-learning][Interpolation of atomic energies][Composition][Many Species]
• https://arxiv.org/abs/1810.12534[Computational method][Creation materials][Required composition][Structure]
• https://arxiv.org/abs/2309.06930[Modeling dislocation dynamics data][Semantic web technologies]
• https://arxiv.org/abs/2309.14450[Learning dislocation dynamics][Mobility law][Large-scale MD simulation]
• https://arxiv.org/abs/1904.05690[Materials discovery][Stable][Nontoxic][Halide perovskite][High-efficiency solar cell]
• https://arxiv.org/abs/1902.06573[Expanding the horizon][Automated metamaterials discovery][Quantum annealing]
• https://arxiv.org/abs/2309.13673[Alloy informatics][Ab initio charge density profile][Case study][Hydrogen effect][Face-centered cubic crystal]
• https://arxiv.org/abs/1806.09662[Materials informatics approach][Identification][One-band correlated material][Analogous to the cuprates]
• https://arxiv.org/abs/1901.04133[Materials discovery][Properties prediction][Thermal transport][Materials informatics][Mini-review]
• https://arxiv.org/abs/2109.04007[MaterialsAtlas.org][Materials informatics web app platform][Materials discovery][Survey][State-of-the-art]
• https://arxiv.org/abs/1908.00746[Materials informatics][Evolutionary algorithms][Application to search for superconducting hydrogen compound]
• https://arxiv.org/abs/2001.02876[Synergy of binary substitutions][Improving the cycle performance][LiNiO₂][Revealed by ab initio materials informatics]
• https://arxiv.org/abs/2004.00925[Revisiting Tc-structure trend][Cuprate superconductor][Materials informatics]
• https://arxiv.org/abs/2205.00829[Function decomposition tree][Causality-first perspective][Systematic description][Materials informatics]
• https://arxiv.org/abs/2410.20951[Neural Hamilton][A.I.][Understand][Hamiltonian mechanics]
• https://arxiv.org/abs/2210.07683[AI-accelerated materials informatics method][Discovery][Ductile alloy]
• https://arxiv.org/abs/2311.04418[AI-accelerated discovery][Altermagnetic material]
• https://arxiv.org/abs/2409.08065[AI-accelerated discovery][High critical temperature superconductor]
• https://arxiv.org/abs/2310.13136[Approaches for uncertainty quantification][AI-predicted material properties][Comparison]
• https://arxiv.org/abs/2406.16224[From text to test][AI-generated control software][Materials science instrument]
• https://arxiv.org/abs/2210.07929[Object storage][Persistent memory][Data infrastructure][HPC materials informatics]
• https://arxiv.org/abs/1806.06040[Materials informatics][Dark matter detection]
• https://arxiv.org/abs/2305.03797[Materials Informatics][Algorithmic design rule]
• https://arxiv.org/abs/2308.16259[Materials informatics transformer][Language model][Interpretable materials properties prediction]
• https://arxiv.org/abs/2407.04648[Efficient materials informatics][Rockets and Electrons]
• https://arxiv.org/abs/2311.05133[Materials properties prediction][MAPP][Empowering][Solely][Chemical formulas]
• https://arxiv.org/abs/2502.12698[Machine learning exploration][Topological polarization pattern][Hexagonal boron nitride moire superlattice]
• https://arxiv.org/abs/1706.09122[Accurate force field][Molybdenum][Machine learning][Large materials data]
• https://arxiv.org/abs/1707.04826[Machine learning application][Life time of materials]
• https://arxiv.org/abs/1707.07294[Machine learning][Materials informatics][Recent applications and prospects]
• https://arxiv.org/abs/1707.09699[Predicting phase behavior][Grain boundary][Evolutionary search][Machine learning]
• https://arxiv.org/abs/1708.02741[Conceptual and practical bases][High accuracy][Machine learning][Interatomic potential]
• https://arxiv.org/abs/1708.04766[Fundamental band gap][Alignment of two-dimensional semiconductors][Explored by machine learning]
• https://arxiv.org/abs/1708.06017[Resolving transition metal chemical space][Feature selection][Machine learning][Structure-property relationship]
• https://arxiv.org/abs/1708.08530[Machine learning][band gap][Kesterite][Quaternary compound][Photovoltaics application]
• https://arxiv.org/abs/1709.01666[Descriptors][Machine learning][Materials data]
• https://arxiv.org/abs/1709.04576[Catalyst design][Actively learned machine][Non-ab initio input features][CO2 reduction reaction]
• https://arxiv.org/abs/1709.05417[Leaving the valley][Charting the energy landscape][Metal/organic interface][Machine learning]
• https://arxiv.org/abs/1709.06757[Symmetry-adapted machine-learning][Tensorial properties][Atomistic system]
• https://arxiv.org/abs/1710.02605[Optimal mean radius][Volume fraction][Nanocrsytalline phase][Softmagnetic alloy][Machine learning][Calphad approach]
• https://arxiv.org/abs/1710.05677[Linearized machine-learning][Interatomic potential][Non-magnetic elemental metals][Limitation of pairwise descriptors][Trend of predictive power]
• https://arxiv.org/abs/1710.09861[Machine learning][Crystal identification and discovery]
• https://arxiv.org/abs/1712.06018[Predicting][Dissolution kinetics][Silicate glasses][Machine learning]
• https://arxiv.org/abs/1801.04900[Applying machine learning technique][Predict][Energetic material]
• https://arxiv.org/abs/2406.09620[Applying machine learning][Elucidate ultrafast demagnetization dynamics][Ni and Ni80Fe20]
• https://arxiv.org/abs/1802.05377[Novel superhard tungsten nitride][Predicted][Machine-learning accelerated crystal structure searching]
• https://arxiv.org/abs/1803.01416[Machine learning][Determination of atomic dynamics][Grain boundary]
• https://arxiv.org/abs/1803.01035[Machine learning][Entanglement Freedom][How I learned to stop worrying][Love linear regression]
• https://arxiv.org/abs/2102.11868[Machine learning regression][Operator dynamics]
• https://arxiv.org/abs/2008.03670[Lattice thermal conductivity prediction][Symbolic regression][Machine learning]
• https://arxiv.org/abs/2212.04450[GAUCHE][Library][Gaussian processes][Chemistry]
• Tota Nakamura, J. Phys. Soc. Jpn. 94, 031004 (2025)[Gaussian process regression][Improved evaluator][Physical quantities]
• https://arxiv.org/abs/2006.13426[Non-parametric local pseudopotential][Machine Learning][Tin pseudopotential built][Gaussian process regression]
• https://arxiv.org/abs/2106.08612[Efficient Gaussian process regression][Prediction][Molecular crystal][Harmonic free energy]
• https://arxiv.org/abs/2206.08744[Improved uncertainty quantification][Gaussian process regression][Interatomic potential]
• https://arxiv.org/abs/2307.01407[Enhancing ab initio diffusion calculation][Materials through Gaussian process regression]
• https://arxiv.org/abs/2311.16012[Acceleration][Solvation free energy calculation][Thermodynamic integration][Gaussian process regression][Improved Gelman-Rubin convergence diagnostics]
• https://arxiv.org/abs/2407.12525[Efficient ensemble uncertainty estimation][Gaussian processes regression]
• https://arxiv.org/abs/2312.11487[Symbolic learning][Material discovery]
• https://arxiv.org/abs/1901.04136[Symbolic regression][Materials science]
• https://arxiv.org/abs/2305.19551[Combining first-principles modeling][Symbolic regression][Designing efficient single-atom catalyst][Oxygen evolution reaction][Mo₂CO₂ MXene]
• https://arxiv.org/abs/2403.10320[Universal crack tip correction algorithm][Physical deep symbolic regression]
• https://arxiv.org/abs/2002.05076[Structure-property map][Kernel principal covariates regression]
• https://arxiv.org/abs/2105.13303[Calibrated bootstrap][Uncertainty quantification][Regression model]
• https://arxiv.org/abs/2107.00638[Symbolic regression][Work function][Metal/organic interfaces][When are descriptors physically meaningful?]
• https://arxiv.org/abs/2412.05947[Materials-discovery workflows][Guided by symbolic regression][Identifying acid-stable oxide][Electrocatalysis]
• https://arxiv.org/abs/2206.06422[Symbolic regression][Materials Science][Discovering interatomic potential][Data]
• https://arxiv.org/abs/2412.08143[1D kinetic energy density functionals][learned][Symbolic regression]
• https://arxiv.org/abs/2206.02040[MetaNOR][Meta-learnt nonlocal operator regression approach][Metamaterial modeling]
• https://arxiv.org/abs/2410.20331[Embedded nonlocal operator regression][ENOR][Quantifying model error][Learning nonlocal operator]
• https://arxiv.org/abs/2501.19179[Learning non-local molecular interaction][Equivariant local representation][Charge equilibration]
• https://arxiv.org/abs/2401.00744[Harmonizing covariance][Expressiveness][Deep Hamiltonian regression][Crystalline material research][Hybrid cascaded regression framework]
• https://arxiv.org/abs/1912.11381[Solving quantum statistical mechanics][Variational autoregressive network][Quantum Circuit]
• https://arxiv.org/abs/2009.05580[Probabilistic formulation][Open quantum many-body systems dynamics][Autoregressive model]
• https://arxiv.org/abs/2210.05871[Autoregressive neural Slater-Jastrow ansatz][Variational Monte Carlo simulation]
• https://arxiv.org/abs/2301.04277[Simulations of Disordered Matter][3D][Morphological autoregressive protocol (MAP)][Water]
• https://arxiv.org/abs/2306.05917[Impact][Conditional modelling][Universal autoregressive quantum state]
• https://arxiv.org/abs/2307.04340[Crystal structure generation][Autoregressive large language modeling]
• https://arxiv.org/abs/2310.04166[Autoregressive neural quantum state][Quantum number symmetries]
• https://arxiv.org/abs/2503.06687[UniGenX][Unified generation][Sequence and structure][Autoregressive diffusion]
• https://arxiv.org/list/cond-mat.mtrl-sci/recent[Intelligent identification][Two-dimensional Structure][Machine-Learning][Optical microscopy]
• https://arxiv.org/abs/1803.06042[Stability engineering][Halide perovskite][Machine learning]
• https://arxiv.org/abs/1803.07059[Autonomous scanning probe microscopy][in-situ][Tip conditioning][Machine Learning]
• https://arxiv.org/abs/1803.09827[Vibrational propertie][Metastable polymorph structure][Machine learning]
• https://arxiv.org/abs/1803.11246[Accelerating materials development][Automation][Machine learning][High-performance computing]
• https://arxiv.org/abs/1804.04651[Machine learning][Computational screening][Inorganic solid electrolytes][Dendrite suppression][Li Metal Anode]
• https://arxiv.org/abs/1804.05924[Applied machine learning][Predict stress hotspot][Hexagonal close packed materials]
• https://arxiv.org/abs/1804.08401[Hybrid piezoelectric-magnetic neurons][Energy-efficient][Machine learning]
• https://arxiv.org/abs/1805.01568[Machine learning][General purpose interatomic potential]
• https://arxiv.org/abs/1805.02303[Machine-learning guided discovery][High-performance][Spin-driven][Thermoelectric material]
• https://arxiv.org/abs/2409.18441[Machine-learning guided search][Phonon-mediated superconductivity][Boron and carbon compounds]
• https://arxiv.org/abs/1805.07325[Machine learning][Force-field inspired descriptor][Fast screening and mapping][Energy landscape]
• https://arxiv.org/abs/1805.10855[Kinetic energy density][Fourth order gradient expansion][Performance][Different classes][Improvement][Machine learning]
• https://arxiv.org/abs/1805.11541[Chemical shift][Molecular solid][Machine learning]
• https://arxiv.org/abs/1806.00829[Machine learning][Quantum phase transition]
• https://arxiv.org/abs/1806.03553[Quantitative trend][8 physical properties of 115000 inorganic compounds][Machine learning]
• https://arxiv.org/abs/1806.03841[Analytic continuation][domain-knowledge free][Machine learning]
• https://arxiv.org/abs/1807.06156[Machine learning][Energetic Material Properties]
• https://arxiv.org/abs/1807.09985[Artificial intelligent][Atomic Force Microscope][Machine learning]
• https://arxiv.org/abs/2005.14422[Single-atom alloy catalyst][Designed][Artificial intelligence]
• https://arxiv.org/abs/2012.01979[Artificial intelligence accelerator][Graphene optoelectronic device]
• https://arxiv.org/abs/2102.08269[Materials gene][Heterogeneous catalysis][Clean experiment][Artificial intelligence]
• https://arxiv.org/abs/2108.02837[Lossless multi-scale constitutive elastic relation][Artificial intelligence]
• https://arxiv.org/abs/2201.05655[Emissivity prediction][Functionalized surface][Artificial intelligence]
• https://arxiv.org/abs/2202.01916[Artificial intelligence][Powered material search engine]
• https://arxiv.org/abs/2204.12968[Accelerating materials-space exploration][Mapping materials properties][Artificial intelligence][Lattice thermal conductivity]
• https://arxiv.org/abs/2208.07612[Accelerating nanomaterials discovery][Artificial intelligence][HPC center]
• https://arxiv.org/abs/2209.09636[Artificial intelligence][Concrete material][Scientometric view]
• https://arxiv.org/abs/2209.11618[Artificial intelligence][Advanced material]
• https://arxiv.org/abs/2211.08179[Artificial intelligence approache][Materials-by-design][Energetic material][State-of-the-art][Challenge][Future direction]
• https://arxiv.org/abs/2303.08162[Artificial intelligence][Artificial material][Moire atom]
• https://arxiv.org/abs/2304.13927[NIMS-OS][Automation software][Implement][Closed loop][Artificial intelligence][Robotic experiment][Materials science]
• https://arxiv.org/abs/2312.02796[Materials expert-artificial intelligence][Materials discovery]
• https://arxiv.org/abs/2401.04070[Accelerating computational materials discovery][Artificial intelligence][Cloud high-performance computing][Large-scale screening][Experimental validation]
• https://arxiv.org/abs/2402.07000[Artificial intelligence-enabled optimization][Battery-grade lithium carbonate production]
• https://arxiv.org/abs/2406.19397[Scanning probe microscopy][Artificial intelligence][Quantum computing]
• https://arxiv.org/abs/2407.10022[AtomAgents][Alloy design and discovery][Physics-aware multi-modal multi-agent artificial intelligence]
• https://arxiv.org/abs/2407.17669[Atomic resolution observation][Nanoparticle surface dynamics][Instabilities][Artificial intelligence]
• https://arxiv.org/abs/2409.10304[Impactful research][Artificial intelligence][Chemistry and materials science]
• https://arxiv.org/abs/2410.03746[Resolution enhancement][Scanning electron micrograph][Artificial intelligence]
• https://arxiv.org/abs/2412.00498[Uni-electrolyte][Artificial intelligence platform][Designing electrolyte molecule][Rechargeable batteries]
• https://arxiv.org/abs/2501.15838[CrySPAI][New crystal structure prediction software][Artificial intelligence]
• https://arxiv.org/abs/2303.12702[Automatic identification][Crystal Structure][Interface][Artificial-intelligence-based electron microscopy]
• https://arxiv.org/abs/2305.02259[Thermally-driven multilevel non-volatile memory][Monolayer MoS₂][Neuro-inspired artificial learning]
• https://arxiv.org/abs/2205.02554[Mapping superconductivity][High-pressure hydride][Superhydra project]
• https://arxiv.org/abs/1807.10751[Committee machine][Vote][Similarity between materials]
• https://arxiv.org/abs/1808.00479[Machine learning][Scientific discovery][Electronic quantum matter visualization experiment]
• https://arxiv.org/abs/1808.01714[Machine learning][valence force field model]
• https://arxiv.org/abs/1808.04519[Machine-learning solver][Modified diffusion equations]
• https://arxiv.org/abs/1808.05292[Structural characterization][Machine learning][Grain boundary energy and mobility]
• https://arxiv.org/abs/1809.03960[Atomic positions independent descriptor][Machine learning][Material properties]
• https://arxiv.org/abs/2501.08919[Revealing local structure][Machine-learning-fused multimodal spectroscopy]
• https://arxiv.org/abs/1809.09203[General machine-learning][Surrogate model][Materials prediction]
• https://arxiv.org/abs/1809.09865[Phase diagram][Disordered higher order topological Insulator][Machine learning]
• https://arxiv.org/abs/1810.02323[Machine learning][Electron correlation][Disordered medium]
• https://arxiv.org/abs/1810.02858[Temperature effect][Phonon dispersion stability][Zirconium][Machine learning-driven][Atomistic simulation]
• https://arxiv.org/abs/2311.06010[Machine learning-driven structure prediction][Iron hydride]
• https://arxiv.org/abs/2406.05142[Machine learning-driven optimization][TPMS][Architected material][Simulated annealing]
• https://arxiv.org/abs/2407.04877[Leveraging data mining][Active learning][Domain adaptation][Multi-stage][Machine learning-driven approach][Efficient discovery][Advanced acidic oxygen evolution electrocatalyst]
• https://arxiv.org/abs/2501.01092[Machine learning-driven insight][Excitonic effect][2D material]
• https://arxiv.org/abs/2502.01813[Machine learning-driven analytical model][Threshold displacement energy prediction][Material]
• https://arxiv.org/abs/1810.03972[Machine learning clustering technique][Powder X-ray diffraction pattern][Distinguish alloy substitution]
• https://arxiv.org/abs/2005.11660[Feature space][XRD pattern][Constructed][Auto-encoder]
• https://arxiv.org/abs/1810.05586[Machine learning force][Trained by Gaussian process][Liquid state][Transferability][Temperature and pressure]
• https://arxiv.org/abs/1810.05538[Learning multiple order parameters][Interpretable machine]
• https://arxiv.org/abs/1903.02175[Materials development][Interpretable machine learning]
• https://arxiv.org/abs/2006.13552[Predicting][Thermally activated ¦Â event][Metallic glass][Interpretable machine learning]
• https://arxiv.org/abs/2010.00532[Persistent homology advance][Interpretable machine learning][Nanoporous material]
• https://arxiv.org/abs/2103.03633[Unveiling][Glass Veil][Elucidating][Optical][Glass][Interpretable machine learning]
• https://arxiv.org/abs/2108.05933[Phase classification][Multi-principal element alloy][Interpretable machine learning]
• https://arxiv.org/abs/2112.00239[Interpretable machine learning][Materials Design]
• https://arxiv.org/abs/2307.07609[Interpretable machine learning][Understand][Performance][Semi local density functionals][Materials thermochemistry]
• https://arxiv.org/abs/2409.14905[Interpretable machine learning][High-strength high-entropy alloy design]
• https://arxiv.org/abs/2410.05658[Identifying highly deformable van der Waals layered chalcogenides][Superior thermoelectric performance][Deformability factor][Interpretable machine learning]
• https://arxiv.org/abs/2502.15764[High-throughput computational screening][Interpretable machine learning][Metal-organic framework][Iodine capture]
• https://arxiv.org/abs/1810.10170[Machine learning][Band gap and alignment][Nitride semiconductor]
• https://arxiv.org/abs/1810.10042[Efficiently measuring][Quantum device ][Machine learning]
• https://arxiv.org/abs/1810.12814[Band gap prediction][Large organic crystal structure][Machine learning]
• https://arxiv.org/abs/2501.03456[Text to band gap][Pre-trained language model][Encoder][Semiconductor band gap prediction]
• https://arxiv.org/abs/2501.12919[Contrastive language-structure][Pre-training driven][Materials science literature]
• https://arxiv.org/abs/1811.00628[Independent vector analysis][Data fusion][Molecular property prediction][Machine learning]
• https://arxiv.org/abs/1811.01914[Machine learning][Molecular dynamics][Strongly correlated electrons]
• https://arxiv.org/abs/1811.08464[Machine learning][Phase stability][Disorder][Automatic flow framework][Materials discovery][PhD thesis]
• https://arxiv.org/abs/1811.09267[Efficient extraction][High-order force constant][Machine learning][hiphive package]
• https://arxiv.org/abs/1812.02708[Ternary mixed-anion semiconductor][Tunable band gap][Machine-learning][Crystal structure prediction]
• https://arxiv.org/abs/1812.04932[Catching the essence][Hohenberg-Kohn's first theorem][Recreating PES][cluster][Machine learning]
• https://arxiv.org/abs/2004.14442[Practical approach][Hohenberg-Kohn map][Many-body correlation][Learning the electronic density]
• https://arxiv.org/abs/2201.11991[Universal machine learning model][Elemental grain boundary energies]
• https://arxiv.org/abs/2402.09251[Universal machine learning][Kohn-Sham Hamiltonian][Material]
• https://arxiv.org/abs/2408.15165[Latent Ewald summation][Machine learning][Long-range interaction]
• https://arxiv.org/abs/2412.15455[Learning charge][Long-range interaction][Energies and forces]
• https://arxiv.org/abs/2411.15351[Accelerating CALPHAD-based phase diagram prediction][Complex alloy][Universal machine learning potential][Opportunities and challenges]
• https://arxiv.org/abs/2410.13820[Enhancing universal machine learning potential][Polarizable long-range interaction]
• https://arxiv.org/abs/2412.10516[CHIPS-FF][Evaluating universal machine learning force field][Material properties]
• https://arxiv.org/abs/2403.04217[Performance assessment][Universal machine learning interatomic potential][Challenges and Directions][Materials' surfaces]
• https://arxiv.org/abs/2405.07105[Overcoming systematic softening][Universal machine learning interatomic potential][Fine-tuning]
• https://arxiv.org/abs/2412.16551[Universal machine learning interatomic potential][Ready][Phonon]
• https://arxiv.org/abs/2502.03660[Energy & force regression][DFT Trajectories][Not enough][Universal machine learning interatomic potential]
• https://arxiv.org/abs/2502.03578[Universal machine learning interatomic potential][Poised][Supplant DFT][Modeling general defect][Metals and random alloys]
• https://arxiv.org/abs/2502.09970[Universal machine learning interatomic potential][Ready for solid ion conductor]
• https://arxiv.org/abs/1801.06219[Predicting colloidal crystal][Shape][Inverse design][Machine learning]
• https://arxiv.org/abs/1812.11212[Machine learning][Polymer cloud-point engineering][Inverse design]
• https://arxiv.org/abs/1901.01638[Accurate and transferable machine-learning][Interatomic potential][Silicon]
• https://arxiv.org/abs/2107.02599[Transferable machine-learning scheme][Pure metal][Alloy][Predicting adsorption energy]
• https://arxiv.org/abs/2205.02879[Exploiting ligand additivity][Transferable machine learning][Multireference character across][Transition metal complex ligand]
• https://arxiv.org/abs/2106.10768[Representation][Strategy][Transferable machine learning model][Chemical discovery]
• https://arxiv.org/abs/2404.07181[BAMBOO][Predictive and transferable machine learning force field framework][Liquid electrolyte development]
• https://arxiv.org/abs/2008.03023[Tackling the challenge][Huge materials science search space][Quantum-inspired annealing]
• https://arxiv.org/abs/1901.01963[Machine learning topological phases][Real space]
• https://arxiv.org/abs/1901.01501[Accelerated continuous time quantum Monte Carlo method][Machine learning]
• https://arxiv.org/abs/1901.02717[Reliable][Explainable][Machine learning][Accelerated material discovery]
• https://arxiv.org/abs/1901.05801[Machine learning][Novel thermal-materials Discovery][Early Successes][Opportunities][Challenges]
• https://arxiv.org/abs/1901.08615[Approximating power of machine-learning ansatz][Quantum many-body state]
• https://arxiv.org/abs/1901.09071[Layer dependence][Graphene-diamene phase transition][Epitaxial and exfoliated few-layer graphene][Machine learning]
• https://arxiv.org/abs/1901.10971[Machine-learning][Atomic-scale properties][Physical principles]
• https://arxiv.org/abs/1902.02941[Machine learning][Improved estimator][Magnetization curve][Spin gap]
• https://arxiv.org/abs/1902.03682[Paradigm shift][Electron-based crystallography][Machine learning]
• https://arxiv.org/abs/1902.03486[Machine learning Forcefield][Silicate glass]
• https://arxiv.org/abs/1902.04079[Multi-faceted machine learning][Competing order][Disordered interacting system]
• https://arxiv.org/abs/1902.05158[Atomistic machine learning][Prediction][Understanding]
• https://arxiv.org/abs/2305.05536[Coarse-grained versus fully atomistic machine learning][Zeolitic imidazolate framework]
• https://arxiv.org/abs/2306.15638[Robustness][Local prediction][Atomistic machine learning model]
• https://arxiv.org/abs/2404.12367[Information theory][Unifies atomistic machine learning][Uncertainty quantification][Materials thermodynamics]
• https://arxiv.org/abs/2405.02461[Uncertainty quantification][Propagation][Atomistic machine learning]
• https://arxiv.org/abs/1902.08818[Autonomous atomic scale Manufacturing][Machine learning]
• https://arxiv.org/abs/1902.09776[Predicting Young's modulus][Glass][Sparse Dataset][Machine learning]
• https://arxiv.org/abs/1903.00552[Single-component order parameter][URu₂Si₂][Uncovered][Resonant ultrasound spectroscopy][Machine learning]
• https://arxiv.org/abs/1903.06651[Accelerated discovery][Efficient solar-cell material][Quantum and machine-learning Methods]
• https://arxiv.org/abs/1903.06813[Machine learning][Voltage of electrode Material][Metal-ion battery]
• https://arxiv.org/abs/1903.08060[Hidden self-energy][Origin of cuprate superconductivity][Revealed by machine learning]
• https://arxiv.org/abs/1903.08482[Investigating ultrafast quantum magnetism][Machine learning]
• https://arxiv.org/abs/1810.00346[Extracting many-particle entanglement entropy][Observable][Supervised machine learning]
• https://arxiv.org/abs/1901.00788[Classification][Local chemical environment][X-ray absorption spectra][Supervised machine learning]
• https://arxiv.org/abs/2005.08131[Evaluation of synthetic][Experimental training data][Supervised machine learning][Charge state detection][Quantum dot]
• https://arxiv.org/abs/2502.09477[DiffRenderGAN][Addressing training data scarcity][Deep segmentation network][Quantitative nanomaterial analysis][Differentiable rendering][Generative modelling]
• https://arxiv.org/abs/1903.10742[Generative tensor network classification model][Supervised machine learning]
• https://arxiv.org/abs/2206.08841[Random projection][Kernelised leave one cluster][Out cross-validation][Universal baseline][Evaluation tool][Supervised machine learning][Materials properties]
• https://arxiv.org/abs/2501.02144[Establishing baseline][Generative discovery][Inorganic crystal]
• https://arxiv.org/abs/2211.08591[Exploring supervised machine learning][Multi-phase identification][Quantification][Powder X-ray diffraction spectra]
• https://arxiv.org/abs/2109.00570[Process parameter optimization][Friction stir welding][6061AA][Supervised machine learning regression-based algorithm]
• https://arxiv.org/abs/2309.04067[Prediction][Cu oxidation state][EELS and XAS Spectra][Supervised machine learning]
• https://arxiv.org/abs/1904.00031[NetKet][Machine learning toolkit][Many-body quantum system]
• https://arxiv.org/abs/1904.01486[Unveiling phase transition][Machine learning]
• https://arxiv.org/abs/1904.01756[Atomic-level characterisation][Quantum computer array][Machine learning]
• https://arxiv.org/abs/1904.03780[Linking plastic heterogeneity][Bulk metallic glass][Quench-in structural defect][Machine learning]
• https://arxiv.org/abs/1810.12700[Machine learning][Density functional theory][Hubbard model]
• https://arxiv.org/abs/1811.12425[Classifying snapshots][Doped Hubbard model][Machine learning]
• https://arxiv.org/abs/1901.07900[Characterization][Photoexcited state][Half-filled one-dimensional extended Hubbard model][Machine learning]
• https://arxiv.org/abs/1904.06032[Machine learning][Phase diagram][Half-filled one-dimensional extended Hubbard model]
• https://arxiv.org/abs/2211.09584[Mitigating][Hubbard sign problem][Novel application][Machine learning]
• https://arxiv.org/abs/2302.09507[Predicting structure-dependent Hubbard U parameter][Assessing hybrid functional-level exchange][Machine learning]
• https://arxiv.org/abs/2406.02457[Machine learning Hubbard parameter][Equivariant neural network]
• https://arxiv.org/abs/1904.08875[DScribe][Library of descriptors][Machine learning][Materials Science]
• https://arxiv.org/abs/1904.10423[Data ecosystem][Support machine learning][Materials Science]
• https://arxiv.org/abs/1905.01330[TensorNetwork][Library for physics and machine learning]
• https://arxiv.org/abs/1912.04055[FLAME][Library of atomistic modeling environments]
• https://arxiv.org/abs/1905.02350[Error minimization][Predicting accurate adsorption energy][Machine learning]
• https://arxiv.org/abs/1905.03938[Machine learning-guided synthesis][Advanced inorganic material]
• https://arxiv.org/abs/2404.13022[Machine learning-guided accelerated discovery][Structure-property correlation][Lean magnesium alloy][Biomedical application]
• https://arxiv.org/abs/2502.05411[Machine learning-guided construction][Analytic kinetic energy functional][Orbital free density functional theory]
• https://arxiv.org/abs/1905.04312[Machine learning][Quantum state][NISQ era]
• https://arxiv.org/abs/1905.06780[Functional form][Superconducting critical temperature][Machine learning]
• https://arxiv.org/abs/1905.09497[Machine-learning][Interatomic potential][Phonon transport][Perfect crystalline Si][Crystalline Si with vacancies]
• https://arxiv.org/abs/1906.03860[Quantum supremacy][Analog quantum processor][Material science][Machine learning]
• https://arxiv.org/abs/1906.05886[Predicting superhard material][Machine learning][Informed evolutionary structure search]
• https://arxiv.org/abs/1906.06329[TensorNetwork][Machine learning]
• https://arxiv.org/abs/1906.08534[Predicting][Curie temperature][Ferromagnet][Machine learning]
• https://arxiv.org/abs/1906.08888[Performance and cost assessment][Machine learning][Interatomic potential]
• https://arxiv.org/abs/1906.10155[Machine learning][Phase transition][Quantum processor]
• https://arxiv.org/abs/1907.01480[Exploring effective charge][Electromigration][Machine learning]
• https://arxiv.org/abs/1907.10587[Understanding magnetic properties][Actinide-based compound][Machine learning]
• https://arxiv.org/abs/1907.10290[Quantum compressed sensing][Unsupervised tensor network][Machine learning]
• https://arxiv.org/abs/1907.10929[Automatic microscopic image analysis][Moving window local Fourier transform][Machine learning]
• https://arxiv.org/abs/1907.12002[Machine learning][DFT potential energy surface][Inorganic halide perovskite][CsPbBr₃]
• https://arxiv.org/abs/1706.01840[Designing magnetism][Fe-based Heusler alloy][Machine learning approach]
• https://arxiv.org/abs/1901.00722[Machine learning approach][Thermal conductivity modeling][Irradiated uranium-molybdenum nuclear fuel]
• https://arxiv.org/abs/2306.08017[Transferable machine learning approach][Predicting electronic structure][Charged defect]
• https://arxiv.org/abs/1901.01972[Machine learning approach][Automated fine-tuning][Semiconductor spin qubit]
• https://arxiv.org/abs/1902.00594[Machine learning approach][Determine the space group of a structure][Atomic pair distribution function][PDF]
• https://arxiv.org/abs/1903.02757[Prediction of atomization energy][Au13+ Cluster][Machine learning approach]
• https://arxiv.org/abs/1908.04604[Valley notch filter][Graphene strain superlattice][Green's function][Machine learning approach]
• https://arxiv.org/abs/2205.15151[Berezinskii-Kosterlitz-Thouless transition][Unsupervised machine learning approach]
• https://arxiv.org/abs/2004.00008[Efficient machine learning approach][Optimizing][Timing resolution][High purity germanium detector]
• https://arxiv.org/abs/2008.08341[Phase formation][Mechanical property][Complex concentrated alloy][Machine learning approach]
• https://arxiv.org/abs/2010.04742[Machine learning approach][Muon spectroscopy analysis]
• https://arxiv.org/abs/2012.08657[Iterative machine learning approach][Discovering unexpected thermal conductivity enhancement][Aperiodic superlattice]
• https://arxiv.org/abs/2107.06044[Parametrization][ Non-bonded force field term][Metal-organic framework][Machine learning approach]
• https://arxiv.org/abs/2107.13149[Machine learning approach][Predict L-edge x-ray absorption spectra][Light transition metal ion compound]
• https://arxiv.org/abs/2110.12887[Descriptor][Intrinsic hydrodynamic thermal transport][Screening][Phonon database][Machine learning approach]
• https://arxiv.org/abs/2112.11537[Accelerating][Theoretical study][Li-polysulphide adsorption][Single-atom catalyst][Machine learning approache]
• https://arxiv.org/abs/2202.00564[Machine learning approach][Quantum phase][Frustrated one dimensional spin-1/2 system]
• https://arxiv.org/abs/2407.14148[Predicting grain boundary segregation][Magnesium alloy][Atomistically informed machine learning approach]
• https://arxiv.org/abs/2211.16576[Physics-based machine learning approach][Modeling][Temperature-dependent yield strength][Superalloy]
• https://arxiv.org/abs/2207.09444[Machine learning approach][Genome][Two-dimensional material][Flat electronic band]
• https://arxiv.org/abs/2208.04172[Impact of physicochemical features][Carbon electrode][Capacitive performance][Supercapacitor][Machine learning approach]
• https://arxiv.org/abs/2307.05211[Data-driven machine learning approach][Predicting yield strength][Additively manufactured multi-principal element alloy]
• https://arxiv.org/abs/2208.12705[Machine learning approach][Predict][Structural and magnetic properties][Heusler alloy families]
• https://arxiv.org/abs/2209.08595[Low-cost machine learning approach][Prediction][Transition metal phosphor][Excited state properties]
• https://arxiv.org/abs/2211.04373[Machine learning approach][Drawing phase diagram][Topological lasing mode]
• https://arxiv.org/abs/2309.07424[Composition][Structure][GGA bandgap prediction][Machine learning approach]
• https://arxiv.org/abs/2407.07736[Prediction][Frequency-dependent optical spectrum][Solid material][Multi-output][Multi-fidelity machine learning approach]
• https://arxiv.org/abs/2406.00042[Rise and fall of Anderson localization][Lattice vibrations][Time-dependent machine learning approach]
• https://arxiv.org/abs/2403.02553[Enhancing magnetocaloric material discovery][Machine learning approach][Autogenerated Database][Large language model]
• https://arxiv.org/abs/2406.07345[Machine learning approach][Classical density functional theory]
• https://arxiv.org/abs/2411.18721[Machine learning approach][Capturing hidden parameter][Autonomous thin-film deposition]
• https://arxiv.org/abs/2502.17534[Machine learning approach][Design of frequency selective surface][Radar absorbing material][Image prediction]
• https://arxiv.org/abs/2405.03274[MACE][Machine learning approach][Chemistry emulation]
• https://arxiv.org/abs/2411.00436[Modelling silica][MACE-MP-0][Machine learnt interatomic potential]
• https://arxiv.org/abs/2409.01523[Machine learning approach][Vibronically renormalized electronic band structure]
• https://arxiv.org/abs/1908.04762[Prediction magnetocrystalline anisotripy][Fe-Rh thin film][Machine leaning]
• https://arxiv.org/abs/1908.05829[Machine learning][Magnetic parameter][Spin configuration]
• https://arxiv.org/abs/1908.06198[Machine learning][Physical non-local exchange-correlation functional][DFT]
• https://arxiv.org/abs/2410.07972[Learning][Equivariant non-local electron density functional]
• https://arxiv.org/abs/1908.09102[Accelerating small-angle scattering experiment][Simulation-based machine learning]
• https://arxiv.org/abs/2501.10651[MOFA][Discovering materials][Carbon capture][GenAI- and simulation-based workflow]
• https://arxiv.org/abs/1908.10953[Predicting outcome][Catalytic reaction][Machine learning]
• https://arxiv.org/abs/1909.05915[Uncovering atomistic mechanism][Crystallization][Machine learning]
• https://arxiv.org/abs/1909.07080[Machine learning][Force field][Metallic nanoparticle]
• https://arxiv.org/abs/1909.10768[Absorption spectra][Charge transfer][PEDOT nanoaggregate][Machine learning]
• https://arxiv.org/abs/1910.02496[Machine learning design][Trapped-ion quantum spin simulator]
• https://arxiv.org/abs/1910.06291[The Materials Simulation Toolkit][Machine learning][MAST-ML][Automated open source toolkit][Accelerate data-driven materials research]
• https://arxiv.org/abs/1910.10254[Machine learning inter-atomic potential][Generation driven][Active learning][Amorphous][Liquid hafnium dioxide]
• https://arxiv.org/abs/1910.10161[Detection of topological materials][Machine learning]
• https://arxiv.org/abs/1910.11300[Machine learning effective model][Quantum system]
• https://arxiv.org/abs/1910.13453[Vulnerability][Machine learning phases of matter]
• https://arxiv.org/abs/1911.02416[Predicting densities and elastic moduli of SiO2-based glasses][Machine learning]
• https://arxiv.org/abs/1911.02263[Surfing multiple conformation-property landscape][Machine learning][Designing magnetic anisotropy]
• https://arxiv.org/abs/1911.05569[Forecasting system][Computational time][DFT][Multiverse ansatz][Machine Learning][Cheminformatics]
• https://arxiv.org/abs/1911.07571[Casimir effect][Machine learning]
• https://arxiv.org/abs/1911.09578[Engineering quantum current state][Machine learning]
• https://arxiv.org/abs/1911.12576[Optimization][Heterogeneous ternary Li₃PO₄-Li₃BO₃-Li₂SO₄ mixture][Li-ion conductivity][Machine learning]
• https://arxiv.org/abs/1912.03578[Cooling rate][Structural Relaxation][Amorphous drug][Elastically collective nonlinear Langevin equation theory][Machine learning]
• https://arxiv.org/abs/2001.00030[Quantum adversarial][Machine learning]
• https://arxiv.org/abs/2408.09297[Out-of-distribution materials property prediction][Adversarial learning][Fine-tuning]
• https://arxiv.org/abs/1912.13460[Machine learning][Effective Hamiltonian][High entropy alloy]
• https://arxiv.org/abs/2001.02589[Machine learning][Completely automatic tuning][Quantum device faster than human experts]
• https://arxiv.org/abs/2001.04605[Machine learning][Materials modeling][Fundamental][Opportunity][2D material]
• https://arxiv.org/abs/2001.05150[Machine learning analysis][Tunnel magnetoresistance][Magnetic tunnel junction][Disordered MgAl₂O₄]
• https://arxiv.org/abs/2004.00080[Machine learning analysis][Perovskite oxides grown][Molecular beam epitaxy]
• https://arxiv.org/abs/2112.09601[Joint machine learning analysis][Muon spectroscopy data][Different materials]
• https://arxiv.org/abs/2306.11853[Generalization across experimental parameter][Machine learning analysis][High resolution transmission electron microscopy dataset]
• https://arxiv.org/abs/2409.17321[Machine learning analysis][Structural data][Predict electronic properties][Near-surface InAs quantum well]
• https://arxiv.org/abs/2001.06728[Big-data science][Porous material][Materials genomics][Machine learning]
• https://arxiv.org/abs/2001.08889[Determination][Glass transition temperature][Polyimide][Atomistic molecular dynamics][Machine-learning algorithm]
• https://arxiv.org/abs/2001.10565[Classification][Strongly disordered topological wire][Machine learning]
• https://arxiv.org/abs/2001.11328[Accelerating continuum-scale brittle fracture simulation][Machine learning]
• https://arxiv.org/abs/2001.11322[Machine learning][Plastic deformation]
• https://arxiv.org/abs/2001.11153[Characterizing transition-metal dichalcogenide thin-film][Hyperspectral imaging][Machine learning]
• https://arxiv.org/abs/2312.05201[Embedding theory][ML][Real-time tracking][Structural dynamics][Hyperspectral dataset]
• https://arxiv.org/abs/2002.02994[Pairing glue][Cuprate superconductor][Self-energy][Machine learning]
• https://arxiv.org/abs/2002.05225[Accelerated design][Fe-based soft magnetic material][Machine learning][Stochastic optimization]
• https://arxiv.org/abs/1802.10127[Identifying melting Points of Metals][Classical MD][Unsupervised machine learning]
• https://arxiv.org/abs/2008.03275[Harnessing interpretable and unsupervised machine learning][Address Big Data][Modern X-ray diffraction]
• https://arxiv.org/abs/2007.08361[Unsupervised machine learning][Transfer learning][k-means clustering][Classify materials image data]
• https://arxiv.org/abs/2001.01711[Unsupervised machine learning][Band topology][PRL 124, 226401(2020)]
• https://arxiv.org/abs/2002.07266[Predicting new superconductors][Critical temperature][Unsupervised machine learning]
• https://arxiv.org/abs/2002.10004[Nanoscale electronic inhomogeneity][FeSe_0.4Te_0.6][Unsupervised machine learning]
• https://arxiv.org/abs/2005.10581[Spherical-angular dark field imaging][Sensitive microstructural phase clustering][Unsupervised machine learning]
• https://arxiv.org/abs/2010.09196[Unsupervised machine learning discovery][Chemical transformation pathway][Atomically-resolved imaging data]
• https://arxiv.org/abs/2101.05712[Unsupervised machine learning][Topological phase transition][Experimental data]
• https://arxiv.org/abs/2101.10161[Phase diagram study][Two-dimensional frustrated antiferromagnet][Unsupervised machine learning]
• https://arxiv.org/abs/2207.02075[Clustering superconductor][Unsupervised machine learning]
• https://arxiv.org/abs/2212.13550[Nanomaterial][Supercapacitor][Uncovering research theme][Unsupervised machine learning]
• https://arxiv.org/abs/2404.10943[Unsupervised machine learning][Detection][Exotic phase][Skyrmion phase diagram]
• https://arxiv.org/abs/2407.18320[Solving physics-based initial value problem][Unsupervised machine learning]
• https://arxiv.org/abs/2411.07319[Unsupervised machine learning phase classification][Falicov-Kimball model]
• https://arxiv.org/abs/2212.14087[Machine learning][Memory][Parent phase][Ferroelectric relaxor][Ba(Ti_1¡Ýx,Zr_x)O₃]
• https://arxiv.org/abs/2004.10731[Unsupervised segmentation-based machine learning][Advanced analysis tool][Single molecule break junction data]
• https://arxiv.org/abs/2003.00994[Machine learning][Spectral indicator][Topology]
• https://arxiv.org/abs/2003.01878[Physics-informed feature engineering approach][Machine learning][Limited amounts of data][Alloy design][Shape memory alloy demonstration]
• https://arxiv.org/abs/2003.01943[Machine learning][Predicting and understanding][Mechanical property][Steel]
• https://arxiv.org/abs/2003.02722[Chemical bonding][Metallic Glass][Machine learning][Crystal orbital Hamilton population]
• https://arxiv.org/abs/2008.07694[False metal][Real insulator][Degenerate gapped metal]
• https://arxiv.org/abs/2008.07923[Learning DFT]
• https://arxiv.org/abs/2003.04550[Sampling strategy][Efficient potential energy surface mapping][Predicting atomic diffusivity][Machine learning]
• https://arxiv.org/abs/2003.11040[Machine learning][Quantum matter]
• https://arxiv.org/abs/2003.12081[Representation][Molecule][Material][Interpolation][Quantum-mechanical simulation][Machine learning]
• https://arxiv.org/abs/2003.13418[Machine learning][Discovery of application dependent design principles][Two-dimensional material]
• https://arxiv.org/abs/2003.13388[ML4Chem][Machine learning package][Chemistry][Materials Science]
• https://arxiv.org/abs/2411.15945[Understanding machine learning paradigm][Lens of statistical thermodynamics][Tutorial]
• https://arxiv.org/abs/2004.00232[Machine learning][Multi-fidelity scale bridging][Dynamical simulation]
• https://arxiv.org/abs/2204.01788[Efficient machine-learning model][Fast assessment][Elastic properties][High-entropy alloy]
• https://arxiv.org/abs/2004.03025[Efficient computational design][2D van der Waals heterostructure][Band-alignment][Lattice-mismatch][Web-app generation][Machine-learning]
• https://arxiv.org/abs/2004.04439[Prediction][Mechanical property][Non-equiatomic high-entropy alloy][Atomistic simulation][Machine learning]
• https://arxiv.org/abs/2501.11297[Machine-learning bond-order potential][Exploring][Configuration space][Carbon]
• https://arxiv.org/abs/1611.03277[Machine-learning based interatomic potential][Amorphous carbon]
• https://arxiv.org/abs/2004.04882[Machine-learning based extraction][Short-range part][Interaction][Non-contact atomic force microscopy]
• https://arxiv.org/abs/2404.08653[Machine-learning based selection][Synthesis][Candidate metal-insulator transition metal oxides]
• https://arxiv.org/abs/2208.12736[Machine-learning based screening][Lead-free halide double perovskite][Photovoltaic application]
• https://arxiv.org/abs/2211.07999[Machine-learning based prediction][Small molecule][Surface interaction potential]
• https://arxiv.org/abs/2409.12820[Machine-learning based high-bandwidth magnetic sensing]
• https://arxiv.org/abs/2501.04684[Novel highly parallelizable machine-learning based method][Fast solution][Integral equation][Electromagnetic scattering problem]
• https://arxiv.org/abs/2502.00234[Fast solver][Discrete diffusion model][Theory and applications][High-order algorithm]
• https://arxiv.org/abs/2004.05424[Machine learning][Coupling physics][Predict][High-temperatures alloy]
• https://arxiv.org/abs/2004.06703[Stacking fault energy prediction][Austenitic steel][Thermodynamic modeling][Machine learning]
• https://arxiv.org/abs/2209.12036[Temperature-dependent anharmonic phonon][Quantum paraelectric KTaO₃][First principles][Machine-learned force field]
• https://arxiv.org/abs/2109.06282[Phase transition][Zirconia][Machine-learned force field][Beyond DFT]
• https://arxiv.org/abs/2302.12993[Complexity][Many-body interaction][Transition metal][Machine-learned force field][TM23 data set]
• https://arxiv.org/abs/2404.09755[Accurate quantum Monte Carlo force][Machine-learned force field][Ethanol][Benchmark]
• https://arxiv.org/abs/2410.15746[Dynamical disorder][Mesophase ferroelectric HdabcoClO₄][Machine-learned force field study]
• https://arxiv.org/abs/2501.04876[Modelling complex proton transport phenomena][Exploring][Limits of fine-tuning and transferability][Foundational machine-learned force field]
• http://arxiv.org/abs/1609.05737[Machine-learned approximation][Density functional theory Hamiltonian]
• https://arxiv.org/abs/2502.01171[Efficient and scalable density functional theory Hamiltonian prediction][Adaptive sparsity]
• https://arxiv.org/abs/1903.09137[Modeling meso-scale energy localization][Shocked HMX][Part II][Training machine-learned surrogate model][Void shape][Void-void interaction]
• https://arxiv.org/abs/1906.02244[Machine-learned impurity level prediction][Semiconductor][Example of Cd-based chalcogenides]
• https://arxiv.org/abs/1911.11201[Machine-learned metrics][Predicting][likelihood of success][Materials discovery]
• https://arxiv.org/abs/2001.10591[Critical examination][Compound stability prediction][Machine-learned formation energy]
• https://arxiv.org/abs/2004.07295[Understanding high pressure hydrogen][Hierarchical machine-learned potential]
• https://arxiv.org/abs/2102.01103[Machine-learned phase diagram][Generalized Kitaev honeycomb magnet]
• https://arxiv.org/abs/2012.04489[alpha-beta phase transition][Zirconium][On-the-fly machine-learned force field]
• https://arxiv.org/abs/2303.00682[Nonlocal machine-learned exchange functional][Molecule][Solid]
• https://arxiv.org/abs/2307.08929[On-the-fly machine learning][Parametrization][Effective Hamiltonian]
• https://arxiv.org/abs/2404.07961[Overcoming][Chemical complexity bottleneck][On-the-fly machine learned molecular dynamics simulation]
• https://arxiv.org/abs/2104.03831[Machine-learned prediction][Electronic fields in a crystal]
• https://arxiv.org/abs/2112.03707[Critical assessment][Machine-learned repulsive potential][Density functional based tight-binding method][Case study for pure silicon]
• https://arxiv.org/abs/2004.08267[To switch or not to switch][Machine learning][Ferroelectricity]
• https://arxiv.org/abs/2004.08753[Machine learning][Metastable phase diagram]
• https://arxiv.org/abs/2004.08527[Large family][Two-dimensional ferroelectric metal][Discovered][Machine learning]
• https://arxiv.org/abs/2004.10091[Molecular design][Signal processing][Machine learning][Time-frequency-like representation][Forward design]
• https://arxiv.org/abs/2004.11817[Fast scanning probe microscopy][Machine Learning][Non-rectangular scan][Compressed sensing][Gaussian process optimization]
• https://arxiv.org/abs/2004.14766[Machine learning][Small dataset]
• https://arxiv.org/abs/2004.14407[Learning from Sparse Dataset][Predicting concrete's strength][Machine Learning]
• https://arxiv.org/abs/2004.14415[Revealing the phase diagram][Kitaev material][Machine Learning][Cooperation][Competition][Spin liquid]
• https://arxiv.org/abs/2005.00721[Learning What a Machine Learns][Many-body][Localization transition]
• https://arxiv.org/abs/2005.00951[Machine learning][Surrogate crystal plasticity model][Spatially resolved 3D orientation evolution][Uniaxial tension]
• https://arxiv.org/abs/2005.03595[Machine learning][Diffractive imaging][Subwavelength resolution]
• https://arxiv.org/abs/2005.05235[Machine learning][Materials development][Additive manufacturing]
• https://arxiv.org/abs/2005.04338[Multi-fidelity graph network][Machine learning][Experimental][Ordered and disordered materials]
• https://arxiv.org/abs/2005.04387[Eddy current testing][Metal crack][Spin Hall magnetoresistance sensor][Machine learning]
• https://arxiv.org/abs/2005.05831[Modelling][Dielectric constant][Machine learning]
• https://arxiv.org/abs/2005.08872[Prediction][Reduced glass transition temperature][Machine learning]
• https://arxiv.org/abs/2005.10210[Machine learning route][Band mapping][Band structure]
• https://arxiv.org/abs/2005.13046[Machine learning][Formation enthalpy][Intermetallics]
• https://arxiv.org/abs/2005.13265[Machine learning][Predicting][Time dependent dynamics][Local yielding][Dry foam]
• https://arxiv.org/abs/2005.14142[Dirac-type nodal spin liquid][Machine learning]
• https://arxiv.org/abs/2005.14260[Overview][Computer vision][Machine learning][Microstructural characterization and analysis]
• https://arxiv.org/abs/2005.14228[Machine learning][Condensed matter physics]
• https://arxiv.org/abs/2006.03320[Automatic machine-learning][Potential generation scheme][Simulation protocol][LiGePS-type superionic conductor]
• https://arxiv.org/abs/2006.04205[Machine learning dynamics][Phase separation][Correlated electron magnet]
• https://arxiv.org/abs/2006.10001[Investigating phase transition][Local crystallographic analysis][Machine learning][Atomic environment]
• https://arxiv.org/abs/2204.07230[Learning two-phase microstructure evolution][Neural operator][Autoencoder architecture]
• https://arxiv.org/abs/2006.10267[Machine learning][Chemical transformation][Si-graphene system][Atomically resolved image][Variational autoencoder]
• https://arxiv.org/abs/2107.08013[Machine-learning Kondo physics][Variational autoencoder]
• https://arxiv.org/abs/2401.11967[Exploring descriptor][Titanium microstructure][Digital fingerprint][Variational autoencoder]
• https://arxiv.org/abs/2212.13120[Neural structure field][Application][Crystal structure autoencoder]
• https://arxiv.org/abs/2303.18236[Physics][Chemistry][Parsimonious representation][Image analysis][Invariant variational autoencoder]
• https://arxiv.org/abs/2408.14928[Targetin][Partition function][Chemically disordered material][Generative approach][Inverse variational autoencoder]
• https://arxiv.org/abs/2412.16200[Robust spectral anomaly detection][EELS spectral image][Three dimensional convolutional variational autoencoder]
• https://arxiv.org/abs/2501.05312[Unveiling structure-property correlation][Ferroelectric Hf_0.5Zr_0.5O₂ film][Variational autoencoder]
• https://arxiv.org/abs/2502.09423[Transformer-enhanced variational autoencoder][Crystal structure prediction]
• https://arxiv.org/abs/2503.17427[3D variational autoencoder][Fingerprinting microstructure][Volume element]
• https://arxiv.org/abs/2311.17920[Autoencoder-based analytic continuation method][Strongly correlated quantum system]
• https://arxiv.org/abs/2407.04631[Autoencoder][Compressing angle-resolved photoemission spectroscopy data]
• https://arxiv.org/abs/2410.18148[Deep autoencoder][SVD-like convergence][Flat minima]
• https://arxiv.org/abs/2501.14709[Enhanced confocal laser scanning microscopy][Adaptive physics informed deep autoencoder]
• https://arxiv.org/abs/2309.06449[Quantized non-volatile nanomagnetic synapse][Autoencoder][Efficient unsupervised network anomaly detection]
• https://arxiv.org/abs/2503.06325[Understanding][Role of autoencoders][Stiff dynamical system][Information theory]
• https://arxiv.org/abs/2411.02126[Unsupervised detection][Semantic correlation][Big data]
• https://arxiv.org/abs/2310.07927[Enhanced sampling][Crystal nucleation][Graph representation learnt variables]
• https://arxiv.org/abs/2006.11221[Leveraging machine learning][Alleviate Hubbard model][Sign problem]
• https://arxiv.org/abs/2405.14649[Leveraging machine learning][Advanced nanoscale X-ray analysis][Unmixing multicomponent signal][Enhancing chemical quantification]
• https://arxiv.org/abs/2006.13203[Machine learning][Active-nematic hydrodynamics]
• https://arxiv.org/abs/2006.14604[Opportunities and challenges][Machine Learning][Materials science][Annual Reviews of Materials Research, vol. 50, 2020]
• https://arxiv.org/abs/2006.14302[Database of 2D hybrid perovskite materials][Open-access collection][Crystal structure][Band gap][Atomic partial charge][Predicted by machine learning]
• https://arxiv.org/abs/2006.15898[Machine learning identification][Impurity][STM Image]
• https://arxiv.org/abs/2006.15227[Bypassing the computational bottleneck][Quantum-embedding theory][Strong electron correlation][Machine learning]
• https://arxiv.org/abs/2006.16833[Machine learning depinning][Dislocation pileup]
• https://arxiv.org/abs/2007.07531[Accelerating inverse crystal structure prediction][Machine learning][Carbon allotrope]
• https://arxiv.org/abs/2007.07512[Machine learning][Electron-boson mechanism][Superconductor]
• https://arxiv.org/abs/2007.10646[Accelerated mapping][Electronic density of states pattern][Metallic nanoparticle][Machine-learning]
• https://arxiv.org/abs/2007.12875[Fast evaluation][Interaction integral][Confined system][Machine learning]
• https://arxiv.org/abs/2007.13610[Analytic continuation][Self-energy][Machine Learning]
• https://arxiv.org/abs/2007.14206[Machine learning][Potential repository]
• https://arxiv.org/abs/2007.14832[Predicting][Molecular material][Multiscale simulation workflow][Machine learning]
• https://arxiv.org/abs/2007.14809[Machine learning phase][Criticality][Real data for training]
• https://arxiv.org/abs/1710.04187[Machine learning potential][Graphene]
• https://arxiv.org/abs/2307.10072[Efficient][Accurate][Transferable machine learning potential][Application][Dislocation][Cracks in Iron]
• https://arxiv.org/abs/2406.08275[Accurate and transferable machine learning interatomic potential][Equimolar and non-equimolar high-entropy diborides]
• https://arxiv.org/abs/1907.09088[Thermal conductivity modeling][Machine learning potential][Application to crystalline and amorphous silicon]
• https://arxiv.org/abs/2008.10773[Enabling robust offline active learning][Machine learning potential][Simple physics-based prior]
• https://arxiv.org/abs/1912.01789[Unravelling complex strengthening mechanism][NbMoTaW multi-principal element alloy][Machine learning potential]
• https://arxiv.org/abs/2212.14611[Complex dislocation network structure][Mixed small angle grain boundaries][Unraveled][Machine learning potential molecular dynamics]
• https://arxiv.org/abs/2006.05475[Simple and efficient algorithms][Training machine learning potential][Force data]
• https://arxiv.org/abs/2006.13655[Accurate][Transferable][Machine learning potential][Carbon]
• https://arxiv.org/abs/2007.11448[Machine learning potential][Hexagonal boron nitride][Thermally][Mechanically][Rippling]
• https://arxiv.org/abs/2007.15944[Application of machine learning potential][Predict][Grain boundary][fcc elemental metal]
• https://arxiv.org/abs/2008.09750[Machine learning potential][Multicomponent system][Ti-Al binary system]
• https://arxiv.org/abs/2008.10977[Bin and hash method][Analyzing reference data and descriptors][Machine Learning Potential]
• https://arxiv.org/abs/2105.11959[Structure and lattice thermal conductivity][Grain boundary][Silicon][Machine learning potential][Molecular dynamics]
• https://arxiv.org/abs/2107.02594[Accelerated identification][Equilibrium structure][Multicomponent inorganic crystal][Machine learning potential]
• https://arxiv.org/abs/2107.11311[AENET-LAMMPS][AENET-TINKER][Interface][Accurate][Efficient][Molecular dynamics simulation][Machine learning potential]
• https://arxiv.org/abs/2109.14068[Insight][Lithium manganese oxide-water interface][Machine learning potential]
• https://arxiv.org/abs/2112.10434[Machine learning potential][Always extrapolate][It does not matter]
• https://arxiv.org/abs/2201.01370[Interatomic machine learning potential][Aluminium][Application][Solidification phenomena]
• https://arxiv.org/abs/2201.07648[Convergence acceleration][Machine learning potential][Atomistic simulation]
• https://arxiv.org/abs/2202.13009[Long-range dispersion-inclusive machine learning potential][Structure search][Optimization][Hybrid organic-inorganic interface]
• https://arxiv.org/abs/2203.07219[Extending][Quantum computihttps://arxiv.org/abs/2203.09613ng][Materials science][Machine learning potential]
• https://arxiv.org/abs/2207.04009[Systematic structure dataset][Machine learning potential][Application][Moment tensor potential][Magnesium][Defect]
• https://arxiv.org/abs/2209.13823[Systematic development][Polynomial machine learning potential][Metallic][Alloy system]
• https://arxiv.org/abs/2401.14877[Predictive power][Polynomial machine learning potential][Liquid state][22 elemental systems]
• https://arxiv.org/abs/2401.17531[On-the-fly training][Polynomial machine learning potential][Computing lattice thermal conductivity]
• https://arxiv.org/abs/2403.02570[Globally-stable][Metastable crystal structure enumeration][Polynomial machine learning potential][Elemental As, Bi, Ga, In, La, P, Sb, Sn, and Te]
• https://arxiv.org/abs/2407.20630[Polynomial machine learning potential][Application][Global structure search][Ternary Cu-Ag-Au alloy]
• https://arxiv.org/abs/2210.01378[Lattice dynamics][Elastic properties][alpha-U][High-temperature][High-pressure][Machine learning potential simulation]
• https://arxiv.org/abs/2211.05713[Simulation][Machine learning potential][Identify][Ion conduction mechanism][Mediating non-Arrhenius behavior][LGPS]
• https://arxiv.org/abs/2301.03497[Phase transition][Inorganic halide perovskite][Machine learning potential]
• https://arxiv.org/abs/2306.03989[Machine learning potential-based generative algorithm][On-lattice crystal structure prediction]
• https://arxiv.org/abs/2306.07246[Reliable machine learning potential][Artificial neural network][Graphene]
• https://arxiv.org/abs/2307.15528[Machine learning potential][Modelling H₂ Adsorption/Diffusion][MOF][Open metal site]
• https://arxiv.org/abs/2307.15127[Unravelling negative][In-plane stretchability][2D MOF][Large scale machine learning potential molecular dynamics]
• https://arxiv.org/abs/2309.01089[Vacancy ordering][Boson peak][Metastable cubic Ge-Sb-Te][Machine learning potential]
• https://arxiv.org/abs/2310.19350[Disorder-dependent Li diffusion][Li₆PS₅Cl investigated][Machine learning potential]
• https://arxiv.org/abs/2311.04461[Hydrogen diffusion][Lower mantle][Revealed][Machine learning potential]
• https://arxiv.org/abs/2401.06622[Capturing short-range order][High-entropy alloy][Machine learning potential]
• https://arxiv.org/abs/2401.17875[Perspective][Atomistic simulation][Water and aqueous systems][Machine learning potential]
• https://arxiv.org/abs/2402.08834[Machine learning potential][Powered insight][Mechanical stability][Amorphous Li-Si alloy]
• https://arxiv.org/abs/2403.02155[Accelerating fourth-generation machine learning potential][Quasi-linear scaling particle mesh charge equilibration]
• https://arxiv.org/abs/2403.05724[From electrons to phase diagrams][Classical and machine learning potentials][Automated workflow][Materials science][Pyiron]
• https://arxiv.org/abs/2405.00306[Environment-adaptive machine learning potential]
• https://arxiv.org/abs/2406.00183[Predicting solvation free energies][Implicit solvent machine learning potential]
• https://arxiv.org/abs/2406.07157[Machine learning potential][Cu-W system]
• https://arxiv.org/abs/2406.08243[Efficient strategy][Construct general machine learning potential][High-entropy ceramics]
• https://arxiv.org/abs/2409.20235[Gegeneral machine learning model][Aluminosilicate melt viscosity][Application][Surface properties][Dry lava planet]
• https://arxiv.org/abs/2406.13178[Efficient modelling][Anharmonicity and quantum effects][PdCuH₂][Machine learning potential]
• https://arxiv.org/abs/2406.17499[Evaluating][Improving][Predictive accuracy][Mixing enthalpies][Volume][Disordered alloy][Universal pre-trained machine learning potential]
• https://arxiv.org/abs/2407.21088[Hydrogen diffusion][Magnesium][Machine learning potential]
• https://arxiv.org/abs/2407.21088[Hydrogen diffusion][Magnesium][Machine learning potential]
• https://arxiv.org/abs/2408.13416[Accelerating material melting temperature prediction][Implementing machine learning potential][SLUSCHI package]
• https://arxiv.org/abs/2408.16157[Importance of learning without Constraints][Reevaluating benchmark][Invariant and equivariant features][Machine learning potential][Generating free energy landscape]
• https://arxiv.org/abs/2503.05771[Materials foundation model][Hybrid invariant-equivariant architectures]
• https://arxiv.org/abs/2411.00720[Machine learning potential][Heterogeneous catalysis]
• https://arxiv.org/abs/2411.10122[Self-learning path integral hybrid Monte Carlo][Mixed ab initio and machine learning potentials][Modeling nuclear quantum effect][Water]
• https://arxiv.org/abs/2411.10973[Quantitative evaluation][Nuclear quantum effect][Phase transition][BaTiO₃][Large-scale molecular dynamics simulation][Machine learning potential]
• https://arxiv.org/abs/2412.00522[Extending][Atomic decomposition][Many-body representation][Chemistry-motivated monomer-centered approach][Machine learning potential]
• https://arxiv.org/abs/2501.00589[Efficient training][Machine learning potential][Metallic glasses][CuZrAl validation]
• https://arxiv.org/abs/2501.05250[Providing machine learning potential][High quality uncertainty estimate]
• https://arxiv.org/abs/2502.13522[Enhancing machine learning potential][Transfer learning][Chemical elements]
• https://arxiv.org/abs/2503.11537[Basic stability test][Machine learning potential][Molecular simulation][Computational drug discovery]
• https://arxiv.org/abs/2402.03219[Experiment-driven atomistic materials modeling][Case study][Combining][XPS][ML potential][Oxygen-rich amorphous carbon]
• https://arxiv.org/abs/2406.12909[Scalable training][Graph foundation model][Atomistic materials modeling][HydraGNN]
• https://arxiv.org/abs/2408.04765[Scalable learning][Potential][Predict time-dependent Hartree-Fock dynamics]
• https://arxiv.org/abs/2008.01243[Predicting][Activity][Selectivity][Bimetallic metal catalyst][Ethanol Reforming][Machine learning]
• https://arxiv.org/abs/2008.03189[Assessment of the structural resolution][Various fingerprints][Machine learning]
• https://arxiv.org/abs/2008.05125[Prediction][Rare-earth 2-17-X Magnets Ce₂Fe_17-xCo_xCN][Combined machine-learning and ab-initio Study]
• https://arxiv.org/abs/2302.08101[GAASP][Genetic algorithm based atomistic sampling protocol][High-entropy material]
• https://arxiv.org/abs/2411.18087[Adaptive genetic algorithm][Determining optimal structure][Atomic clusters]
• https://arxiv.org/abs/2008.09187[Designing][Optical glass][Machine learning][Genetic algorithm]
• https://arxiv.org/abs/2008.09681[Unifying framework][Strong and fragile liquid][Machine learning][[Liquid silica]
• https://arxiv.org/abs/2008.11213[Engineering topological phase][Statistical and machine learning method]
• https://arxiv.org/abs/2008.11277[Detecting non-local effect][Simple covalent system][Machine learning method]
• https://arxiv.org/abs/2008.12412[Predicting band gap][Band-edge position][Oxide perovskite][DFT][Machine learning]
• https://arxiv.org/abs/2009.03194[Predicting thermal, mechanical, and optical properties][Oxide glass][Machine learning][Large dataset]
• https://arxiv.org/abs/2209.00658[Stable solid molecular hydrogen][Above 900K][Machine-learned potential trained][Diffusion Quantum Monte Carlo][Phys. Rev. Lett. 130, 076102 (2023)]
• https://arxiv.org/abs/2009.05050[Charting][Low-loss region][Electron energy loss spectroscopy][Machine learning]
• https://arxiv.org/abs/2009.06476[Machine learning][Non-Hermitian topological phase]
• https://arxiv.org/abs/2009.08501[Separating physically distinct mechanism][Complex infrared plasmonic nanostructure][Machine learning][Electron energy loss spectroscopy]
• https://arxiv.org/abs/2009.11569[IrO₂ surface complexion][Identified][Machine learning][Surface Investigation]
• https://arxiv.org/abs/2009.12082[Probing][Transition][Dislocation jamming][Pinning][Machine learning]
• https://arxiv.org/abs/2010.00397[Upscaling][Porosity-permeability relationship][Microporous carbonate][Darcy scale][Machine learning]
• https://arxiv.org/abs/2010.01030[Machine-learning-enhanced][Time-of-flight mass spectrometry analysis]
• https://arxiv.org/abs/2010.03702[Quantitative prediction][Photo-emission dynamics][Metal halide perovskite][Machine learning]
• https://arxiv.org/abs/2010.04815[Data-centric framework][Crystal structure identification][Atomistic simulation][Machine learning]
• https://arxiv.org/abs/2010.07683[Potential][Challenge][Polymer informatics][Exploiting machine learning][Polymer design]
• https://arxiv.org/abs/2010.08151[Multiscale][Nanoconfined charging dynamics][Supercapacitor][Machine learning]
• https://arxiv.org/abs/2010.09438[How machine learning can help the design and analysis][Composite materials][Structure]
• https://arxiv.org/abs/2010.09435[Introduction to electrocatalyst design][Machine learning][Renewable energy storage]
• https://arxiv.org/abs/2010.14048[MeltNet][Predicting alloy melting temperature][Machine learning]
• https://arxiv.org/abs/2010.16099[MLatticeABC][Generic lattice constant prediction][Crystal material][Machine learning]
• https://arxiv.org/abs/2011.00383[Machine learning forecasting][Active nematics]
• https://arxiv.org/abs/2011.01262[Structural signature][Thermodynamic stability][Vitreous silica][Insight][[Machine Learning][Molecular dynamics simulation]
• https://arxiv.org/abs/2011.02259[Accelerating coupled cluster calculation][Nonlinear dynamics][Shallow machine learning]
• https://arxiv.org/abs/2011.02038[Machine learning][Evolutionary prediction][Superhard B-C-N compound]
• https://arxiv.org/abs/2011.03861[Supervised learning][Physical network][From machine learning to learning machines]
• https://arxiv.org/abs/2203.09788[Orientation adaptive minimal learning machine][Application][Thiolate-protected gold nanocluster][Gold-thiolate ring]
• https://arxiv.org/abs/2404.08657[Advancing extrapolative prediction][Material properties][Learning to learn]
• https://arxiv.org/abs/2011.06900[Evolutionary computing][Machine learning][Discovering][Low-energy defect configuration]
• https://arxiv.org/abs/2011.09458[Machine learning][Phase behavior][Active matter system]
• https://arxiv.org/abs/2012.07583[Machine learning][Data analytics][Design and manufacturing][High-entropy material][Exhibiting mechanical][Fatigue]
• https://arxiv.org/abs/2012.08653[Optimization][Quantum-dot qubit fabrication][Machine learning]
• https://arxiv.org/abs/2012.11841[Residual matrix product state][Machine learning]
• https://arxiv.org/abs/2012.12244[Machine learning dielectric screening][Simulation][Excited state][Molecule][Material]
• https://arxiv.org/abs/2012.15222[Global optimization][Atomistic structure][Enhanced][[Machine learning]
• https://arxiv.org/abs/2101.00269[Extending Shannon's ionic radii database][Machine learning]
• https://arxiv.org/abs/2101.01942[Machine-learning free-energy functional][Density profile][Simulation]
• https://arxiv.org/abs/2011.10719[Predicting impurity spectral function][Machine learning]
• https://arxiv.org/abs/2101.02810[Machine-learning-guided prediction model][Critical temperature][Cuprate]
• https://arxiv.org/abs/2101.04383[Interpretable discovery][New semiconductor][Machine learning]
• https://arxiv.org/abs/2101.06307[Machine-learning][Accelerated geometry optimization][Molecular simulation]
• https://arxiv.org/abs/2101.06385[Mixed precision][Fermi-operator expansion][Tensor Core][Machine learning perspective]
• https://arxiv.org/abs/2502.09250[Quest][New materials][Network theory][Machine learning perspective]
• https://arxiv.org/abs/2101.07460[High temperature oxidation behavior][Disordered (Ti_0.5Zr_0.5)₂AlC MAX phase][Machine learning-augmented DFT Approach]
• https://arxiv.org/abs/2101.09432[Machine learning][Dynamics of quantum kicked rotor]
• https://arxiv.org/abs/2102.00173[Determination][Dzyaloshinskii-Moriya interaction][Pattern recognition][Machine learning]
• https://arxiv.org/abs/2102.03024[Machine learning][Neutron and X-ray scattering]
• https://arxiv.org/abs/2102.09714[Machine learning][Mirror skin effect][Presence of disorder]
• https://arxiv.org/abs/2103.00193[High-Tc ternary metal hydride][YKH₁₂][LaKH₁₂][Discovered][Machine learning]
• https://arxiv.org/abs/1912.12923[Bayesian tensor network][Optimization algorithm][Probabilistic machine learning]
• https://arxiv.org/abs/2410.06422[Predicting battery capacity fade][Probabilistic machine learning model][With and without pre-trained priors]
• https://arxiv.org/abs/2304.05949[CMOS][Stochastic nanomagnet][Heterogeneous computer][Probabilistic inference and learning]
• https://arxiv.org/abs/1904.03160[Discrete Fourier transform][Prediction][Molecule][Quantum machine learning]
• https://arxiv.org/abs/1907.03741[Expressive power][Tensor-network factorization][Probabilistic modeling][Hidden Markov model][Quantum machine learning]
• https://arxiv.org/abs/2002.12925[Predicting excited states][Ground state wavefunction][Supervised quantum machine learning]
• https://arxiv.org/abs/2004.12076[Quantum machine learning][Quantum biomimetics][Perspective]
• https://arxiv.org/abs/2103.02037[Implementation][Quantum machine learning][Electronic structure calculation][Periodic system][Quantum computing device]
• https://arxiv.org/abs/2111.05076[Application][Quantum machine learning][Quantum correlated system][Quantum convolutional neural network][Classifier][Many-body wavefunction][Quantum variational eigensolver]
• https://arxiv.org/abs/2203.15525[Quantum machine learning correct][Classical force field][Stretching DNA base pairs][Explicit solvent]
• https://arxiv.org/abs/2405.18989[Classification analysis][Transition-metal chalcogenides and oxides][Quantum machine learning]
• https://arxiv.org/abs/2409.07405[Uncovering quantum many-body scars][Quantum machine learning]
• https://arxiv.org/abs/2409.16346[Scalable quantum dynamics compilation][Quantum machine learning]
• https://arxiv.org/abs/2103.04169[Free energy][Machine-learning-enhanced ab initio calculation][(Co_xMn_1-x)₃O₄ mixed phase][Phys. Rev. Materials 5, 035402, 2021]
• https://arxiv.org/abs/2103.05510[Machine-learning semi-local density functional theory][Many-body lattice model][Zero and finite temperature]
• https://arxiv.org/abs/2501.16893[Machine-learning semi-local exchange-correlation functionals][Kohn-Sham density functional theory][Hubbard model]
• https://arxiv.org/abs/2103.07236[Machine learning quantum criticality][Spin-1/2 quantum antiferromagnet][Square lattice][Plaquette structure]
• https://arxiv.org/abs/2103.07513[Predicting][Phase preference][Two-dimensional transition metal dichalcogenide][Machine learning]
• https://arxiv.org/abs/2103.12662[Comprehensive quality investigation][Wire-feed additive manufacturing][Machine learning][Experimental data]
• https://arxiv.org/abs/2204.04198[Modern application][Machine learning][Quantum science]
• https://arxiv.org/abs/2406.03121[MESS][Modern electronic structure simulation][Machine learning]
• https://arxiv.org/abs/2309.01160[Oxygen vacancy formation energy][Metal oxide][High throughput computational studies][Machine learning prediction]
• https://arxiv.org/abs/1910.01183[High-throughput DFPT][Machine learning prediction][Infrared][Piezoelectric][Dielectric Response]
• https://arxiv.org/abs/1801.08217[Electronic branched Flow][Random potential][Machine learning prediction]
• https://arxiv.org/abs/1809.01753[Automatic convergence][Machine learning prediction][Monkhorst-Pack k-points][Plane-wave cut-off][DFT]
• https://arxiv.org/abs/1812.11642[Machine learning prediction][DNA charge transport]
• https://arxiv.org/abs/1906.03233[Machine learning prediction][Accurate atomization energy][Organic molecule][Low-fidelity quantum chemical calculation]
• https://arxiv.org/abs/2012.15739[Uncertainty bound][Multivariate machine learning prediction][High-strain brittle fracture]
• https://arxiv.org/abs/2103.12543[Machine learning prediction][Magnetic][Fe-based metallic glass][Local structure]
• https://arxiv.org/abs/2108.08766[Molecular simulation-derived feature][Machine learning prediction][Metal glass forming ability]
• https://arxiv.org/abs/2109.13762[Machine learning prediction][Superalloy microstructure]
• https://arxiv.org/abs/2204.05967[Machine learning prediction][Local electronic properties][Disordered correlated electron system]
• https://arxiv.org/abs/2210.13597[Critical examination][Robustness][Generalizability][Machine learning prediction][Materials properties]
• https://arxiv.org/abs/2304.01146[Distance-based analysis][Machine learning prediction reliability][Dataset][Materials science]
• https://arxiv.org/abs/2304.02218[Identification][High-reliability region][Machine learning prediction][Materials science][Transparent conducting oxides and perovskites as examples]
• https://arxiv.org/abs/2305.15390[Machine learning prediction][Critical cooling rate][Metallic glasses][Expanded Dataset][Elemental feature][Chemistry of Materials, 34(7), 2945-2954 (2022)]
• https://arxiv.org/abs/2307.06879[Machine learning prediction][High-Curie-Temperature Material]
• https://arxiv.org/abs/2312.02475[Accurate machine learning prediction][Coercivity][High-performance permanent magnet]
• https://arxiv.org/abs/2401.10998[Leveraging domain adaptation][Accurate machine learning prediction][New halide Perovskites]
• https://arxiv.org/abs/2310.04667[Machine learning prediction][Self-assembly][Analysis][Molecular structure dependence][Critical packing parameter]
• https://arxiv.org/abs/2404.13858[Machine learning prediction model][Solid electrolytes][Lattice dynamics properties]
• https://arxiv.org/abs/2502.11762[Layer-resolved quantum transport][Twisted bilayer graphene][Counterflow][Machine learning prediction]
• https://arxiv.org/abs/2103.12066[Machine learning][In situ quality estimation][Molten pool condition-quality relation][Modeling][Experimental data]
• https://arxiv.org/abs/2103.15998[Revealing][Chemical bonding][Adatoms array][Machine learning][3D scanning tunneling spectroscopy data]
• https://arxiv.org/abs/2104.00586[Machine learning enabled prediction][Cathode material][Zn ion Battery]
• https://arxiv.org/abs/2104.05786[Understanding fission gas bubble distribution][Lanthanide transportation][Thermal conductivity degradation][Neutron-irradiated alpha-U][Machine learning]
• https://arxiv.org/abs/2104.08318[Li_xCoO₂ phase stability][Machine learning-enabled scale bridging][Electronic structure][Statistical mechanics][Phase field theory]
• https://arxiv.org/abs/2104.10888[Machine learning aided materials design platform][Predicting][Mechanical property][Na-ion solid-state electrolyte]
• https://arxiv.org/abs/2104.12921[Machine learning][Phase transition][Nonlinear polariton lattice]
• https://arxiv.org/abs/2105.05697[Systematic coarse-graining][Epoxy resin][Machine learning-informed energy renormalization]
• https://arxiv.org/abs/2503.14092[Machine learning-integrated modeling][Thermal properties][Relaxation dynamics][Metallic glasses]
• https://arxiv.org/abs/2105.07303[Composition based crystal materials symmetry prediction][Machine learning][Enhanced descriptor]
• https://arxiv.org/abs/2105.08221[Arrested phase separation][Double-exchange model][Machine-learning][Large-scale simulation][Phys. Rev. Lett. 127, 146401(2021)]
• https://arxiv.org/abs/2105.09729[Adoption][Image-driven machine learning][Microstructure characterization][Materials Design][Perspective]
• https://arxiv.org/abs/2105.09947[Ground-state][Machine learning][Quantum constraint]
• https://arxiv.org/abs/2105.11319[Machine learning][Feature engineering][Crystal structure][Application][Prediction][Formation energy][Cubic compound]
• https://arxiv.org/abs/2105.12943[Phase transition][Two-dimensional ferroelectric][Paraelectric][Ga₂O₃ monolayer][Density functional theory][Machine-learning Study]
• https://arxiv.org/abs/2501.17815[Machine-learning study][Phase transition][Ising][Blume-Capel][Ising-metamagnet model]
• https://arxiv.org/abs/2105.12867[Application][DFTB][Machine learning][Evaluate the stability][Biomass intermediate][Rh(111) surface]
• https://arxiv.org/abs/2105.14806[Aluminium alloy design][Discovery][Machine learning]
• https://arxiv.org/abs/2106.04229[BIGDML][Exact machine learning][Force field]
• https://arxiv.org/abs/2106.04162[Glue function][Cuprate][Optical spectra][Machine learning]
• https://arxiv.org/abs/2106.05235[Machine learning][Superconducting critical temperature][Eliashberg theory]
• https://arxiv.org/abs/2106.07749[Machine learning][Composition-property relationship][Chalcogenide glass]
• https://arxiv.org/abs/2106.10951[Discovering equation][Govern experimental materials stability][Environmental stress][Scientific machine learning]
• https://arxiv.org/abs/2212.10283[Interpretable model][Extrapolation][Scientific machine learning]
• https://arxiv.org/abs/2303.12638[Scientific machine learning framework][Understand flash graphene synthesis]
• https://arxiv.org/abs/2410.13141[Federated scientific machine learning][Approximating function][Solving differential equation][Data heterogeneity]
• https://arxiv.org/abs/2501.18708[Combining physics-based][Data-driven model][Advancing][frontier][Research][Scientific machine learning]
• https://arxiv.org/abs/2501.18637[Machine learning][Microstructure--property relationship][Robust feature][Foundational vision transformer]
• https://arxiv.org/abs/2106.13109[Machine learning][Tame divergent density functional approximation][New path][Consensus materials design principles]
• https://arxiv.org/abs/2106.13327[Machine learning][Data mining][Leverage community knowledge][Engineering][Stable metal-organic framework]
• https://arxiv.org/abs/2106.16075[Machine learning][Derivative discontinuity][Density-functional theory]
• https://arxiv.org/abs/2106.16152[Machine learning][S-Wave scattering][Phase shift][Bypassing][Radial schrödinger equation]
• https://arxiv.org/abs/2107.00493[Graph-based machine learning][Beyond stable material][Relaxed crystal structure]
• https://arxiv.org/abs/2503.07378[Materials map][Integrating experimental and computational data][Graph-based machine learning][Enhanced materials discovery]
• https://arxiv.org/abs/2107.01055[Atomic structure optimization][Machine-learning][Interpolation][Chemical element][Phys. Rev. Lett. 127, 166001 (2021)]
• https://arxiv.org/abs/2107.01040[Machine learning][Microscopic ingredient][Graphene oxide/cellulose interaction]
• https://arxiv.org/abs/2107.03735[Lattice thermal conductivity][Half-Heusler][DFT][Machine learning][Enhancing predictivity][Active sampling][Principal component analysis]
• https://arxiv.org/abs/1808.02470[Machine learning-assisted discovery][Many new solid Li-ion conducting materials]
• https://arxiv.org/abs/2107.02613[Machine learning-assisted][High-throughput][Semi-empirical search][OFET][Molecular material]
• https://arxiv.org/abs/2109.02794[Machine learning-assisted exploration][Thermally conductive polymer][High-throughput MD]
• https://arxiv.org/abs/2201.11168[Machine learning-assisted design][Material properties]
• https://arxiv.org/abs/2305.15410[Machine learning-assisted close-set X-ray diffraction phase identification][Transition metal]
• https://arxiv.org/abs/2208.03410[Machine learning-assisted manipulation][Readout][Molecular spin qubit]
• https://arxiv.org/abs/2309.16415[Interpreting X-ray absorption spectra][Vanadyl phthalocyanine][Spin qubit candidate][Machine learning-assisted approach]
• https://arxiv.org/abs/2407.01145[Machine learning-assisted 3D printing][Thermoelectric material][Ultrahigh performance][Room temperature]
• https://arxiv.org/abs/2409.00360[Accelerating phonon thermal conductivity prediction][Order of magnitude][Machine learning-assisted extraction][Anharmonic force constant]
• https://arxiv.org/abs/2411.00134[Machine learning-assisted profiling][Ladder polymer structure][Scattering]
• https://arxiv.org/abs/2107.07028[Machine learning][Materials discovery][Two-dimensional topological insulator]
• https://arxiv.org/abs/2107.08919[Data cluster analysis][Machine learning][Classification][Twisted bilayer graphene]
• https://arxiv.org/abs/2107.10387[Design][Graphical user interface][Few-shot machine learning classification][Electron microscopy data]
• https://arxiv.org/abs/2404.00155[Machine learning classification][Local environments][Molecular crystal]
• https://arxiv.org/abs/2107.10215[Comprehensive study][Lithium adsorption][diffusion][Janus Mo/WXY (X, Y= S,Se, Te)][Machine learning]
• https://arxiv.org/abs/2107.12975[Cross-architecture tuning][Silicon and SiGe-based quantum devices][Machine learning]
• https://arxiv.org/abs/2107.13960[Data-driven dynamical mean-field theory][Error-correction approach][Solve][Quantum many-body problem][Machine learning]
• https://arxiv.org/abs/2209.14328[Scalably learning][Quantum many-body Hamiltonian][Dynamical data]
• https://arxiv.org/abs/2107.14664[Distributed representation][Atoms and Materials][Machine learning]
• https://arxiv.org/abs/2107.14362[MLMOD Package][Machine learning][Data-driven modeling][LAMMPS]
• https://arxiv.org/abs/2107.14280[Deciphering cryptic behavior][Bimetallic transition metal complex][Machine learning]
• https://arxiv.org/abs/2108.03523[KLIFF][Framework][Develop analytic][Machine learning][Interatomic potential]
• https://arxiv.org/abs/2108.04809[Spiderweb nanomechanical resonator][Bayesian optimization][Nature and guided][Machine learning]
• https://arxiv.org/abs/2108.04904[Machine learning][Consistent thermodynamic model][Automatic differentiation]
• https://arxiv.org/abs/2108.05823[Machine-learning detection][Berezinskii-Kosterlitz-Thouless transition][q-state clock model][Phys. Rev. B 104, 075114 (2021)]
• Masahito Mochizuki and Yusuke Miyajima, J. Phys. Soc. Jpn. 94, 031003 (2025)[Machine-learning detection][Berezinskii-Kosterlitz-Thouless transition]
• https://arxiv.org/abs/2304.08871[Machine-learning detection][Berezinskii-Kosterlitz-Thouless transition][Second-order phase transition][XXZ model]
• https://arxiv.org/abs/2502.09214[Machine-learning detection][Berezinskii-Kosterlitz-Thouless Transition]
• https://arxiv.org/abs/2502.08017[Grain boundary segregation spectra][Generalized machine-learning potential]
• https://arxiv.org/abs/1804.02150[Automatic selection][Atomic fingerprint][Reference configuration][Machine-learning potential]
• https://arxiv.org/abs/2110.00624[Ultra-fast][Interpretable machine-learning potential]
• https://arxiv.org/abs/1810.10820[Impact of local lattice relaxations][Phase stability][Chemical ordering][bcc NbMoTaW][High-entropy alloy][Machine-learning potential]
• https://arxiv.org/abs/2008.07786[Training machine-learning potential][Crystal structure prediction][Disordered structure]
• https://arxiv.org/abs/2101.10468[Construction of machine-learning potential][Accurate and efficient atomic-scale simulation]
• https://arxiv.org/abs/2105.00414[Predicting highly correlated hydride-ion diffusion][SrTiO₃ crystal][Fragment kinetic Monte Carlo method][Machine-learning potential]
• https://arxiv.org/abs/2107.05620[Machine-learning potential][Enable predictive][tractable][High-throughput screening][Random alloy]
• https://arxiv.org/abs/2108.06232[Generalizable machine-learning potential][Ag-Au nanoalloy][Surface reconstruction][Segregation][Diffusion]
• https://arxiv.org/abs/2207.09010[Short-range order][Its impact][BCC NbMoTaW multi-principal element alloy][Machine-learning potential]
• https://arxiv.org/abs/2206.13727[Persistent homology-based descriptor][Machine-learning potential]
• https://arxiv.org/abs/2206.05068[Boosting current-induced molecular dynamics][Machine-learning potential]
• https://arxiv.org/abs/2412.18633[BoostMD][Accelerating molecular sampling][Leveraging ML force field feature][Previous time-step]
• https://arxiv.org/abs/2202.13773[Spectral-neighbour representation][Vector field][Machine-learning potential][Including spin]
• https://arxiv.org/abs/2208.00804[Efficient atomistic simulation][Radiation damage][W and W-Mo][Machine-learning potential]
• https://arxiv.org/abs/2209.00316[Evolution][Boroxol ring][Lithium borosilicate glasses][Machine-learning potential]
• https://arxiv.org/abs/2212.10385[High-accuracy thermodynamic properties][Melting point][Ab initio calculation][Machine-learning potential]
• https://arxiv.org/abs/2208.02974[Origin of negative thermal expansion][Pressure induced amorphization][Zirconium tungstate][Machine-learning potential]
• https://arxiv.org/abs/2404.17353[Large-scale atomistic study][Plasticity][Amorphous gallium oxide][Machine-learning potential]
• https://arxiv.org/abs/2309.00996[Machine-learning potential][Nanoscale simulation][Deformation][Fracture][Example of TiB₂ ceramic]
• https://arxiv.org/abs/2303.14434[Heat flux][Semi-local machine-learning potential]
• https://arxiv.org/abs/2406.04948[Origin of the yield stress anomaly][L12 intermetallics][Physically-informed machine-learning potential]
• https://arxiv.org/abs/2408.05782[Accurate prediction][Structural and mechanical properties][Amorphous material][Machine-learning potential][Case study of silicon nitride]
• https://arxiv.org/abs/2409.08886[PiNNAcLe][Adaptive learn-on-the-fly algorithm][Machine-learning potential]
• https://arxiv.org/abs/2503.16018[Machine-learning potential][Structurally and chemically complex MAB phases][Strain hardening][Ripplocation-mediated plasticity]
• https://arxiv.org/abs/2503.18171[Predicting mode-I/II fracture toughness][Crack growth][Diboride ceramics][Machine-learning potential]
• https://arxiv.org/abs/2310.13756[Learning interatomic potential][Multiple scale]
• https://arxiv.org/abs/1706.10229[Achieving DFT accuracy][Machine-learning interatomic potential][Thermomechanics][Defects][Bcc ferromagnetic iron]
• https://arxiv.org/abs/1908.07330[Machine-learning interatomic potential][Radiation damage][Defect][Tungsten]
• https://arxiv.org/abs/2006.06794[Machine-learning interatomic potential][Enable first-principles multiscale modeling][Lattice thermal conductivity][Graphene/Borophene heterostructure]
• https://arxiv.org/abs/2009.03662[Accelerating first-principles estimation][Thermal conductivity][Machine-learning interatomic potential][MTP/ShengBTE solution]
• https://arxiv.org/abs/2102.06163[Machine-learning interatomic potential][Materials science]
• https://arxiv.org/abs/2203.01117[Machine-learning interatomic potential][Molecular dynamics simulation][Ferroelectric KNbO₃ perovskite]
• https://arxiv.org/abs/2110.10434[Exploring thermal expansion][Carbon-based nanosheet][Machine-learning interatomic potential]
• https://arxiv.org/abs/2212.03096[Complex Ga₂O₃ polymorph][Explored][Accurate][General-purpose machine-learning interatomic potential]
• https://arxiv.org/abs/2009.06533[Development][General-purpose machine-learning interatomic potential][Physically-informed neural network method]
• https://arxiv.org/abs/2302.08698[Complex strengthening mechanism][Nanocrystalline Ni-Mo alloy][Machine-learning interatomic potential]
• https://arxiv.org/abs/2407.04126[Atomistic modeling][Bulk and grain boundary diffusion][Solid electrolyte][Li₆PS₅Cl][Machine-learning interatomic potential]
• https://arxiv.org/abs/2407.10361[Information-entropy-driven generation][Material-agnostic dataset][Machine-learning interatomic potential]
• https://arxiv.org/abs/2408.06322[Discovering high-entropy oxide][Machine-learning interatomic potential]
• https://arxiv.org/abs/2409.06982[Comparison of intermediate-range order][GeO₂ glass][Molecular dynamics][Machine-learning interatomic potential][Reverse Monte Carlo fitting][Experimental data]
• https://arxiv.org/abs/2409.13390[Hydrogen under pressure][Benchmark][Machine-learning interatomic potential]
• https://arxiv.org/abs/2411.01282[Efficient moment tensor machine-learning interatomic potential][Accurate description][Defect][Ni-Al alloy]
• https://arxiv.org/abs/2412.19162[Accelerating global search][Adsorbate molecule position][Machine-learning interatomic potential][Active learning]
• https://arxiv.org/abs/2403.05729[Systematic assessment][Various universal machine-learning interatomic potential]
• https://arxiv.org/abs/2501.05211[Application][Pretrained universal machine-learning interatomic potential][Physicochemical simulation][Liquid electrolyte][Li-ion battery]
• https://arxiv.org/abs/2503.11797[Machine-learning interatomic potential][Twisted hexagonal boron nitride][Accurate structural relaxation][Emergent polarization]
• https://arxiv.org/abs/2405.20270[Bridging electronic and classical density-functional theory][Universal machine-learned functional approximation]
• https://arxiv.org/abs/1802.07605[Accelerating crystal structure prediction][Machine-learning][Interatomic potential][Active learning]
• https://arxiv.org/abs/2108.10601[Non-classical nucleation][Zinc oxide][Physically-motivated machine-learning approach]
• https://arxiv.org/abs/2108.12945[Machine learning][Predicting thermal transport properties of Solids]
• https://arxiv.org/abs/2306.02091[Sub-micrometer phonon mean free path][Metal-organic framework][Revealed][Machine-learning molecular dynamics simulation]
• https://arxiv.org/abs/2109.01501[Order-N orbital-free density-functional calculation][Machine learning][Functional derivative][Semiconductor][Metal]
• https://arxiv.org/abs/2109.08005[Elucidating proximity magnetism][Polarized neutron reflectometry][Machine learning]
• https://arxiv.org/abs/2109.09394[Prediction][Metal alloy][Machine learning]
• https://arxiv.org/abs/2110.00517[Prediction][Carbon nanostructure][Mechanical][Defect][Machine learning]
• https://arxiv.org/abs/2110.00997[Deep dive][Machine learning DFT][Materials Science and Chemistry]
• https://arxiv.org/abs/2110.04115[Machine-learning][Determination][Stacking order][Bilayer graphene]
• https://arxiv.org/abs/2110.03923[Machine learning][Accelerate halide perovskite commercialization][Scale-Up]
• https://arxiv.org/abs/2110.04529[Design][Quaternary eutectic solder][Machine learning]
• https://arxiv.org/abs/2110.06818[Assessing][Accuracy][Machine learning thermodynamic perturbation theory][DFT and Beyond]
• https://arxiv.org/abs/2110.08284[Machine learning][Assisted][GaAsN circular polarimeter]
• https://arxiv.org/abs/2110.10564[Multiscale materials modelling][Machine learning][Hydrogen-steel interaction][Deformation]
• https://arxiv.org/abs/2110.15241[Photonic kernel machine learning][Ultrafast spectral analysis]
• https://arxiv.org/abs/2110.15817[Integration][Machine learning][Neutron scattering][Hamiltonian tuning][Spin ice][Pressure]
• https://arxiv.org/abs/2206.09944[Integrating machine learning][Mechanistic model][Predicting][Yield strength][High entropy alloy]
• https://arxiv.org/abs/2111.01037[Interpretable][Explainable][Machine learning][Materials Science][Chemistry]
• https://arxiv.org/abs/2111.01111[Predicting][Hard-coating alloy][Ab-initio][Machine learning method]
• https://arxiv.org/abs/2111.00851[Quantum computing enhanced machine learning][Physico-chemical application]
• https://arxiv.org/abs/2111.01905[Audacity][huge][Overcoming challenge][Data scarcity][Data quality][Machine learning][Computational materials discovery]
• https://arxiv.org/abs/2111.02206[Machine-learning correction][Density-functional crystal structure optimization]
• https://arxiv.org/abs/2111.06496[Uncovering material deformation][Machine learning][Combined][Four-dimensional scanning transmission electron microscopy]
• https://arxiv.org/abs/2111.07362[Machine learning][Strain-rate-dependent predictability][Discrete dislocation plasticity]
• https://arxiv.org/abs/2111.09368[Machine learning][Compositional disorder][Comparison][Different descriptors]
• https://arxiv.org/abs/2111.11285[Bridging][Reality gap][Quantum device][Physics-aware machine learning]
• https://arxiv.org/abs/2409.12877[Physics aware machine learning][Micromagnetic energy minimization][Recent algorithmic development]
• https://arxiv.org/abs/2111.11262[Synergistic coupling][Ab initio-machine learning simulation][Dislocation]
• https://arxiv.org/abs/2405.05092[Understanding solid nitrogen][Machine learning simulation]
• https://arxiv.org/abs/2412.07753[Structural, electronic, and Li-ion adsorption properties][PolyPyGY explored][First-principles][Machine learning simulation][New multi-ringed 2D carbon allotrope]
• https://arxiv.org/abs/2111.12547[Modified divide-and-conquer based machine learning method][Predicting][Creep life][Superalloy]
• https://arxiv.org/abs/2111.12923[Discovering superhard B-N-O compound][Iterative machine learning][Evolutionary structure prediction]
• https://arxiv.org/abs/2111.15177[Machine learning study][Flat-Band state][Constructed][Molecular-orbital representation][Randomness][J. Phys. Soc. Jpn. 91, 044703 (2022)]
• https://arxiv.org/abs/2112.02169[UV-visible absorption spectra][Solvated molecule][Quantum chemical machine learning]
• https://arxiv.org/abs/2112.09612[Inorganic synthesis][Reaction condition prediction][Generative machine learning]
• https://arxiv.org/abs/2112.09764[Reflection][Future of machine learning][Materials research]
• https://arxiv.org/abs/2112.10789[Machine learning discovery][New phases][Programmable quantum simulator snapshot]
• https://arxiv.org/abs/2112.11099[High pressure hydrogen][Machine learning][Quantum Monte Carlo]
• https://arxiv.org/abs/2112.12124[Machine learning nonequilibrium electron force][Adiabatic spin dynamics]
• https://arxiv.org/abs/2012.07578[Thermal conductivity][h-BN monolayer][Machine learning interatomic potential]
• https://arxiv.org/abs/2201.08906[Machine learning interatomic potential][High throughput screening][Optimization][High-entropy alloy]
• https://arxiv.org/abs/1901.02118[Group-theoretical high-order rotational invariant][Structural representation][Application][Linearized machine learning interatomic potential]
• https://arxiv.org/abs/2301.08630[Evaluating approach][On-the-fly machine learning interatomic potential][Activated mechanisms sampling][Activation-relaxation technique nouveau]
• https://arxiv.org/abs/2005.04913[Exploring phononic][Two-dimensional material][Machine learning interatomic potential]
• https://arxiv.org/abs/2006.01915[Sensitivity][Dimensionality][Atomic environment representation][Machine learning interatomic potential]
• https://arxiv.org/abs/2308.15653[Statistical method][Resolving poor uncertainty quantification][Machine learning interatomic potential]
• https://arxiv.org/abs/2307.13710[Robust training][Machine learning interatomic potential][Dimensionality reduction][Stratified sampling]
• https://arxiv.org/abs/2306.11639[Discrepancies][Error evaluation metrics][Machine learning interatomic potential]
• https://arxiv.org/abs/2212.00263[Lattice thermal conductivity][Elastic modulus][XN4 (X=Be, Mg and Pt) 2D materials][Machine learning interatomic potential]
• https://arxiv.org/abs/2209.12322[Classical][Machine learning interatomic potential][BCC vanadium]
• https://arxiv.org/abs/2208.14420[Intercalation chemistry][Disordered rockSalt Li₃V₂O₅ anode][Cluster expansion][Machine learning interatomic potential]
• https://arxiv.org/abs/2208.10082[Machine learning interatomic potential][Anisotropic thermal transport][Bulk hexagonal boron nitride]
• https://arxiv.org/abs/2205.01209[Machine learning interatomic potential][Simulation][Carbon][Extreme condition]
• https://arxiv.org/abs/2112.14533[Hessian-based assessment][Atomic force][Training machine learning interatomic potential]
• https://arxiv.org/abs/2309.16239[Beam induced heating][Electron microscopy][Machine learning interatomic potential]
• https://arxiv.org/abs/2312.11708[Accelerating][Prediction][Inorganic surface][Machine learning interatomic potential]
• https://arxiv.org/abs/2401.02284[Enhancing][Quality][Reliability][Machine learning interatomic potential][Better reporting practice]
• https://arxiv.org/abs/2402.05222[Validation workflow][Machine learning interatomic potential][Complex ceramics]
• https://arxiv.org/abs/2402.07472[Cartesian atomic cluster expansion][Machine learning interatomic potential]
• https://arxiv.org/abs/2402.18891[Benchmarking phonon anharmonicity][Machine learning interatomic potential]
• https://arxiv.org/abs/2403.01980[Chemical transferability][Accuracy][Ionic liquid simulation][Machine learning interatomic potential]
• https://arxiv.org/abs/2403.10154[Structure-property relation][Silicon oxycarbide][Machine learning interatomic potential]
• https://arxiv.org/abs/2403.18122[Adaptive loss weighting][Machine learning interatomic potential]
• https://arxiv.org/abs/2404.10746[Interpolation and differentiation][Alchemical degrees of freedom][Machine learning interatomic potential]
• https://arxiv.org/abs/2404.18393[Machine learning interatomic potential][Keras API]
• https://arxiv.org/abs/2405.14960[Ni-Cr complexation][FLiBe molten salt][Machine learning interatomic potential][J. Mol. Liq. 400, 124521 (2024)]
• https://arxiv.org/abs/2406.10915[Self-consistent Coulomb interaction][Machine learning interatomic potential]
• https://arxiv.org/abs/2407.00133[Calculation of crystal defects][CaWO₄][100 eV displacement cascade][Linear machine learning interatomic potential]
• https://arxiv.org/abs/2407.15404[Accurate estimation][Interfacial thermal conductance][Silicon and diamond][Machine learning interatomic potential]
• https://arxiv.org/abs/2409.07947[Data-efficient multi-fidelity training][High-fidelity machine learning interatomic potential]
• https://arxiv.org/abs/2409.08194[Unifying][Description][Hydrocarbon][Hydrogenated carbon material][Chemically reactive machine learning interatomic potential]
• https://arxiv.org/abs/2409.17869[Best practice][Fitting machine learning interatomic potential][Molten salt][NaCl-MgCl₂]
• https://arxiv.org/abs/2410.20398[Evaluation][Uncertainty estimation][Gaussian process regression][Machine learning interatomic potential]
• https://arxiv.org/abs/2411.12096[Cartesian atomic moment][Machine learning interatomic potential]
• https://arxiv.org/abs/2411.14608[Machine learning interatomic potential][Modeling uranium mononitride]
• https://arxiv.org/abs/2412.02191[Exploring][Energy landscape][Aluminas][Machine learning interatomic potential]
• https://arxiv.org/abs/2412.06270[Machine learning interatomic potential][Low-modulus Ti-Nb-Zr alloy][Vicinity][Dynamical instability]
• https://arxiv.org/abs/2501.14039[AtomProNet][Data flow to and from][Machine learning interatomic potential][Materials science]
• https://arxiv.org/abs/2502.02211[Tadah][Swiss army knife][Developing and deployment][Machine learning interatomic potential]
• https://arxiv.org/abs/2502.07293[Global universal scaling][Ultra-small parameterization][Machine learning interatomic potential][Super-linearity]
• https://arxiv.org/abs/2502.15530[Accurate and efficient machine learning interatomic potentials][Finite temperature modeling][Molecular crystal]
• https://arxiv.org/abs/2503.05084[Thermal boundary conductance][Standalone and non-standalone GaN/AlN heterostructures][Predicted][Machine learning interatomic potential]
• https://arxiv.org/abs/2503.09814[Practical guide][Machine learning interatomic potential][Status and future][Current Opinion in Solid State and Materials Science, 35, 101214 (2025)]
• https://arxiv.org/abs/2412.18198[Driving force of atomic ordering][Fe_1¡ÝxPt_x][Investigated][Density functional theory][Machine-learning interatomic potentials Monte Carlo simulation]
• https://arxiv.org/abs/2112.14308[Reconstructing][Exit wave][High-resolution transmission electron microscopy][Machine learning]
• https://arxiv.org/abs/2201.00629[Low-cost sensor][Indoor PV energy harvesting estimation][Machine learning]
• https://arxiv.org/abs/2201.01629[Topological characterization][Dynamic chiral magnetic texture][Machine learning]
• https://arxiv.org/abs/2201.04949[Nanoscale soil-water retention curve][Unsaturated clay][MD][Machine learning]
• https://arxiv.org/abs/2201.04970[Insight][Cation ordering][Double perovskites oxide][Machine learning]
• https://arxiv.org/abs/2201.04933[Machine learning-enhanced][Efficient spectroscopic ellipsometry modeling]
• https://arxiv.org/abs/2407.15573[Machine learning-enhanced design][Lead-free halide perovskite material][Density functional theory]
• https://arxiv.org/abs/2503.04870[Leveraging large language model][Address data scarcity][Machine learning][Application][Graphene synthesis]
• https://arxiv.org/abs/2503.06512[LLM-Feynman][Leveraging large language model][Universal scientific formula][Theory discovery]
• https://arxiv.org/abs/2501.07485[Machine learning-enhanced characterisation][Structured spectral densities][Leveraging][Reaction coordinate mapping]
• https://arxiv.org/abs/2304.07592[Leveraging composition-based material descriptor][Machine learning optimization]
• https://arxiv.org/abs/2201.05683[Machine learning technique][Construct detailed phase diagram][Skyrmion system]
• https://arxiv.org/abs/2201.05849[Machine learning][Predict][L-point direct bandgap][Bi_1-xSb_x nanomaterial]
• https://arxiv.org/abs/2201.08091[Predicting][Machine learning][Structural instabilities][2D material]
• https://arxiv.org/abs/2201.07889[Machine learning enhances algorithm][Quantifying non-equilibrium dynamics][Correlation spectroscopy experiment][Frame-rate-limited time resolution]
• https://arxiv.org/abs/2412.08229[Machine learning enhanced collision operator][Lattice Boltzmann method][Invariant network]
• https://arxiv.org/abs/2201.11662[MeltpoolNet][Melt pool characteristic prediction][Metal additive manufacturing][Machine learning]
• https://arxiv.org/abs/2201.11671[Capture agent free biosensing][Porous silicon array][Machine learning]
• https://arxiv.org/abs/2201.12630[Machine learning study][Magnetic ordering][2D material]
• https://arxiv.org/abs/2202.00380[Machine-learning-enhanced quantum sensor][Accurate magnetic field imaging]
• httpsTopogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials://arxiv.org/abs/2202.01042[Machine learning][Exploring small polaron configurational space]
• https://arxiv.org/abs/2401.12042[Machine learning based prediction][Polaron-vacancy pattern][TiO₂(110) surface]
• https://arxiv.org/abs/2409.16179[Machine learning small polaron dynamics]
• https://arxiv.org/abs/2202.01051[Element selection][Functional materials discovery][Integrated machine learning][Atomic contributions]
• https://arxiv.org/abs/1803.03073[Compositional optimization][Hard-magnetic phase][Machine-learning model]
• https://arxiv.org/abs/2007.03839[Machine-learning model][Raman spectra analysis][Twisted bilayer graphene]
• https://arxiv.org/abs/2104.10443[Interpretability][Machine-learning model][Physical science]
• https://arxiv.org/abs/2108.13171[Functional nanomaterials Design][Workflow][Building machine-learning model]
• https://arxiv.org/abs/2109.03751[Scale-invariant machine-learning model][Accelerate][Discovery][Quaternary chalcogenide][Ultralow lattice thermal conductivity]
• https://arxiv.org/abs/2501.06775[Hierarchy-boosted funnel learning][Identifying semiconductor][Ultralow lattice thermal conductivity]
• https://arxiv.org/abs/2112.06551[Accurate computational prediction][Core-electron binding energies][Carbon-based material][Machine-learning model combining DFT and GW]
• https://arxiv.org/abs/2208.06139[Beyond potential][Integrated machine-learning model]
• https://arxiv.org/abs/2202.01449[Predicting tensorial molecular properties][Equivariant machine-learning model]
• https://arxiv.org/abs/2503.17949[Equivariant machine learning interatomic potential][Global charge redistribution]
• https://arxiv.org/abs/2407.01068[Adaptive energy reference][Machine-learning model][Electronic density of states]
• https://arxiv.org/abs/2503.09618[General formalism][Machine-learning model][Multipolar-spherical harmonics]
• https://arxiv.org/abs/2202.01885[Machine-learning convex][Texture-dependent macroscopic yield][Crystal plasticity simulation]
• https://arxiv.org/abs/2202.04137[Machine learning][Heterogeneous porous material]
• https://arxiv.org/abs/2202.05255[Topogivity][Machine-learned chemical rule][Discovering topological materials]
• https://arxiv.org/abs/2202.06199[Panoramic mapping][Phonon transport][Ultrafast electron diffraction][Machine learning]
• https://arxiv.org/abs/2202.06763[Machine learning-aided discovery][Superionic solid-state electrolyte][Li-Ion Batteries]
• https://arxiv.org/abs/2402.16190[Accurate prediction][Keyhole depth][Machine learning-aided simulation]
• https://arxiv.org/abs/2202.07372[Towards machine learning][Microscopic mechanism][Formula search][Crystal structure stability]
• https://arxiv.org/abs/2202.09186[Electronic structure machine learning][Surrogate][without training]
• https://arxiv.org/abs/2202.10125[ABO₃ perovskites' formability prediction][Crystal structure classification][Machine learning]
• https://arxiv.org/abs/2410.10023[Physics-informed AI][ML-based sparse system identification algorithm][Discovery of PDE's][Representing nonlinear dynamic system]
• https://arxiv.org/abs/2503.17060[PINK][Physical-informed machine learning][Lattice thermal conductivity]
• https://arxiv.org/abs/2112.07625[Physics-informed machine learning][Optical mode][Composite]
• https://arxiv.org/abs/2202.10423[Physics-informed machine learning][Uncertainty quantification][Mechanics][Heterogeneous material]
• https://arxiv.org/abs/2203.00484[Experimental identification][Second-order non-Hermitian skin effect][Physics-graph-informed machine learning]
• https://arxiv.org/abs/2212.00667[Physically informed machine-learning algorithm][Identification][Two-dimensional atomic crystal]
• https://arxiv.org/abs/2301.09725[Physics-informed machine learning][Asymptotic homogenization][Elliptic equation]
• https://arxiv.org/abs/2312.15301[Inverting][Kohn-Sham equation][Physics-informed machine learning]
• https://arxiv.org/abs/2402.11126[Kolmogorov n-Widths][Multitask physics-informed machine learning][PIML][[Towards robust metrics]
• https://arxiv.org/abs/2403.10682[Evaluation][GlassNet][Physics-informed machine learning][Glass stability][Glass-forming ability]
• https://arxiv.org/abs/2403.14015[Data-driven modeling][Dislocation mobility][Atomistics][Physics-informed machine learning]
• https://arxiv.org/abs/2410.13228[From PINNs to PIKANs][Recent advance][Physics-informed machine learning]
• https://arxiv.org/abs/2202.10715[Extraction][Interaction parameter][¦Á¡ÝRuCl₃][Neutron data][Machine learning]
• https://arxiv.org/abs/2202.13087[Predicting][Formation][Stability][Oxide perovskite][Extracting underlying mechanism][Machine learning]
• https://arxiv.org/abs/2202.13753[Machine learning-enabled][High-entropy alloy][Discovery]
• https://arxiv.org/abs/2203.02391[Machine-learning][Thermodynamic model][Design][New rare-earth compound]
• https://arxiv.org/abs/2203.03366[Improvement][Gradient descent method][Quantum tensor network machine learning]
• https://arxiv.org/abs/2203.03392[Naturally-meaningful][Efficient descriptor][Machine learning][Material properties][Robust one-shot ab initio descriptor]
• https://arxiv.org/abs/2203.09613[Entangling solid solutions][Machine learning][Tensor network][Materials property prediction]
• https://arxiv.org/abs/2203.10349[Machine learning][Impurity charge-state transition level][Semiconductor][Elemental properties][Multi-fidelity dataset]
• https://arxiv.org/abs/2203.12443[Machine learning aided atomic structure identification][Interfacial ionic hydrate][Atomic force microscopy image]
• https://arxiv.org/abs/2203.14398[Investigation][Bi-particle state][Gate-array-controlled quantum-dot system][Machine learning]
• https://arxiv.org/abs/2204.08151[Machine-learning rationalization][Prediction][Solid-state synthesis condition]
• https://arxiv.org/abs/2204.09820[Machine learning][Optical scanning probe nanoscopy]
• https://arxiv.org/abs/2204.11996[Understanding creep suppression mechanism][Polymer nanocomposite][Machine learning]
• https://arxiv.org/abs/2205.01591[Machine learning][Density functional theory]
• https://arxiv.org/abs/2205.02121[Accelerating phase-field-based simulation][Machine learning]
• https://arxiv.org/abs/2205.04547[Machine learning][Diffusion Monte Carlo energy densities]
• https://arxiv.org/abs/2205.04732[Machine-learned spin-lattice potential][Dynamic simulation][Defective magnetic iron]
• https://arxiv.org/abs/2207.09423[Exploring][Configurational space][Amorphous graphene][Machine-learned atomic energies]
• https://arxiv.org/abs/2205.10046[GPUMD][Package][Constructing accurate machine-learned potential][Performing highly efficient atomistic simulation]
• https://arxiv.org/abs/2209.12948[Developing machine-learned potential][Coarse-grained molecular simulation][Challenges and pitfalls]
• https://arxiv.org/abs/2309.08937[Machine-learned potential energy surface][Free sodium cluster][Density functional accuracy][Applications to melting]
• https://arxiv.org/abs/2310.05279[Combining][D3 dispersion correction][Neuroevolution machine-learned potential]
• https://arxiv.org/abs/2311.01664[Theoretical case study][Generalisation][Machine-learned potential]
• https://arxiv.org/abs/2311.04732[General-purpose machine-learned potential][16 elemental metals][Their alloys]
• https://arxiv.org/abs/2401.16249[Molecular dynamics simulation][Heat transport][Machine-learned potential][Mini review][Tutorial][GPUMD][Neuroevolution potential]
• https://arxiv.org/abs/2404.02626[Polyvalent machine-learned potential][Cobalt][From bulk to nanoparticles]
• https://arxiv.org/abs/2407.15175[Asparagus][Toolkit][Autonomous][User-guided construction][Machine-learned potential energy surface]
• https://arxiv.org/abs/2408.03058[Dual-cutoff machine-learned potential][Condensed organic system][Uncertainty-guided active learning]
• https://arxiv.org/abs/2409.07610[More data hurts][Optimizing data coverage][Mitigating diversity][Induced underfitting][Ultra-fast machine-learned potential]
• https://arxiv.org/abs/2411.02834[Utilizing][Machine-learned potential][Explore enhanced radiation tolerance][MoNbTaVW high-entropy alloy]
• https://arxiv.org/abs/2411.04769[Insight][Effect of force error][Thermal conductivity][Machine-learned potential]
• https://arxiv.org/abs/2411.06804[Predicting ionic conductivity][Solid][Machine-learned potential energy landscape]
• https://arxiv.org/abs/2411.10911[Constructing accurate machine-learned potential][Performing highly efficient atomistic simulation][Predict structural and thermal properties]
• https://arxiv.org/abs/2412.00401[PAL][Parallel active learning][Machine-learned potential]
• https://arxiv.org/abs/2412.01642[Electrostatic interaction][Atomistic][Machine-learned potential][Polar material]
• https://arxiv.org/abs/2409.16039[Machine-learning potential][Phonon transport][AlN with defects][Multiple charge state]
• https://arxiv.org/abs/2407.03448[Accurate nuclear quantum statistics][Machine-learned classical effective potential]
• https://arxiv.org/abs/2106.03369[Modeling refractory high-entropy alloy][Efficient machine-learned interatomic potential][Defect][Segregation]
• https://arxiv.org/abs/1906.07816[Machine-learned interatomic potential][Alloy][Alloy Phase Diagram]
• https://arxiv.org/abs/2109.15002[Thermal transport][Phase transition][Zirconia][On-the-fly machine-learned interatomic potential]
• https://arxiv.org/abs/2104.05853[Large scale structure prediction][Near-stoichiometric magnesium oxide][Machine-learned interatomic potential][Novel crystalline phase][Oxygen-vacancy ordering]
• https://arxiv.org/abs/2203.08458[Simple machine-learned interatomic potential][Complex alloy]
• https://arxiv.org/abs/2211.12484[How to validate][Machine-learned interatomic potential]
• https://arxiv.org/abs/2303.02519[Validation][Machine-learned interatomic potential][Temperature-dependent electron thermal diffuse scattering]
• https://arxiv.org/abs/2304.01650[Constructing][Evaluating][Machine-learned interatomic potential][Li-based disordered rocksalt]
• https://arxiv.org/abs/2304.03109[Unraveling][Crystallization kinetics][Ge₂Sb₂Te₅ phase change compound][Machine-learned interatomic potential]
• https://arxiv.org/abs/2403.15897[Genetic algorithm][Trained machine-learned interatomic potential][Silicon-carbon system]
• https://arxiv.org/abs/2404.15128[Unveiling][Crystallization kinetics][Ge-rich Ge_xTe alloys][Large scale simulation][Machine-learned interatomic potential]
• https://arxiv.org/abs/2406.19462[Strong atomic reconstruction][Twisted bilayer][Highly flexible InSe][Machine-learned interatomic potential][Continuum model approaches]
• https://arxiv.org/abs/2408.15779[Fast and accurate machine-learned interatomic potential][Large-scale simulation][Cu, Al and Ni]
• https://arxiv.org/abs/2409.11808[Accelerating][Training and improving][Reliability][Machine-learned interatomic potential][Strongly anharmonic material][Active learning]
• https://arxiv.org/abs/2503.07344[Radiation damage][Phase stability][Al_xCrCuFeNi_y alloy][Machine-learned interatomic potential]
• https://arxiv.org/abs/2503.15432[Accurate][Transferable][Verifiable][Machine-learned interatomic potential][Layered material]
• https://arxiv.org/abs/2305.02158[Shotgun crystal structure prediction][Machine-learned formation energies]
• https://arxiv.org/abs/2501.02932[Predicting band gap][Chemical composition][Simple learned model][Material property][Atypical statistics]
• https://arxiv.org/abs/2306.15153[Experimental exploration][ErB₂][SHAP analysis][Machine-learned model][Magnetocaloric material][Materials design]
• https://arxiv.org/abs/2401.00072[Machine-learned model][Magnetic material]
• https://arxiv.org/abs/2411.08194[Computationally efficient machine-learned model][GST phase change material][Direct and indirect learning]
• https://arxiv.org/abs/2405.01240[Machine-learned tuning][Artificial Kitaev chain][Tunneling-spectroscopy measurement]
• https://arxiv.org/abs/2407.11450[Machine-learned kinetic energy model][Light weight metals and compounds][Group III-V element]
• https://arxiv.org/abs/2408.12390[Density dependence][Thermal conductivity][Nanoporous and amorphous carbon][Machine-learned molecular dynamics]
• https://arxiv.org/abs/2410.07886[Homogeneous nucleation][Undercooled Al-Ni melt][Machine-learned interaction potential]
• https://arxiv.org/abs/2411.02257[Machine-learned nodal structure][Fermion system]
• https://arxiv.org/abs/2411.09631[NEP-MB-pol][Unified machine-learned framework][Fast and accurate prediction][Water's thermodynamic][Transport properties]
• https://arxiv.org/abs/2501.05903[Discovery][Sustainable energy material][Machine-learned material space]
• https://arxiv.org/abs/2205.05729[Nonlocal machine learning][Micro-structural defect evolution][Crystalline material]
• https://arxiv.org/abs/2205.07925[Machine learning][Relativity-inspired quantum dynamics]
• https://arxiv.org/abs/2008.08793[Explainable machine learning][Materials discovery][Predicting][Potentially formable][Nd-Fe-B crystal structure][Extracting structure-stability relationship]
• https://arxiv.org/abs/2205.10084[Spinel nitride solid solution][Charting properties][Configurational space][Explainable machine learning]
• https://arxiv.org/abs/2303.03748[Computing formation enthalpies][Explainable machine learning method][Lanthanide orthophosphate][Solid solution]
• https://arxiv.org/abs/2308.07823[Explainable machine learning][Hydrogen diffusion][Metal][Random binary alloy]
• https://arxiv.org/abs/2406.04445[Explainable machine learning identification][Superconductivity][Single-particle spectral function]
• https://arxiv.org/abs/2503.04492[Accurate predictive model][Band gap][Selected important features][Explainable machine learning]
• https://arxiv.org/abs/2205.13631[Size-dependent nucleation][Crystal phase transition][Machine learning metadynamics][Phys. Rev. Lett. 129, 185701 (2022)]
• Ling Liu, Yingqi Tian, Xuanye Yang, and Chungen Liu, Phys. Rev. Lett. 131, 158001 (2023)[Mechanistic insight][Water autoionization][Metadynamics simulation][Enhanced][Machine learning].
• https://arxiv.org/abs/2206.01669[Relating plasticity][Dislocation][Data analysis][Scaling vs. machine learning]
• https://arxiv.org/abs/2206.04968[Information][Necessary][Sufficient][Predict materials properties][Machine learning]
• https://arxiv.org/abs/2206.06031[Universal synthetic dataset][Machine learning][Spectroscopic data]
• https://arxiv.org/abs/2206.07605[Quantum-corrected thickness-dependent thermal conductivity][Amorphous silicon][Predicted][Machine-learning molecular dynamics simulation]
• https://arxiv.org/abs/2206.08296[Finite temperature dielectric properties][KTaO₃][Machine learning][Phonon spectra][Barrett law][Strain engineering][Electrostriction]
• https://arxiv.org/abs/2206.08048[Automated analysis][Continuum field][Atomistic simulation][Statistical machine learning]
• https://arxiv.org/abs/2206.08951[Denoising scanning tunneling microscopy image][Machine learning]
• https://arxiv.org/abs/2206.12239[Machine learning][Edge energies][High symmetry Au nanoparticle]
• https://arxiv.org/abs/2206.15370[Development][Exchange-correlation functional][Machine learning]
• https://arxiv.org/abs/2206.15075[Topological data analysis][Machine learning]
• https://arxiv.org/abs/2207.03599[Quantum chemical root][Machine-learning molecular similarity descriptor]
• https://arxiv.org/abs/2207.04994[Uncertainty-aware][Mixed-variable machine learning][Materials design]
• https://arxiv.org/abs/2011.08426[First-principles prediction][Electronic transport][Experimental semiconductor heterostructure][Physics-based machine learning]
• https://arxiv.org/abs/2207.05171[Physics-based machine-learning approach][Modeling][Temperature-dependent yield strength][Medium- or high-entropy alloy]
• https://arxiv.org/abs/2311.11364[Local environment-based machine learning][Molecular adsorption energy prediction]
• https://arxiv.org/abs/2207.06031[Unsupervised recognition][Informative feature][Tensor network machine learning][Quantum entanglement variation]
• https://arxiv.org/abs/2409.12462[Unsupervised reward-driven image segmentation][Automated scanning transmission electron microscopy experiment]
• https://arxiv.org/abs/2207.10144[Statistical mechanics][Material][First principles calculation][Machine learning]
• https://arxiv.org/abs/2207.12118[Machine-learning][Accelerated identification][Exfoliable two-dimensional material]
• https://arxiv.org/abs/2207.12837[Classical and quantum machine learning application][Spintronics]
• https://arxiv.org/abs/2207.14622[Innate dynamics][Identity crisis][Metal surface][Unveiled][Machine learning][Atomic environments]
• https://arxiv.org/abs/2208.02182[Machine learning optimization][Majorana hybrid nanowire][Phys. Rev. Lett. 130, 116202 (2023)]
• https://arxiv.org/abs/2208.04146[Linking properties][Microstructure][Liquid metal embedded elastomers][Machine learning]
• https://arxiv.org/abs/2208.06923[Predicting creep failure][Machine learning][Which features matter?]
• https://arxiv.org/abs/2208.05546[Data-driven machine learning][Predict mechanical properties][Monolayer TMD]
• https://arxiv.org/abs/2208.04976[Machine learning][1- and 2-electron reduced density matrices][Polymeric molecule]
• https://arxiv.org/abs/1909.08565[Construction][Machine learned force field][Quantum chemical accuracy][Application][Chemical insight]
• https://arxiv.org/abs/2206.00540[Piezo- and pyroelectricity][Zirconia][Machine learned force field]
• https://arxiv.org/abs/2407.15290[Shock hugoniot calculation][On-the-fly machine learned force field][Ab initio accuracy]
• https://arxiv.org/abs/2408.07554[Cyclic and helical symmetry-informed machine learned force field][Application][Lattice vibration][Carbon nanotube]
• https://arxiv.org/abs/2409.16051[Tracing ion migration][Halide provskites][Machine learned force field]
• https://arxiv.org/abs/2503.05845[Machine learned force field][Fundamental][Its reach, and challenges]
• https://arxiv.org/abs/2308.01278[Machine learned force-field][Ab-initio quality description][Metal-organic framework]
• https://arxiv.org/abs/2009.04045[Experimentally driven][Automated machine learned][Inter-Atomic Potential][Refractory oxide]
• https://arxiv.org/abs/2502.03126[Robust machine learned interatomic potential][Nb: collision cascade simulation][Accurate defect configurations]
• https://arxiv.org/abs/2101.00049[Particle swarm][Hyper-parameter optimization][Machine learned interatomic potential]
• https://arxiv.org/abs/2203.16055[Benchmarking][Structural evolution method][Training of machine learned interatomic potential]
• https://arxiv.org/abs/2208.09692[Carbon phase diagram][Empirical][Machine learned interatomic potential]
• https://arxiv.org/abs/1910.12052[Classification][Quantification][Crystal defect][Energetic bombardment][Machine learned molecular dynamics simulation]
• https://arxiv.org/abs/2207.11828[Optimal data generation][Machine learned interatomic potential]
• https://arxiv.org/abs/2309.08689[Collinear-spin machine learned interatomic potential][Fe₇Cr₂Ni alloy]
• https://arxiv.org/abs/2409.06242[Investigating ionic diffusivity][Amorphous solid electrolytes][Machine learned interatomic potential]
• https://arxiv.org/abs/2409.07039[Combining Brillouin spectroscopy][Machine learned interatomic potential][Probe mechanical properties][Metal organic framework]
• https://arxiv.org/abs/2502.07104[Uncertainty quantification][Misspecified machine learned interatomic potential]
• https://arxiv.org/abs/2502.19081[Efficient and accurate spatial mixing][Machine learned interatomic potential][Materials science]
• https://arxiv.org/abs/2212.01432[Machine learned interatomic potential][Dispersion strengthened plasma facing component]
• https://arxiv.org/abs/2010.01976[Computational study][Crystal defects formation][Machine learned molecular dynamics simulation]
• https://arxiv.org/abs/2404.00050[Grappa][Machine learned molecular mechanics force field]
• https://arxiv.org/abs/2305.02990[Accelerating GW calculation][Machine learned dielectric matrices]
• https://arxiv.org/abs/2306.00970[Improving][Reliability][Machine learned potential][Modeling inhomogenous liquid]
• https://arxiv.org/abs/2306.02255[Exploring model complexity][Machine learned potential][Simulated properties]
• https://arxiv.org/abs/2309.03840[Generating minimal training set][Machine learned potential][Phys. Rev. Lett. 132, 167301 (2024)]
• https://arxiv.org/abs/2412.02877[Machine learned potential][High-throughput phonon calculation][Metal-organic framework]
• https://arxiv.org/abs/2403.00377[Emergence][Accurate atomic energies][Machine learned noble gas potential]
• https://arxiv.org/abs/2407.09628[Accelerating electron dynamics simulation][Machine learned time propagator]
• https://arxiv.org/abs/2208.11448[nanoNET][Machine learning platform][Predicting nanoparticles distribution][Polymer matrix]
• https://arxiv.org/abs/2005.05618[Machine learning guided discovery][Gigantic magnetocaloric effect][HoB₂][Near hydrogen liquefaction temperature]
• https://arxiv.org/abs/2207.05546[Enhancing thermoelectric properties][Isotope graphene nanoribbon][Machine learning guided manipulation][Disordered antidot][Interface]
• https://arxiv.org/abs/2208.13742[Machine learning guided high-throughput search][Non-oxide garnet]
• https://arxiv.org/abs/2303.09342[Machine learning guided discovery][Superconducting calcium borocarbide]
• https://arxiv.org/abs/2406.12850[Machine learning guided discovery][Stable][Spin-resolved topological insulator]
• https://arxiv.org/abs/2209.00628[Monotonic Gaussian process][Physics-constrained machine learning][Materials science application]
• https://arxiv.org/abs/2209.03173[Machine learning][High-entropy alloy][Progress][Challenge][Opportunities]
• https://arxiv.org/abs/2209.04891[Statistical perspective][Predicting][Strength of metals][Revisiting][Hall-Petch relationship][Machine learning]
• https://arxiv.org/abs/2202.00930[Machine learning based prediction][Electronic structure][Quasi-one-dimensional material][Under strain]
• https://arxiv.org/abs/2209.01358[Machine learning based approach][Solving atomic structure][Nanomaterials combining pair distribution function][Density functional theory]
• https://arxiv.org/abs/2209.07145[Machine learning based modeling][Disordered elemental semiconductor][Understanding][Atomic structure][a-Si][a-C]
• https://arxiv.org/abs/2301.07790[Machine learning based approach][Predict ductile damage model parameter][Polycrystalline metal]
• https://arxiv.org/abs/2407.09514[Machine learning based prediction][Proton conductivity][Metal-organic framework]
• https://arxiv.org/abs/2407.15338[Revealing][Molecular structure][a-Al₂O₃(0001)-water interface][Machine learning based computational vibrational spectroscopy]
• https://arxiv.org/abs/2503.00689[Stress][Strain][Displacement][Novel machine learning based framework][Predict mixed mode I/II fracture toughness]
• https://arxiv.org/abs/2208.05672[Searching][Chromate replacement][Natural language processing][Machine learning algorithm]
• https://arxiv.org/abs/2209.07578[Pixel-wise classification][Graphene-detection][Tree-based machine learning algorithm]
• https://arxiv.org/abs/2409.14370[Machine learning algorithm][Optimization][Magnetocaloric effect][All-d-metal Heusler alloy]
• https://arxiv.org/abs/2503.09517[Materials discovery][Quantum-enhanced machine learning algorithm]
• https://arxiv.org/abs/2209.12605[Mechanical properties prediction][Metal additive manufacturing][Machine learning]
• https://arxiv.org/abs/2209.13949[Application][Machine learning][Mechanical properties][Copper graphene composite]
• https://arxiv.org/abs/2210.02410[Vendi score][Diversity evaluation metric][Machine learning]
• https://arxiv.org/abs/2210.03066[MagNet][Machine learning][Enhanced three-dimensional magnetic reconstruction]
• https://arxiv.org/abs/2210.02666[When not to use machine learning][Perspective][Potential][Limitation]
• https://arxiv.org/abs/2210.07930[Machine learning frontier orbital energies][Nanodiamond]
• https://arxiv.org/abs/2210.09783[Machine-learning-optimized][Perovskite nanoplatelet synthesis]
• https://arxiv.org/abs/2210.10391[Machine learning][Sustainable energy future]
• https://arxiv.org/abs/2210.10700[Basic electro-topological descriptor][Prediction][Organic molecule geometries][Simple machine learning]
• https://arxiv.org/abs/2210.12132[Perspective][Machine learning][Data science][Strongly correlated electron problem]
• https://arxiv.org/abs/2405.02618[Combining machine learning model][First-principles high-throughput calculation][Accelerate][Search][Promising Thermoelectric Materials]
• https://arxiv.org/abs/1709.02727[Machine learning modeling][Superconducting critical temperature]
• https://arxiv.org/abs/1806.01038[Predicting][Thermodynamic stability][Perovskite oxide][Machine learning model]
• https://arxiv.org/abs/1906.02889[Machine learning modeling][High entropy alloy][Short-range order]
• https://arxiv.org/abs/1906.06378[Machine learning model][Lattice thermal conductivity prediction][Inorganic Material]
• https://arxiv.org/abs/2006.06252[Machine learning model][Cluster][Map tribocorrosion regime][Feature space]
• https://arxiv.org/abs/2010.13306[Database][Machine learning Model][Identify thermally driven][Metal-insulator transition compound]
• https://arxiv.org/abs/2102.00191[Importance][Feature engineering][Database selection][Machine learning model][Case study][Carbon crystal structure]
• https://arxiv.org/abs/2107.07997[Uncertainty prediction][Machine learning model]
• https://arxiv.org/abs/2109.08098[MOFSimplify][Machine learning model][Extracted stability data][Three thousand metal-organic frameworks]
• https://arxiv.org/abs/2201.00798[Descriptor][Machine learning model][Generalized force field][Condensed matter system]
• https://arxiv.org/abs/2203.01276[Machine learning model][Predict calculation outcome][Transferability necessary][Computational catalysis]
• https://arxiv.org/abs/2203.01376[Homogeneous ice nucleation][Ab initio machine learning model][Water]
• https://arxiv.org/abs/1908.02398[Assessing and improving machine learning model][Prediction][Polymer glass transition Temperature]
• https://arxiv.org/abs/1908.00926[Accelerating approach][Designing ferromagnetic material][Machine leaning modeling][Magnetic ground state][Curie temperature]
• https://arxiv.org/abs/2205.03708[Description][Collective magnetization process][Machine learning model]
• https://arxiv.org/abs/2210.08878[Hundreds][New][Stable][One-dimensional material][Generative machine learning model]
• https://arxiv.org/abs/2210.13587[Quantifying][Performance][Machine learning model][Materials discovery]
• https://arxiv.org/abs/2210.14191[Database][Ultrastable MOFs][Reassembled][Stable fragment][Machine learning model]
• https://arxiv.org/abs/2407.01485[Machine learning model][Atom-diatom reactions across isotopologues]
• https://arxiv.org/abs/2407.08390[Chemical bond-based machine learning model][Dipole moment][Application][Dielectric properties][Liquid methanol and ethanol]
• https://arxiv.org/abs/2410.14498[Bond-based machine learning model][Molecular polarizabilities][A priori Raman spectra]
• https://arxiv.org/abs/2407.19241[Advancement][Tuning thermoelectric properties][Insight][Hybrid functional studies][Strain engineering][Machine learning model]
• https://arxiv.org/abs/2409.06921[Voltage mining][(De)lithiation-stabilized Cathode][Machine learning model][Li-ion cathode voltage]
• https://arxiv.org/abs/2409.12284[Best of both worlds][Enforcing detailed balance][Machine learning model][Transition rate]
• https://arxiv.org/abs/2410.09659[Many-body expansion][Machine learning model][Octahedral transition metal complexes]
• https://arxiv.org/abs/2410.13873[Robustness][Machine learning model][Predicting thermodynamic properties][Searching][New quasicrystal approximant]
• https://arxiv.org/abs/2410.22718[Transferability][Chemical bond-based machine learning model][Dipole moment][GHz to THz dielectric properties][Liquid propylene glycol][Polypropylene glycol]
• https://arxiv.org/abs/2410.23444[Machine learning model][Different cheminformatics data set][Forecast][Power conversion efficiency][Organic solar cell]
• https://arxiv.org/abs/2501.00003[Machine learning model][Si nanoparticle growth][Nonthermal plasma]
• https://arxiv.org/abs/2502.00363[Machine learning model][Reinforced concrete pipes condition prediction][State-of-the-art][Artificial neural network][Multiple linear regression][Wisconsin case study]
• https://arxiv.org/abs/2210.15070[Noise robust automatic charge state recognition][Quantum dot][Machine learning][Pre-processing][Visual explanation][Grad-CAM]
• https://arxiv.org/abs/2211.00691[Accelerated design][Chalcogenide Glasses][Interpretable machine learning][Composition property relationship]
• https://arxiv.org/abs/2211.01624[Accelerating][Discovery][g-C₃N₄-supported single atom catalysts][Hydrogen evolution reaction][Combined DFT and machine learning strategy]
• https://arxiv.org/abs/2211.01583[Data-based polymer-unit fingerprint (PUFp)][Newly accessible expression][Polymer organic semiconductor][Machine learning]
• https://arxiv.org/abs/2211.03075[Prediction][Superconducting properties][Machine learning model]
• https://arxiv.org/abs/2211.03265[Machine-learning approach][Discovery][Conventional superconductor]
• https://arxiv.org/abs/2211.03223[Cementron][Machine learning][Constituent phase][Cement clinker][Optical image]
• https://arxiv.org/abs/2211.03500[Structure][Solidification][(Fe_0.75B_{0.15Si0.1)100-xTax (x=0-2) melts][Experiment][Machine learning]}
• https://arxiv.org/abs/2211.04504[All rf-based tuning algorithm][Quantum device][Machine learning]
• https://arxiv.org/abs/2211.06490[Non-volatile][All-spin non-binary matrix multiplier][Efficient hardware accelerator][Machine learning]
• https://arxiv.org/abs/2211.08194[Machine learning][Interpreting coherent X-ray speckle pattern]
• https://arxiv.org/abs/2211.09239[Machine learning][Nuclear materials research]
• https://arxiv.org/abs/2211.15415[Machine learning][Screening large organic molecule]
• https://arxiv.org/abs/2211.16486[AdsorbML][Accelerating adsorption energy calculation][Machine learning]
• https://arxiv.org/abs/2212.00634[Discrete element simulation][Machine learning][Improving][Performance][Dry catalyst continuous impregnation process]
• https://arxiv.org/abs/2212.04322[Encrypted machine learning][Molecular quantum properties]
• https://arxiv.org/abs/2212.05213[Fusing machine learning strategy][Density functional theory][Hasten][Discovery][MXene][Hydrogen generation]
• https://arxiv.org/abs/2212.05175[Machine learning reconstruction][Depth-dependent thermal conductivity profile][Frequency-domain thermoreflectance signal]
• https://arxiv.org/abs/2212.06755[Machine learning superalloy microchemistry][Creep strength][Physical descriptor]
• https://arxiv.org/abs/2503.15149[Machine learning surrogate model][Many-body dispersion interaction][Polymer melt]
• https://arxiv.org/abs/2212.06929[Generating extreme quantum scattering][Graphene][Machine learning]
• https://arxiv.org/abs/2405.09057[Response matching][Generating materials and molecules]
• https://arxiv.org/abs/2410.18262[Hamiltonian matching][Symplectic neural integrator]
• https://arxiv.org/abs/2212.10478[Machine learning][Polymer self-consistent field theory][Two spatial dimension]
• https://arxiv.org/abs/2212.11855[Closed-loop machine learning][Discovery][Novel superconductor]
• https://arxiv.org/abs/2301.01959[Application][Machine learning][Sporadic experimental data][Understanding epitaxial strain relaxation]
• https://arxiv.org/abs/2301.03030[Exploring high thermal conductivity polymer][Interpretable machine learning][Physical descriptor]
• https://arxiv.org/abs/2301.03372[Machine-Learning prediction][Computed band gap][Double perovskite materials]
• https://arxiv.org/abs/2301.07906[Machine-learning][Spectral function][Hole][Quantum antiferromagnet]
• https://arxiv.org/abs/2303.16597[Machine-learning surrogate model][Accelerating][Search][Stable ternary alloy]
• https://arxiv.org/abs/2304.05905[Machine-learning recognition][Dzyaloshinskii-Moriya Interaction][Magnetometry]
• https://arxiv.org/abs/1903.10216[Road to accuracy][Machine-learning-accelerated silicon][Ab initio simulation]
• https://arxiv.org/abs/2205.02967[Putting density functional theory][Test][Machine-learning-accelerated materials discovery]
• https://arxiv.org/abs/2305.07251[Machine-learning-accelerated simulation][Heuristic-free surface reconstruction]
• https://arxiv.org/abs/2312.03598[Machine-learning-accelerated][Quantum transport study][Effects of superlattice disorder][Strain][Mid-wave infrared curved sensor]
• https://arxiv.org/abs/2412.08126[Machine-learning-accelerated surface exploration][Reconstructed BiVO₄(010)][Characterization][Aqueous interface]
• https://arxiv.org/abs/2412.15452[Mechanistic insight][Oxygen evolution reaction][Nickel-doped barium titanate][Machine learning-accelerated simulation]
• https://arxiv.org/abs/2112.08676[Machine learning-accelerated computational solid mechanics][Application][Linear elasticity]
• https://arxiv.org/abs/2403.18298[Deciphering chemical ordering][High entropy material][Machine learning-accelerated high-throughput cluster expansion approach]
• https://arxiv.org/abs/1911.03307[Pushing the limits of atomistic simulations][Ultra-high temperature][Machine-learning force field][ZrB₂]
• https://arxiv.org/abs/1912.10761[Complexity][Transferability][Machine-learning force field][Gold-iron interaction]
• https://arxiv.org/abs/2110.00321[Exploring librational pathway][On-the-fly machine-learning force field][Methylammonium molecule][MAPbX₃ (X=I, Br, Cl) perovskite]
• https://arxiv.org/abs/2101.06099[Anharmonic lattice dynamics][Large thermodynamic ensemble][Machine-learning force field][Breakdown][Phonon quasiparticle picture][CsPbBr₃]
• https://arxiv.org/abs/2303.16538[Efficient generation][Stable linear machine-learning force field][Uncertainty-aware active learning]
• https://arxiv.org/abs/2305.10255[First-principles machine-learning force field][Heterogeneous ice nucleation][Microcline feldspar]
• https://arxiv.org/abs/2407.13507[Strength][2D glasses][Machine-learning force field]
• https://arxiv.org/abs/2411.19764[Chirality-dependent kinetics][Single-walled carbon nanotube][Machine-learning force field]
• https://arxiv.org/abs/2502.19772[Machine-learning force field][Reveal shallow electronic states][Dynamic halide perovskite surface]
• https://arxiv.org/abs/2305.12060[Mechanical property design][Bio-compatible Mg alloy][Machine-learning algorithm]
• https://arxiv.org/abs/1905.02142[Optimizing many-body atomic descriptor][Enhanced computational performance][Machine-learning-based interatomic potential]
• https://arxiv.org/abs/2008.07120[Machine-learning-based sampling method][Exploring local energy minima][Interstitial species in a crystal]
• https://arxiv.org/abs/2010.12467[Machine-learning-based prediction][Lattice thermal conductivity][Half-Heusler compound][Atomic information]
• https://arxiv.org/abs/2105.08940[Thermo-mechanical][Nitrogenated holey graphene (C2N)][Comparison][Machine-learning-based][Classical interatomic potential]
• https://arxiv.org/abs/2110.02827[Colmena][Scalable machine-learning-based steering][Ensemble simulation][High performance computing]
• https://arxiv.org/abs/2411.19617[Materials learning algorithms][MALA][Scalable machine learning][Electronic structure calculation][Large-scale atomistic simulation]
• https://arxiv.org/abs/2111.15593[Machine-learning-based exchange-correlation functional][Physical asymptotic constraint]
• https://arxiv.org/abs/2112.02587[Machine-learning-based intelligent framework][Discovering refractory high-entropy alloy][Improved high-temperature yield strength]
• https://arxiv.org/abs/2304.13932[Yield strength-plasticity][Trade-off][Uncertainty quantification][Machine-learning-based Design][Refractory high-entropy alloy]
• https://arxiv.org/abs/2310.15591[Machine-learning-based non-local kinetic energy density functional][Simple metals and alloys]
• https://arxiv.org/abs/2306.11899[Closing the loop][Autonomous experiment][Enabled][Machine-learning-based online data analysis][Synchrotron beamline environment]
• https://arxiv.org/abs/2408.12627[Machine-learning-based construction][Molecular potential and its application][Exploring the deep-lying-orbital effect][HighOrder harmonic generation]
• https://arxiv.org/abs/1812.01025[Accelerating photovoltaic materials development][High-throughput Experiment][Machine-learning-assisted diagnosis]
• https://arxiv.org/abs/1908.00739[Machine-learning-assisted thin-film growth][Bayesian optimization][Molecular beam epitaxy][SrRuO₃ thin films]
• https://arxiv.org/abs/2210.00579[Large-scale machine-learning-assisted exploration][Whole materials space]
• https://arxiv.org/abs/2306.12898[Machine-learning-assisted][Real-time-feedback-controlled growth][InAs/GaAs quantum dot]
• https://arxiv.org/abs/2308.15907[Machine-learning-assisted construction][Ternary convex hull diagram]
• https://arxiv.org/abs/2310.14205[Machine-learning-assisted analysis][Transition metal dichalcogenide][Thin-film growth]
• https://arxiv.org/abs/2308.11867[Atomic scale understanding][Initial Cu-Ni oxidation][Machine-learning accelerated first-principles simulation][In situ TEM experiment]
• https://arxiv.org/abs/2310.09062[Mechanisms of temperature-dependent thermal transport][Amorphous silica][Machine-learning molecular dynamics]
• https://arxiv.org/abs/2310.15314[Combining linear-scaling quantum transport][Machine-learning molecular dynamics][Thermal and electronic transports][Complex material]
• https://arxiv.org/abs/2311.00633[Ab initio machine-learning unveils strong anharmonicity][Non-Arrhenius self-diffusion][Tungsten]
• https://arxiv.org/abs/2311.01099[Dissimilar thermal transport properties][kappa-Ga₂O₃][beta-Ga₂O₃][Machine-learning homogeneous nonequilibrium molecular dynamics simulation]
• https://arxiv.org/abs/2401.12127[Machine-learning structural reconstruction][Accelerated point defect calculation]
• https://arxiv.org/abs/2401.11244[Accurate description][Ion migration][Solid-state ion conductor][Machine-learning molecular dynamics]
• https://arxiv.org/abs/2406.15956[Decoupling many-body interaction][CeO₂ (111) oxygen vacancy structure][Insights][Machine-learning][Cluster expansion]
• https://arxiv.org/abs/2311.11305[Machine-larning-based interatomic potential][Group IIB][VIA Semiconductor][Comparative study][Universal and independent model]
• https://arxiv.org/abs/2311.11990[Machine-learned atomic cluster expansion potential][Fast and quantum-accurate thermal simulation][Wurtzite AlN]
• https://arxiv.org/abs/2405.06465[Quantum-accurate machine learning potential][Metal-organic framework][Temperature driven active learning]
• https://arxiv.org/abs/2312.01662[Universal deoxidation][Semiconductor substrate][Machine-learning][Real-time-feedback-control]
• https://arxiv.org/abs/2301.08073[Glass hardness][Predicting composition][Load effect][Symbolic reasoning-informed machine learning]
• https://arxiv.org/abs/2301.07743[CycleGAN][Generate realistic STEM image][Machine learning]
• https://arxiv.org/abs/2301.08813[Representation][Material][Machine learning]
• https://arxiv.org/abs/2301.10474[Machine learning][Structural representation][Discovery][High temperature superconductor]
• https://arxiv.org/abs/2301.12639[Machine learning][Robust prediction][Thermal boundary conductance][2D substrate interface]
• https://arxiv.org/abs/2301.13550[Linear Jacobi-Legendre expansion][Charge density][Machine learning-accelerated electronic structure calculation]
• https://arxiv.org/abs/2311.01491[Investigating][Behavior of diffusion model][Accelerating electronic structure calculation]
• https://arxiv.org/abs/2301.13435[Exciton diffusion][Amorphous organic semiconductor][Reducing simulation overhead][Machine learning]
• https://arxiv.org/abs/2302.03362[Machine learning benchmark][Classification][Equivalent circuit model][Solid-state electrochemical impedance spectra]
• https://arxiv.org/abs/2302.03146[Combining][Synchrotron X-ray diffraction][Mechanistic modeling][Machine Learning][In situ subsurface temperature quantification][Additive manufacturing]
• https://arxiv.org/abs/2302.03321[Machine-learning accelerated annealing][Fitting-search style][Multi-alloy structure prediction]
• https://arxiv.org/abs/2412.13838[Machine-learning accelerated descriptor design][Catalyst discovery][CO₂][Methanol conversion case study]
• https://arxiv.org/abs/2503.12591[Revealing nanostructure][High-entropy alloy][Machine-learning accelerated scalable Monte Carlo simulation]
• https://arxiv.org/abs/2302.09542[Dynamics of growing carbon nanotube interface][Machine learning-enabled molecular simulation]
• https://arxiv.org/abs/2406.16211[Machine-learning-enabled fast optical identification][Characterization][2D material]
• https://arxiv.org/abs/2311.15549[From prediction to action][Critical role][Proper performance estimation][Machine-learning-driven materials discovery]
• https://arxiv.org/abs/1803.02802[Realistic atomistic structure][Amorphous silicon][Machine-learning-driven][Molecular dynamics]
• https://arxiv.org/abs/2006.09760[Machine-learning-driven simulated deposition][Carbon film][Low-density][Diamond-like amorphous carbon]
• https://arxiv.org/abs/2302.09337[Revealing intrinsic vortex-core state][Fe-based superconductor][Machine-learning-driven discovery]
• https://arxiv.org/abs/2401.14039[Threshold displacement energy map][Frenkel pair generation][Ga₂O₃][Machine-learning-driven molecular dynamics simulation]
• https://arxiv.org/abs/2409.06071[Constructing multicomponent cluster expansion][Machine-learning][Chemical embedding]
• https://arxiv.org/abs/2411.11122[Topological representation][Layered hybrid lead halide][Machine-learning][Universal cluster]
• https://arxiv.org/abs/2302.09242[Deciphering alloy composition][Superconducting single-layer FeSe_1-xS_x][SrTiO₃(001) substrate][Machine learning][STM/S data]
• https://arxiv.org/abs/2302.12274[Machine learning microscopic form][Nematic order][Twisted double-bilayer graphene]
• https://arxiv.org/abs/2302.13329[Classification][Magnetic order][Electronic structure][Machine learning]
• https://arxiv.org/abs/2302.13745[Predicting elastic and plastic properties][Small iron polycrystal][Machine learning]
• https://arxiv.org/abs/2303.01886[Machine learning][Magnetic stochastic synapses]
• https://arxiv.org/abs/2303.02247[Machine learning][Twin/matrix interface][Local stress field]
• https://arxiv.org/abs/2303.03493[Machine learning][Phase ordering dynamics][Charge density wave]
• https://arxiv.org/abs/2303.04318[Machine learning model][Capture plasmon dynamics][Ag nanoparticle]
• https://arxiv.org/abs/2303.07441[Linking stability][Molecular geometries][Perovskite][Lanthanide][Richness][Machine learning method]
• https://arxiv.org/abs/2303.07647[Recent advances and applications][Machine learning][Experimental solid mechanics][Review]
• https://arxiv.org/abs/2303.10092[Learning from 2D][Machine learning of 3D effective properties][Heterogeneous material][2D microstructure section]
• https://arxiv.org/abs/2303.09814[Sub-10 nm probing][Ferroelectricity][Heterogeneous material][Machine learning][Contact Kelvin probe force microscopy]
• https://arxiv.org/abs/2303.12486[Machine learning-informed structuro-elastoplasticity][Predict][Ductility][Disordered solid]
• https://arxiv.org/abs/2303.13433[Machine learning-enabled tomographic imaging][Chemical short-range atomic ordering]
• https://arxiv.org/abs/2303.15307[PiNNwall][Heterogeneous electrode model][Integrating machine learning][Atomistic simulation]
• https://arxiv.org/abs/2304.05502[Machine learning][Structure-property relationship][Scalability][Limitation]
• https://arxiv.org/abs/2304.06800[Predicting][Fracture propensity][Amorphous silica][Molecular dynamics simulation][Machine learning]
• https://arxiv.org/abs/2303.11244[Machine learning][Phase and size-controlled synthesis][Iron oxides]
• https://arxiv.org/abs/2410.08912[Machine-learning framework][Accelerating spin-lattice relaxation simulation]
• https://arxiv.org/abs/1812.0505[Graph networks][Universal machine learning framework][Molecule][Crystal]
• https://arxiv.org/abs/2401.17587[Semi-supervised machine learning framework][Predicting hydrogen storage capacities][Metal hydride]
• https://arxiv.org/abs/1904.08750[Property-aimed embedding][Machine learning framework][Material discovery]
• https://arxiv.org/abs/2003.12465[Interactive human-machine learning framework][Modelling of ferroelectric-dielectric Composites]
• https://arxiv.org/abs/2411.08063[MatPilot][LLM-enabled AI Materials Scientist][Framework][Human-machine collaboration]
• https://arxiv.org/abs/2101.06474[Optimized and autonomous machine learning framework][Characterizing][Pore][Particle][Grain][Grain boundary][Microstructural image]
• https://arxiv.org/abs/2211.12459[Generalized machine learning framework][Brittle crack problem][Transfer learning][Graph neural network]
• https://arxiv.org/abs/2304.08761[Machine learning framework][Quantifying chemical segregation][Microstructural feature][Atom probe tomography data]
• https://arxiv.org/abs/2404.06499[Machine learning framework][Prediction][Grain boundary segregation][Chemically complex environments]
• https://arxiv.org/abs/2304.11497[Machine learning method][Characterize][Crack length][Position][High-density polyethylene][Ultrasound]
• https://arxiv.org/abs/2304.11226[Probabilistic selection][Design][Concrete][Machine learning][Data-Centric Engineering, 4, e9. (2023)]
• https://arxiv.org/abs/2304.12123[Machine learning modeling][Atomic structure][Physical properties][Alkali and alkaline-earth aluminosilicate glasses and melts]
• https://arxiv.org/abs/2304.11828[Machine learning][Predicting fatigue properties][Additively manufactured material]
• https://arxiv.org/abs/2305.00938[Accelerating microstructure modelling][Machine learning][New method][Combining autoencoder and ConvLSTM]
• https://arxiv.org/abs/2305.00229[Accelerated][Inexpensive][Machine learning][Manufacturing processes][Incomplete mechanistic knowledge]
• https://arxiv.org/abs/2305.03666[Machine learning][Accelerated bandgap prediction][Strain-engineered quaternary III-V semiconductor]
• https://arxiv.org/abs/2305.03698[Scratching depth][Ceramic-metal ratio][Scratch behavior][NbC/Nb ceramic/metal nano-laminate][Molecular dynamics simulation][Machine learning]
• https://arxiv.org/abs/2305.02236[High-sensitivity extreme-ultraviolet transient absorption spectroscopy][Enabled][Machine learning]
• https://arxiv.org/abs/2305.06925[Accurate surface and finite temperature bulk properties][Lithium metal][Large scale][Machine learning interaction potential]
• https://arxiv.org/abs/2403.13952[Consideration][ML interaction potential][Free energy calculation]
• https://arxiv.org/abs/2305.08038[First principles][Machine learning][Identify key pairing strength factor][Cuprate superconductor]
• https://arxiv.org/abs/2305.07942[Build a ruler][Measure electron localization function][Machine learning model][S-Au bonds quantification][Thiolate][Protected gold nanocluster]
• https://arxiv.org/abs/2305.11825[Machine learning moment tensor potential][Modelling dislocation][Fracture][L₁₀-TiAl and D₀₁₉-Ti₃Al Alloys]
• https://arxiv.org/abs/2305.12977[Machine learning][Relationship][Debye and superconducting transition temperatures]
• https://arxiv.org/abs/2305.14764[Detection][Non-uniformity][Parameter][Magnetic domain pattern generation][Machine learning][J. Phys. Soc. Jpn. 93, 054706 (2024)]
• https://arxiv.org/abs/2305.16230[Topological gap protocol][Machine learning optimization][Majorana hybrid wire]
• https://arxiv.org/abs/2305.19440[Machine learning][Tree tensor network][CP rank constraint][Tensor dropout]
• https://arxiv.org/abs/2305.19930[Atom-by-atom design][Metal oxide catalyst][Oxygen evolution reaction][Machine learning]
• https://arxiv.org/abs/2306.01402[Machine learning wave function][Identify fractal phase]
• https://arxiv.org/abs/2306.02015[Machine learning][Experimental design][Parameter estimation][Ultrafast spin dynamics]
• https://arxiv.org/abs/2306.03143[Machine learning feature discovery][Spinon Fermi surface]
• https://arxiv.org/abs/2409.08054[Predicting and accelerating nanomaterials synthesis][Machine learning featurization]
• https://arxiv.org/abs/2306.04426[Machine learning][Universal empirical pseudopotential]
• https://arxiv.org/abs/2503.02404[Electronic structure][Crystalline and amorphous GeSe and GeSbTe compounds][Machine learning empirical pseudopotential]
• https://arxiv.org/abs/2306.06032[Machine learning][Electronic structure][Matter across temperature]
• https://arxiv.org/abs/2306.07953[Unraveling magnetic anisotropy energy][Ferromagnetic monolayer][Ferroelectric ABO₃][DFT][Machine learning]
• http://arXiv.org/abs/1512.00623[Machine learning-based selective sampling procedure][Identifying the low energy region][Potential energy surface][Proton conduction]
• https://arxiv.org/abs/2102.02470[Machine learning-based generalized model][Finite element analysis][Roll deflection][Austenitic stainless steel 316L][Strip rolling]
• https://arxiv.org/abs/2103.13495[Machine learning-based automatic graphene detection][Color correction][Optical microscope image]
• https://arxiv.org/abs/2104.08103[Accurate prediction][Bonding][Machine learning-based model][Isolated state]
• https://arxiv.org/abs/2201.01932[Machine learning-based classification approach][Phase diagram prediction]
• https://arxiv.org/abs/2201.11188[Crystal structure prediction][Machine learning-based element substitution]
• https://arxiv.org/abs/2209.12946[Investigation][Machine learning-based coarse-grained mapping scheme][Organic molecule]
• https://arxiv.org/abs/2207.00456[Machine learning-based mass density model][Hard magnetic 14:2:1 phase][Chemical composition-based feature]
• https://arxiv.org/abs/2208.02141[Machine learning-based classification][Interpretation][Prediction][High-entropy-alloy][Intermetallic phase]
• https://arxiv.org/abs/2211.00090[Machine learning-based sampling][Virtual experiment][Full stress state][Identify parameters][Anisotropic yield model]
• https://arxiv.org/abs/2306.08387[Machine learning-based prediction][Elastic properties][Amorphous metal alloy][Physica A: Statistical Mechanics and its Applications 617, 128678 (2023)]
• https://arxiv.org/abs/2309.00305[Efficient surrogate model][Materials science simulation][Machine learning-based prediction][Microstructure properties]
• https://arxiv.org/abs/2311.15423[Machine learning-based estimation][Explainable artificial intelligence-supported interpretation][Critical temperature][Magnetic ab initio Heusler alloys data]
• https://arxiv.org/abs/2403.15372[Machine learning-based compression][Quantum many body physics][PCA and autoencoder representation][Vertex function]
• https://arxiv.org/abs/2406.17457[Additively manufacturable high-strength aluminum alloy][Thermally stable microstructure][Hybrid machine learning-based design]
• https://arxiv.org/abs/2408.04055[Machine learning-based reward-driven tuning][Scanning probe microscopy][Fully automated microscopy]
• https://arxiv.org/abs/2408.12181[Machine learning-based determination][Magnetic parameter][Magnetic image][Different imaging scale]
• https://arxiv.org/abs/2503.05813[Machine learning-based regional cooling demand prediction][Optimised dataset partitioning]
• https://arxiv.org/abs/2503.11796[Physicochemical characterization][New 2D semiconductor carbon allotrope][C16][Investigation][Density functional theory][Machine learning-based molecular dynamics]
• https://arxiv.org/abs/2502.12147[Learning smooth][Expressive interatomic potential][Physical property prediction]
• https://arxiv.org/abs/2306.08595[TensorKrowch][Smooth integration][Tensor network][Machine learning]
• https://arxiv.org/abs/2306.11054[Machine learning renormalization group][Statistical physics]
• https://arxiv.org/abs/2306.10223[Machine learning search][Stable binary Sn alloy][Na, Ca, Cu, Pd, and Ag]
• https://arxiv.org/abs/2306.11978[Designing Pr-based advanced photoluminescent materials][Machine learning][Density functional theory]
• https://arxiv.org/abs/2306.14285[Transferable][Robust][Machine learning model][Predicting stability][Si anode][Multivalent cation batteries]
• https://arxiv.org/abs/2306.14155[Discovering two-dimensional magnetic topological insulator][Machine learning]
• https://arxiv.org/abs/2306.14845[Open-source robust machine learning platform][Real-time detection][Classification][2D material flake]
• https://arxiv.org/abs/2310.07049[Robust machine learning inference][X-ray absorption][Near edge spectra][Featurization]
• https://arxiv.org/abs/2307.03310[Finding][Dynamics][Integrable quantum many-body system][Machine learning]
• https://arxiv.org/abs/2307.04351[MD-HIT][Machine learning][Materials property prediction][Dataset redundancy control]
• https://arxiv.org/abs/2307.05378[M²Hub][Unlocking][Potential][Machine learning][Materials discovery]
• https://arxiv.org/abs/2307.05911[Grain][Grain boundary][Segmentation][Machine learning][Real and generated dataset]
• https://arxiv.org/abs/2307.06209[Structural][Electronic][Thermal][Mchanical properties][C60-based fullerene][Two-dimensional network][First-principles][Machine learning]
• https://arxiv.org/abs/2307.06384[Machine learning accelerated discovery][Corrosion-resistant][High-entropy alloy]
• https://arxiv.org/abs/2407.11208[Machine learning accelerated prediction][Ce-based ternary compound][Antagonistic pair]
• https://arxiv.org/abs/2307.06556[Metal oxide-based gas sensor array][VOCs analysis][Complex Mixtures][Machine learning]
• https://arxiv.org/abs/2307.11068[Machine learning][Majorana nanowire][Disorder landscape][Phys. Rev. Lett. 132, 206602 (2024)]
• https://arxiv.org/abs/2307.10935[Inorganic synthesis-structure map][Zeolite][Machine learning][Crystallographic distance]
• https://arxiv.org/abs/2307.10578[Delta machine learning][Predicting dielectric properties][Raman spectra]
• https://arxiv.org/abs/2210.17484[Open MatSci ML toolkit][Flexible framework][Machine learning][Materials science]
• https://arxiv.org/abs/2310.07864[Towards foundation models][Materials science][Open MatSci ML toolkit]
• https://arxiv.org/abs/2309.05934[MatSciML][Broad][Multi-task benchmark][Solid-state materials modeling]
• https://arxiv.org/abs/2307.14032[Advances of machine learning][Materials science][Ideas and Techniques]
• https://arxiv.org/abs/2307.14627[Preparation][Entangled many-body state][Machine learning]
• https://arxiv.org/abs/2307.15663[CoRe optimizer][All-in-one solution][Machine learning]
• https://arxiv.org/abs/2307.16052[Unveiling exotic magnetic phase][Fibonacci quasicrystalline stacking][Ferromagnetic Layer][Machine learning]
• https://arxiv.org/abs/2308.00665[Machine learning density functional][Random-phase approximation]
• https://arxiv.org/abs/2308.00490[Discovery][Stable hybrid organic-inorganic double perovskites][High-performance solar cell][Machine-learning algorithm][Crystal graph convolution neural network method]
• https://arxiv.org/abs/2308.01443[Evaluation][Optical constant][Oxide thin film][Machine learning]
• https://arxiv.org/abs/2308.01993[Accelerated organic crystal structure prediction][Genetic algorithm][Machine learning]
• https://arxiv.org/abs/2308.01932[Investigation][Machine learning][Estimating][Critical temperature][Superconductor]
• https://arxiv.org/abs/2202.13554[Machine learning method][Material property prediction][Example polymer compatibility]
• https://arxiv.org/abs/2308.02937[Improving realistic material property prediction][Domain adaptation][Machine learning]
• https://arxiv.org/abs/2308.04937[Machine learning][Unveil][Multiple Pauli blockade][Transport spectroscopy][Bilayer graphene double-quantum dot]
• https://arxiv.org/abs/2308.08226[Accelerated design][Block copolymer][Unbiased exploration strategy][Fusion][Molecular dynamics simulation][Machine learning]
• https://arxiv.org/abs/2409.09691[Extrapolative ML Model][Copolymer]
• https://arxiv.org/abs/2308.07649[Primitive machine learning tool][Mechanical property prediction][Multiple principal element alloy]
• https://arxiv.org/abs/2308.14920[Matbench discovery][Evaluation framework][Machine learning crystal stability prediction]
• https://arxiv.org/abs/2308.16868[Understanding defects][Amorphous silicon][Million-atom simulation][Machine learning]
• https://arxiv.org/abs/2309.02362[Prediction][Uncertainty estimate][Reactor pressure vessel steel embrittlement][Machine learning]
• https://arxiv.org/abs/2309.02573[Hydrogen-induced degradation dynamics][Silicon heterojunction solar cell][Machine learning]
• https://arxiv.org/abs/2309.03587[Modelling atomic and nanoscale structure][Silicon-oxygen system][Active machine learning]
• https://arxiv.org/abs/2411.07367[Modeling extensive defect][Metal][Active machine learning][Automated configuration reconstruction]
• https://arxiv.org/abs/2309.03482[Machine learning][Kinetic energy densities][Target and feature averaging][Better result][Fewer training data]
• https://arxiv.org/abs/2309.04140[How close][Classical two-body potential][Ab initio calculation][Insight][Linear machine learning][Force matching]
• https://arxiv.org/abs/2309.04692[Accelerating discovery][Vacancy ordered 18-valence electron half-Heusler compound][Synergistic approach][Machine learning][Density functional theory]
• https://arxiv.org/abs/2312.17596[Accurate machine learning interatomic potential][FCC and HCP nickel]
• https://arxiv.org/abs/1610.02098[Machine learning force field][Construction][Validation][Outlook]
• https://arxiv.org/abs/1807.02042[Construction][Accurate machine learning force field][Copper and silicon dioxide]
• https://arxiv.org/abs/1904.12961[On-the-fly machine learning force field generation][Application][Melting point]
• https://arxiv.org/abs/2105.02525[Machine learning force field][Local parametrization][Dispersion interaction][Application to the phase diagram of C₆₀]
• https://arxiv.org/abs/2311.11362[Symmetry-invariant][Quantum machine learning force field]
• https://arxiv.org/abs/2403.11705[Coarsening][Chiral domain][Itinerant electron magnet][Machine learning force field approach]
• https://arxiv.org/abs/2403.20138[Na vacancy driven phase transformation][Fast ion conduction][W-doped Na₃SbS₄][Machine learning force field]
• https://arxiv.org/abs/2406.17595[Density-based long-range electrostatic descriptor][Machine learning force field]
• https://arxiv.org/abs/2405.13635[Machine learning force field][Thermal oxidation of silicon]
• https://arxiv.org/abs/2409.01931[Design space][Molecular mechanics][Machine learning force field]
• https://arxiv.org/abs/2412.00340[Theoretical studies][Sodium storage mechanism][Hard carbon anode][Sodium-ion batteries][Molecular simulation][Machine learning force field]
• https://arxiv.org/abs/2412.16741[Point defect formation][Finite temperature][Machine learning force field]
• https://arxiv.org/abs/2412.19333[DPmoire][Tool][Constructing accurate machine learning force field][Moire system]
• https://arxiv.org/abs/2501.09009[Towards fast][Specialized machine learning force field][Distilling foundation model][Energy Hessian]
• https://arxiv.org/abs/2502.20809[PFD][Automatically generating machine learning force field][Universal model]
• https://arxiv.org/abs/2503.08674[Understanding and mitigating distribution shifts][Machine learning force field]
• https://arxiv.org/abs/2502.15190[Decoding lithium's subtle phase stability][Mchine learning force field]
• https://arxiv.org/abs/2501.06171[Machine learning force-field approach][Itinerant electron magnet]
• https://arxiv.org/abs/2309.05325[Superfolded configuration][Low thermal conductivity][Two-dimensional carbon allotrope][Machine learning force constant potential]
• https://arxiv.org/abs/2311.16964[Machine learning force-field model][Metallic spin glass]
• https://arxiv.org/abs/2411.19780[Machine learning force-field model][Kinetic Monte Carlo simulation][Itinerant Ising magnet]
• https://arxiv.org/abs/2309.04478[Multimodal machine learning][Materials science][Composition-structure bimodal learning][Experimentally measured properties]
• https://arxiv.org/abs/2502.07400[Explainable multimodal machine learning][Revealing structure-property relationship][Carbon nanotube fiber]
• https://arxiv.org/abs/2309.06122[Robust synthetic data generation framework][Machine learning][High-resolution transmission electron microscopy][HRTEM]
• https://arxiv.org/abs/2309.15679[Classification][Skyrmionic texture][Extraction][Hamiltonian parameter][Machine learning]
• https://arxiv.org/abs/2309.15127[Grad DFT][Software library][Machine learning][Enhanced density functional theory]
• https://arxiv.org/abs/2309.15868[Mechanical properties][Single and polycrystalline solids][Machine learning]
• https://arxiv.org/abs/2310.03378[Machine learning][Interaction network][Coupled dynamical system]
• https://arxiv.org/abs/2310.03267[Machine learning detection][Correlation][Snapshot][Ultracold atom][Optical lattice]
• https://arxiv.org/abs/2310.04021[Multi-principal element alloy discovery][Directed energy deposition][Machine learning]
• https://arxiv.org/abs/2409.15391[Supply risk-aware][Alloy discovery and design]
• https://arxiv.org/abs/2310.05018[Human-in-the-loop][Machine learning][Automated electron microscopy]
• https://arxiv.org/abs/2310.06598[Kohn-Sham accuracy][Orbital-free density functional theory][Delta-machine learning]
• https://arxiv.org/abs/2310.07089[Machine learning method][Background potential estimation][2DEG]
• https://arxiv.org/abs/2310.07604[Surface segregation][High-entropy alloy][Alchemical machine learning]
• https://arxiv.org/abs/2310.07816[Quantitative analysis][MoS₂ thin film micrograph][Machine learning]
• https://arxiv.org/abs/2310.09911[Machine learning many-body Green's function][Molecular excitation spectra]
• https://arxiv.org/abs/2003.04922[Machine-learning assisted cross-domain prediction][Ionic conductivity][Sodium and lithium-based superionic conductors]
• https://arxiv.org/abs/2310.08378[When the atoms dance][Exploring mechanism][Electron-beam][Modification][Machine-learning assisted high temporal resolution electron microscopy]
• https://arxiv.org/abs/2401.05106[Exploring][Formation][Gold/silver nanoalloy][Gas-phase synthesis][Machine-learning assisted simulation]
• https://arxiv.org/abs/2411.00332[In-situ self-optimization][Quantum dot emission][Laser][Machine-learning assisted epitaxy]
• https://arxiv.org/abs/2110.13308[Calibrating DFT formation enthalpy calculation][Multi-fidelity machine learning]
• https://arxiv.org/abs/2310.13153[Discovering novel halide perovskite alloy][Multi-fidelity machine learning][Genetic algorithm]
• https://arxiv.org/abs/2310.18439[Machine learning detecting Majorana zero mode][Zero bias peak measurement]
• https://arxiv.org/abs/2311.01401[Machine learning design][Perovskite catalytic properties]
• https://arxiv.org/abs/2311.00787[Accelerating electronic stopping power prediction][10 million times][Combination][Time-dependent density functional theory][Machine learning]
• https://arxiv.org/abs/2311.03139[End-to-end material thermal conductivity prediction][Machine learning]
• https://arxiv.org/abs/2311.02522[Comparison][Different machine learning][Predict viscosity][Tri-n-butyl phosphate mixtures][Experimental data]
• https://arxiv.org/abs/2311.07253[Machine learning][Kondo entanglement cloud][Local measurement]
• https://arxiv.org/abs/2311.07114[Novel model][Fatigue life prediction][Wideband random load][Machine learning]
• https://arxiv.org/abs/2311.09739[Machine learning][Polaritonic chemistry][Accessing chemical kinetics]
• https://arxiv.org/abs/2311.09508[Atoms as Words][Novel approach][Deciphering material properties][NLP-inspired machine learning][Crystallographic information Files (CIFs)]
• https://arxiv.org/abs/2301.03497[Phase transition][Inorganic halide perovskite][Machine learning potential][Journal of Physical Chemistry C 127, 13773 (2023)]
• https://arxiv.org/abs/1901.03346[Machine learning assisted measurement][Local topological invariant]
• https://arxiv.org/abs/1906.11275[Machine learning assisted insight][Spin Ice][Dy₂Ti₂O₇]
• https://arxiv.org/abs/2210.14701[Machine learning assisted design][Optimization][Transition metal-incorporated carbon quantum dot catalyst][Hydrogen evolution reaction]
• https://arxiv.org/abs/2212.09796[Machine learning assisted derivation][Effective low energy model][Metallic magnet]
• https://arxiv.org/abs/2311.10558[Machine learning assisted characterization][Labyrinthine pattern transition]
• https://arxiv.org/abs/2311.13904[3D microstructure characterization][Cu-25Cr solid state sintered alloy][X-ray computed tomography][Machine learning assisted segmentation]
• https://arxiv.org/abs/2402.09128[Machine learning assisted prediction][Organic salt structure properties]
• https://arxiv.org/abs/2409.09583[Machine learning assisted screening][Metal binary alloy][Anode material]
• https://arxiv.org/abs/2412.15370[Machine learning assisted canonical sampling][MLACS]
• https://arxiv.org/abs/2502.14683[Machine learning assisted tracking][Magnetic object][Quantum diamond magnetometry]
• https://arxiv.org/abs/2311.16351[Microscopic mechanism][Thermal amorphization][ZIF-4][Melting][ZIF-zni][Molecular dynamics][Machine learning techniques]
• https://arxiv.org/abs/2311.17994[Machine learning][Operator content][Critical self-dual Ising-Higgs gauge model]
• https://arxiv.org/abs/2312.01415[Thermally averaged magnetic anisotropy tensor][Machine learning][Gaussian moment]
• https://arxiv.org/abs/2312.03989[Rapid detection][Rare event][In situ X-ray diffraction data][Machine learning]
• https://arxiv.org/abs/2312.06492[Phase transition][LaMnO₃][SrRuO₃][DFT + U][Machine learning force fields simulation][Phys. Rev. B 108, 235122 (2023)]
• https://arxiv.org/abs/2312.07149[Feature-based prediction][Cross-linked epoxy polymer][Molecular dynamics][Machine learning techniques]
• https://arxiv.org/abs/2312.04660[Application][Machine learning technique][Fast forecast][Aggregation kinetics][Space-inhomogeneous system]
• https://arxiv.org/abs/2312.14552[Machine learning][Structure-guided material][Process design]
• https://arxiv.org/abs/2312.14311[Crystal growth characterization][WSe₂ thin film][Machine learning]
• https://arxiv.org/abs/2312.15899[Corrosion-resistant aluminum alloy design][Machine learning][Combined][High-throughput calculation]
• https://arxiv.org/abs/2401.02866[Spin-1/2 kagome Heisenberg antiferromagnet][Machine learning discovery][Spinon pair density wave ground state]
• https://arxiv.org/abs/2501.05294[First-principles][Machine learning insight][Design][DOTT-carbon][Lithium-ion storage capacity]
• https://arxiv.org/abs/2401.03044[Machine learning inspired model][Hall effect][Non-collinear magnet]
• https://arxiv.org/abs/2401.06533[Predicting][One-particle density matrix][Machine learning]
• https://arxiv.org/abs/2310.13310[Polarizability model][Simulation][Finite temperature Raman spectra][Machine learning molecular dynamics]
• https://arxiv.org/abs/2312.06963[Enhanced ionic conductivity][Crystallization][Glass-Li₃PS₄][Machine learning molecular dynamics simulation]
• https://arxiv.org/abs/2401.11427[Correcting force error-induced underestimation][Lattice thermal conductivity][Machine learning molecular dynamics]
• https://arxiv.org/abs/2403.01077[Exploring structural][Electrochemical properties][Li₃TiCl₆][Machine learning molecular dynamics study]
• https://arxiv.org/abs/2404.11442[Structural properties][Amorphous Na₃OCl electrolyte][First-principles][Machine learning molecular dynamics]
• https://arxiv.org/abs/2404.15465[Thermal boundary conductance][Sharp metal-diamond interface][Predicted][Machine learning molecular dynamics]
• https://arxiv.org/abs/2405.04939[Phases transition mechanism][Growth of WS₂ and MoS₂ layers][Ab initio data driving][Machine learning molecular dynamics]
• https://arxiv.org/abs/2411.14732[Constant-potential machine learning molecular dynamics simulation][Reveal potential-regulated Cu cluster formation][MoS₂]
• https://arxiv.org/abs/2502.10756[Understanding magnesium dissolution][Machine learning molecular dynamics]
• https://arxiv.org/abs/2502.17804[Incongruent melting][Phase diagram][SiC][Machine learning molecular dynamics]
• https://arxiv.org/abs/2503.15389[Revealing][Proton slingshot mechanism][Solid acid electrolytes][Machine learning molecular dynamics]
• https://arxiv.org/abs/2401.10908[Machine learning][Knot topology][Non-Hermitian band braid]
• https://arxiv.org/abs/2401.14808[Raman spectra][Amino acid][Peptides][Machine learning polarizabilities]
• https://arxiv.org/abs/2402.00177[Ab initio amorphous materials database][Empowering machine learning][Decode diffusivity]
• https://arxiv.org/abs/1910.04603[Machine learning driven synthesis][Few-layered WTe₂]
• https://arxiv.org/abs/2402.06479[Thermal transport][Glasses][Machine learning driven simulation]
• https://arxiv.org/abs/2503.04027[Machine learning driven search][Hydrogen storage material]
• https://arxiv.org/abs/2402.08884[Machine learning][Density functional theory][Experiment][Understand][Photocatalytic reduction][CO₂ by CuPt/TiO₂]
• https://arxiv.org/abs/2402.10186[Self-consistent validation][Machine learning electronic structure]
• https://arxiv.org/abs/2402.11204[Predicting superconducting transition temperature][Advanced machine learning][Innovative feature engineering]
• https://arxiv.org/abs/2402.11383[Machine learning][Universal harmonic interatomic potential][Predicting phonon][Crystalline solid]
• https://arxiv.org/abs/2402.11701[Explaining][Machine learning solution][Ising model]
• https://arxiv.org/abs/2403.00259[Deciphering diffuse scattering][Machine learning][Equivariant foundation model][Molten FeO]
• https://arxiv.org/abs/2405.15131[Computational toolkit][Predicting thickness][2D material][Machine learning][Autogenerated dataset][Large language model]
• https://arxiv.org/abs/2403.05119[Estimation][Electronic band gap energy][Material properties][Machine learning]
• https://arxiv.org/abs/2403.10769[Machine learning exchange field][Ab-initio spin dynamics]
• https://arxiv.org/abs/2403.13243[Comparative study][Machine learning models predicting energetics][Interacting defect]
• https://arxiv.org/abs/2403.15579[Efficient first principles][Modeling][Machine learning][Simple representation][High entropy material]
• https://arxiv.org/abs/2404.10903[Superior polymeric gas separation membrane][Designed][Explainable graph machine learning]
• https://arxiv.org/abs/2404.10910[Discovering factorization surface][Quantum spin chain][Machine learning]
• https://arxiv.org/abs/2404.13507[Uncovering obscured phonon dynamics][Powder inelastic neutron scattering][Machine learning]
• https://arxiv.org/abs/2404.14107[PGNAA][Spectral classification][Aluminium and copper alloys][Machine learning]
• https://arxiv.org/abs/2409.04466[Multi-block chemometric approaches][Unsupervised spectral classification][Geological samples]
• https://arxiv.org/abs/2404.17294[Machine learning recognition][Hybrid lead halide perovskite][Perovskite-related structure][Out of X-ray diffraction patterns]
• https://arxiv.org/abs/2405.04876[Accelerating][Prediction][Stacking fault energy][Combining ab initio calculations and machine learning]
• https://arxiv.org/abs/2405.04729[Machine learning][Parameter analysis][Perovskite X-ray detector]
• https://arxiv.org/abs/2405.06104[Fluorescent graphene quantum dots-enhanced machine learning][Accurate detection][Quantification][Hg²⁺][Fe³⁺][Real water sample]
• https://arxiv.org/abs/2405.07370[Accelerating QM/MM simulation][Electrochemical interface][Machine learning][Electronic charge densities]
• https://arxiv.org/abs/2405.11393[Optical materials discovery and design][Federated database][Machine learning]
• https://arxiv.org/abs/2405.12143[Machine learning][Predicting ultralow thermal conductivity][High ZT][Complex thermoelectric material]
• https://arxiv.org/abs/2405.18396[Machine learning][Microstructure-informed materials modeling and design]
• https://arxiv.org/abs/2405.19838[Machine learning][Explore high-entropy alloy][Desired enthalpy][Room-temperature hydrogen storage][Prediction][Density functional theory][Experimental data]
• https://arxiv.org/abs/2405.20857[Machine learning conservation law][Dynamical system]
• https://arxiv.org/abs/2405.20946[Fast characterization][Multiplexed single-electron pumps][Machine learning]
• https://arxiv.org/abs/2406.00363[Exploring quantum localization][Machine learning]
• https://arxiv.org/abs/2406.05143[Determining domain][Machine learning model][Kernel density estimate][Application][Materials property prediction]
• https://arxiv.org/abs/2406.06489[Probing out-of-distribution generalization][Machine learning][Material]
• https://arxiv.org/abs/2406.10551[Electron dynamics][Three-dimensional Brillouin zone][Machine learning]
• https://arxiv.org/abs/2406.12904[Meent][Differentiable electromagnetic simulator][Machine learning]
• https://arxiv.org/abs/2406.13389[Unifying mixed gas adsorption][Molecular sieve membrane][MOF][Machine learning]
• https://arxiv.org/abs/2406.14524[High-Tc superconductor candidates][Machine learning]
• https://arxiv.org/abs/2406.15326[LeapFrog][Getting][Jump][Multi-scale materials simulation][Machine learning]
• https://arxiv.org/abs/2406.15316[Discovery][Novel silicon allotrope][Optimized band gap][Enhance solar cell efficiency][Evolutionary algorithm][Machine learning]
• https://arxiv.org/abs/2406.14809[Gas permeability][Diffusivity][Solubility][Polymer][Simulation-experiment data fusion][Multi-task machine learning]
• https://arxiv.org/abs/2406.15515[Machine learning model][Accurately predicting properties][CsPbCl₃][Perovskite quantum dot]
• https://arxiv.org/abs/2406.15491[Vibrational entropy][Free energy][Solid lithium][Covariance][Atomic displacement][Machine learning]
• https://arxiv.org/abs/2406.16166[Composite material design][Optimized fracture toughness][Machine learning]
• https://arxiv.org/abs/2406.17197[Machine learning][Screening factor][Soft bond valence approach][Rapid crystal structure estimation]
• https://arxiv.org/abs/2406.17956[Impact][Data bias][Machine learning][Crystal compound synthesizability prediction]
• https://arxiv.org/abs/2407.06975[Optimization][Noncollinear magnetic ordering temperature][Y-type hexaferrite][Machine learning]
• https://arxiv.org/abs/2407.18388[Predicting quantum materials properties][Novel faithful machine learning embedding]
• https://arxiv.org/abs/2407.21228[Describe][Transform][Machine learning][Feature engineering][Grain boundaries][Other variable-sized atom cluster]
• https://arxiv.org/abs/2408.00466[Unlocking Thermoelectric Potential: A Machine Learning Stacking Approach for Half Heusler Alloys]
• https://arxiv.org/abs/2407.21146[Low dimensional fragment-based descriptor][Property prediction][Inorganic material][Machine learning]
• https://arxiv.org/abs/2407.09674[Accelerating high-throughput phonon calculation][Machine learning universal potential]
• https://arxiv.org/abs/2407.12621[Distinguishing isotropic and anisotropic signals][X-ray total scattering][Machine learning]
• https://arxiv.org/abs/2407.17924[Inherent structural descriptor][Machine learning]
• https://arxiv.org/abs/2408.00142[Machine learning][Boosted entropy-engineered synthesis][Stable nanometric solid solution][CuCo alloy][Efficient nitrate reduction to ammonia]
• https://arxiv.org/abs/2408.01141[Machine learning][Topological energy braiding][Non-Bloch band]
• https://arxiv.org/abs/2408.03508[Unsupervised][Self-driving multi-step growth][InAs/GaAs quantum dot][Heterostructure][Machine learning]
• https://arxiv.org/abs/2408.03469[Performance classification][Remaining useful life prediction][Lithium batteries][Machine learning][Early cycle electrochemical impedance spectroscopy measurement]
• https://arxiv.org/abs/2408.03556[Machine learning supported annealing][Prediction][Grand canonical crystal structure]
• https://arxiv.org/abs/2408.08654[Accelerating ab initio melting property calculation][Machine learning][Application][High entropy alloy TaVCrW]
• https://arxiv.org/abs/2408.05135[SPACIER][On-demand polymer design][Fully automated all-atom classical molecular dynamics][Integrated][Machine learning pipeline]
• https://arxiv.org/abs/2408.05850[Machine learning][Characterizing uncertain elastic properties][Fused filament fabricated material][Topology optimization application]
• https://arxiv.org/abs/2408.06654[Advancing nonadiabatic molecular dynamics simulation][Solid][Achieving supreme accuracy][Efficiency][Machine learning]
• https://arxiv.org/abs/2408.09447[Deconvoluting thermomechanical effect][X-ray diffraction data][Machine learning]
• https://arxiv.org/abs/2503.05577[opXRD][Open experimental powder X-ray diffraction database]
• https://arxiv.org/abs/2408.09840[Machine learning][Physics knowledge][Prediction][Survey]
• https://arxiv.org/abs/2408.11949[Machine learning unveil][Materials physical properties][Thermoelectric generators efficiency][Half-Heuslers case]
• https://arxiv.org/abs/2408.13843[Consistent machine learning][Topology optimization][Microstructure-dependent neural network material model]
• https://arxiv.org/abs/2408.15062[Multi-channel machine learning][Nonlocal kinetic energy density functional][Semiconductor]
• https://arxiv.org/abs/2408.16499[Predicting topological invariant][Unconventional superconducting pairing][Density of states][Machine learning]
• https://arxiv.org/abs/2409.02633[Predicting miscibility][Binary compound][Machine learning][Genetic algorithm study]
• https://arxiv.org/abs/2409.06085[Differentiable programming across the PDE][Machine learning barrier]
• https://arxiv.org/abs/2411.15101[What you see is not what you get][Neural partial differential equation][Illusion of learning]
• https://arxiv.org/abs/2409.07987[Evolution][Flat band][MoSe₂/WSe₂ moir©ß lattice][Combining machine learning][Band unfolding method]
• https://arxiv.org/abs/2409.07811[Modelling][Nucleate pool boiling][Coated substrate][Machine learning][Empirical approaches]
• https://arxiv.org/abs/2409.11438[Machine learning][Analyzing atomic force microscopy (AFM) image][Generated from polymer blends]
• https://arxiv.org/abs/2409.14573[Decoding][Hidden dynamics][Super-Arrhenius hydrogen diffusion][Multi-principal element alloys][Machine learning]
• https://arxiv.org/abs/2409.18965[Multiscale simulation][Machine learning facilitated design][Two-dimensional nanomaterials-based tunnel field-effect transistor][Review]
• https://arxiv.org/abs/2409.18822[Automated quantum system modeling][Machine learning]
• https://arxiv.org/abs/2410.01213[Versatile machine learning workflow][High-throughput analysis][Supported metal catalyst particle]
• https://arxiv.org/abs/2410.05604[Accelerating][Discovery][Low-energy structure configuration][Computational approach][Integrates first-principles calculation][Monte Carlo sampling][Machine learning]
• https://arxiv.org/abs/2410.05574[Machine learning inversion][Scattering][Mechanically driven polymer]
• https://arxiv.org/abs/2501.14647[Machine learning inversion][Small-angle scattering][Charged polymer]
• https://arxiv.org/abs/2410.11392[Investigating data hierarchies][Multifidelity machine learning][Excitation energies]
• https://arxiv.org/abs/2410.12007[Machine learning][Ising model][Spherical Fibonacci lattice]
• https://arxiv.org/abs/2410.13645[Automated model discovery][Tensional homeostasis][Constitutive machine learning][Growth and remodeling]
• https://arxiv.org/abs/2410.13711[Automated classification][Individual atom][Surface][Machine learning]
• https://arxiv.org/abs/2411.05019[Enhancing accuracy][Feature insight][Hydration free energy prediction][Small molecule][Machine learning]
• https://arxiv.org/abs/2411.07448[Defect formation][CsSnI₃][Density functional theory][Machine learning]
• https://arxiv.org/abs/2411.10605[Effects of four-phonon scattering][Wave-like phonon tunneling effect][Thermoelectric Properties][Mg₂GeSe₄][Machine learning]
• https://arxiv.org/abs/2411.17755[Deciphering acoustic emission][Machine learning]
• https://arxiv.org/abs/2411.18907[Autonomous materials search][Machine learning][Ab initio calculation][L10-FePt-based quaternary alloy]
• https://arxiv.org/abs/2412.00288[Machine learning delta-T noise][Temperature bias estimation]
• https://arxiv.org/abs/2412.01232[Variational formulation][Duality][To solve partial differential equation][B-spline][Machine learning approximants]
• https://arxiv.org/abs/2412.02916[CuXASNet][Rapid and accurate prediction][Copper L-edge X-ray absorption spectra][Machine learning]
• https://arxiv.org/abs/2412.04699[Driving thermoelectric optimization][AgSbTe₂][Design][Experiment][Machine learning]
• https://arxiv.org/abs/2412.09638[Predicting organic-inorganic halide perovskite photovoltaic performance][Optical properties][Constituent film][Machine learning]
• https://arxiv.org/abs/2412.11129[Disentangling coherent][Incoherent effect][Superconductor photoemission spectra][Machine learning]
• https://arxiv.org/abs/2412.11518[Specific heat anomalies][Local symmetry breaking][(Anti-)fluorite materials][Machine learning molecular dynamics study]
• https://arxiv.org/abs/2412.10794[Understanding][Role of defect][Lattice transport properties][Half-Heusler compound][Machine learning analysis]
• https://arxiv.org/abs/2412.11981[Industrial-scale prediction][Cement clinker phase][Machine learning]
• https://arxiv.org/abs/2412.12161[Discover physical concept][Equation][Machine learning]
• https://arxiv.org/abs/2412.12312[Application][Machine learning][Ion beam analysis][Material]
• https://arxiv.org/abs/2412.12224[Application][Machine learning][Grain-related clustering][Laue spot][Polycrystalline energy dispersive Laue pattern]
• https://arxiv.org/abs/2412.14109[Machine learning co-pilot][Screening][Organic molecular additives][Perovskite solar cell]
• https://arxiv.org/abs/2501.00573[Exploring quantum-dot][Engineered solid-state photon upconversion][PbS:Yb³⁺,Er³⁺/CuBiO][Density functional theory][Machine learning method][Water splitting]
• https://arxiv.org/abs/2501.01863[Linear scaling calculation][Atomic forces and energies][Machine learning local density matrix]
• https://arxiv.org/abs/2501.04172[Machine learning][Identifying grain boundaries][Scanning electron microscopy][SEM][Image][Nanoparticle superlattice]
• https://arxiv.org/abs/2501.05164[Tree model][Machine learning][Identify liquid metal][Alloy superconductor]
• https://arxiv.org/abs/2501.15057[Predictive modeling][Uncertainty quantification][Fatigue life][Metal alloy][Machine learning]
• https://arxiv.org/abs/2501.09743[Improvement][Data analytics technique][Reflection high energy electron diffraction][Enable machine learning]
• https://arxiv.org/abs/2501.09287[Machine learning relationship][Nanoporous structure][Electrochemical performance][MOF supercapacitor]
• https://arxiv.org/abs/2502.02447[Hydrogen liquid-liquid transition][First principles][Machine learning]
• https://arxiv.org/abs/2502.04130[Search][Stable and low-energy Ce-Co-Cu ternary compounds][Machine learning]
• https://arxiv.org/abs/2502.04603[Fundamental factor][Governing stabilization][Janus 2D-bulk heterostructure][Machine learning]
• https://arxiv.org/abs/2502.06517[Machine learning][Ground state preparation][Measurement and feedback]
• https://arxiv.org/abs/2502.16665[New framework][X-ray absorption spectroscopy data analysis][Machine learning][XASDAML]
• https://arxiv.org/abs/2502.18859[Machine learning][Phosphor's excitation band position]
• https://arxiv.org/abs/2503.00836[Insights into dendritic growth mechanisms][Batteries][Combined machine learning][Computational study]
• https://arxiv.org/abs/2503.04035[Unveiling][Oxidation mechanism][Octa-penta graphene][Multidimensional exploration][First-principles][Machine learning]
• https://arxiv.org/abs/2503.05525[Machine learning][Improved density functional theory thermodynamics]
• https://arxiv.org/abs/2503.10040[Rapid analysis][Point-contact Andreev reflection spectra][Machine learning][Adaptive data augmentation]
• https://arxiv.org/abs/2503.11013[ARES-Phonon][Phonon calculation package][Nondiagonal supercell finite displacement method][Machine learning]
• https://arxiv.org/abs/2503.16276[Machine learning][Multiple topological materials dataset]
• https://arxiv.org/abs/2112.02287[BenchML][Extensible pipelining framework][benchmarking representation][Material][Molecule][Scale]
• https://arxiv.org/abs/2104.01352[Learning Rule][Materials Properties and Functions]
• https://arxiv.org/abs/2101.00067[Screening and understanding Li adsorption][2-dimensional metallic material][Learning physics]
• https://arxiv.org/abs/2206.15457[PhySRNet][Physics informed super-resolution network][Application][Computational solid mechanics]
• https://arxiv.org/abs/2008.08781[Boron cage effect][Nd-Fe-B crystal structure][Stability]
• https://arxiv.org/abs/2007.07523[Atomistic structure learning algorithm][Surrogate energy model relaxation]
• https://arxiv.org/abs/2002.11952[Autonomous robotic nanofabrication][Reinforcement learning]
• https://arxiv.org/abs/2308.07897[Probabilistic phase labeling][Lattice refinement][Autonomous material research]
• https://arxiv.org/abs/2408.00229[Invariant discovery][Features across multiple length scales][Application][Microscopy and autonomous materials characterization]
• https://arxiv.org/abs/2502.18604[Mind the gap][Bridging][Divide][AI aspiration][Reality][Autonomous characterization]
• https://arxiv.org/abs/2009.06739[Predictive synthesis][Quantum material][Probabilistic reinforcement learning]
• https://arxiv.org/abs/2105.01079[Experimental deep reinforcement learning][Error-robust gateset design][Superconducting quantum computer]
• https://arxiv.org/abs/2209.11259[Computational discovery][Energy-efficient heat treatment][Microstructure design][Deep reinforcement learning]
• https://arxiv.org/abs/2005.12759[Classifying global state preparation][Deep reinforcement learning]
• https://arxiv.org/abs/2203.06975[Precise atom manipulation][Deep reinforcement learning]
• https://arxiv.org/abs/2204.06288[Automated atomic silicon quantum dot circuit design][Deep reinforcement learning]
• https://arxiv.org/abs/2009.14825[Deep reinforcement learning][Efficient measurement][Quantum device]
• https://arxiv.org/abs/2210.11931[Deep reinforcement learning][Inverse inorganic materials design]
• https://arxiv.org/abs/2312.05445[Molecular autonomous pathfinder][Deep reinforcement learning]
• https://arxiv.org/abs/2404.05905[Computing transition pathway][Rare event][Deep reinforcement learning]
• https://arxiv.org/abs/2407.12453[Estimating reaction barrier][Deep reinforcement learning]
• https://arxiv.org/abs/2502.07326[PICTS][Novel deep reinforcement learning approach][Dynamic P-I control][Scanning probe microscopy]
• https://arxiv.org/abs/2502.18111[Controlling dynamics][Stochastic system][Deep reinforcement learning]
• https://arxiv.org/abs/2502.08405[Acceleration][Crystal structure relaxation][Deep reinforcement learning]
• https://arxiv.org/abs/2110.05260[Designing composite][Target effective Young's modulus][Reinforcement learning]
• https://arxiv.org/abs/2204.04785[Driving black-box][Quantum thermal machine][Optimal power/efficiency trade-off][Reinforcement learning]
• https://arxiv.org/abs/2209.04467[Dynamic zoom-in detection][Exfoliated two-dimensional crystal][Reinforcement learning]
• https://arxiv.org/abs/2212.11651[Approximate autonomous quantum error correction][Reinforcement learning][Phys. Rev. Lett. 131, 050601 (2023)]
• https://arxiv.org/abs/2307.05394[Reinforcement learning-guided long-timescale simulation][Hydrogen transport][Metal]
• https://arxiv.org/abs/2310.02902[Searching][High-value molecule][Reinforcement learning][Transformer]
• https://arxiv.org/abs/2311.17519[Reinforcement learning][Thermal fluctuation][Nano-scale]
• https://arxiv.org/abs/2402.00972[Closure discovery][Coarse-grained partial differential equation][Multi-agent reinforcement learning]
• https://arxiv.org/abs/2402.10559[Discovery][Exchange-only gate sequence][CNOT][Record-low gate time][Reinforcement learning]
• https://arxiv.org/abs/2404.18774[Self-training superconducting neuromorphic circuit][Reinforcement learning rule]
• https://arxiv.org/abs/2407.15872[Reinforcement learning strategy][Automate and accelerate h/p-multigrid solvers]
• https://arxiv.org/abs/2408.09524[Enhancing quantum memory lifetime][Measurement-free local error correction][Reinforcement learning]
• https://arxiv.org/abs/2501.05113[Constrained optimization][Charged particle tracking][Multi-agent reinforcement learning]
• https://arxiv.org/abs/2302.13380[Closed-loop error correction learning][Aaccelerate][Experimental discovery][Thermoelectric material]
• https://arxiv.org/abs/1812.00085[NOMAD 2018 Kaggle Competition][Solving materials science challenges][Crowd sourcing]
• https://arxiv.org/abs/2205.15686[NOMAD][Artificial-intelligence toolkit][Turning materials-science data][Knowledge][Understanding]
• https://arxiv.org/abs/2403.10470[MADAS][Python framework][Assessing similarity][Materials-science data]
• https://arxiv.org/abs/2101.01773[Learning the Crystal Structure Genome][Property classification]
• https://arxiv.org/abs/2501.19110[Learning the Hamiltonian matrix][Large atomic system]
• https://arxiv.org/abs/2105.04085[Bond-length distribution][Classified][Coordination environment]
• https://arxiv.org/abs/2103.15855[Learning algorithm][Emergent scaling behavior][Classifying phase transition]
• https://arxiv.org/abs/2308.16621[Meta-analysis][Literature data][Metal additive manufacturing][What can we (and the machine) learn from reported data?]
• https://arxiv.org/abs/2205.05794[Deep-learned generator][Porosity distribution][Metal additive manufacturing]
• https://arxiv.org/abs/2503.03837[Materials Graph Library (MatGL)][Open-source graph deep learning library][Materials science and chemistry]
• https://arxiv.org/abs/2104.10242[Accelerating materials discovery][Bayesian optimization][Graph deep learning]
• https://arxiv.org/abs/2202.02450[Universal graph deep learning][Interatomic potential][Periodic table]
• https://arxiv.org/abs/2302.03331[Graph deep learning][Accelerated efficient crystal structure search][Feature extraction]
• https://arxiv.org/abs/2409.00957[Data-efficient construction][High-fidelity graph deep learning][Interatomic potential]
• https://arxiv.org/abs/2411.01197[Combining graph deep learning][London dispersion interatomic potential][A case study][Pnictogen chalcohalide][J. Chem. Phys. 161, 174106 (2024)]
• https://arxiv.org/abs/1706.06480[Advanced steel microstructure classification][Deep learning]
• https://arxiv.org/abs/1709.02298[The face of crystals][Insightful classification][Deep learning]
• https://arxiv.org/abs/2108.09523[Automating crystal-structure phase mapping][Combining deep learning][Constraint reasoning]
• https://arxiv.org/abs/1712.06113[SchNet][Deep learning architecture][Molecules][Materials]
• https://arxiv.org/abs/1801.05860[Deep learning][Atomically resolved scanning transmission electron microscopy Image][Chemical identification][Tracking local transformation]
• https://arxiv.org/abs/1802.10518[Mapping mesoscopic phase evolution][e-beam induced transformation][Deep learning][Atomically resolved image]
• https://arxiv.org/abs/1805.02791[Microstructural materials Design][Deep adversarial learning]
• https://arxiv.org/abs/1803.05381[Deep learning Analysis][Defect and phase evolution][Electron beam induced transformations][WS₂]
• https://arxiv.org/abs/1805.10503[Deep learning][Topological invariant]
• https://arxiv.org/abs/1809.03704[General resolution enhancement method][Atomic Force Microscopy][Deep learning]
• https://arxiv.org/abs/1809.10860[Deep learning][Bandgap][Topologically doped graphene]
• https://arxiv.org/abs/1811.08928[Deep Learning][DFT]
• https://arxiv.org/abs/1811.09724[3D deep learning][Voxelized atomic configuration][Modeling atomistic potential][Complex solid-solution alloy]
• https://arxiv.org/abs/2404.16524[3D deep learning][Enhanced atom probe tomography analysis][Nanoscale microstructure]
• https://arxiv.org/abs/1901.10669[Predicting the mechanical response][Oligocrystal][Deep learning]
• https://arxiv.org/abs/1902.06876[Reconstruction of 3-D atomic distortion][Electron microscopy][Deep learning]
• https://arxiv.org/abs/2411.18493[Advancing natural orbital functional calculation][Through deep learning-inspired techniques][Large-scale strongly correlated electron system]
• https://arxiv.org/abs/1905.10730[Deep learning-enhanced variational Monte Carlo method][Quantum many-body physics]
• https://arxiv.org/abs/2307.04638[DeePTB][Deep learning-based tight-binding approach][Ab initio accuracy]
• https://arxiv.org/abs/2110.13720[Deep DIC][Deep learning-based digital image correlation][End-to-end displacement][Strain measurement]
• https://arxiv.org/abs/1904.05433[Phase segmentation][Atom-probe tomography][Deep learning-based edge detection]
• https://arxiv.org/abs/2405.10325[Uncertainty][Exploration][Deep learning-based atomistic model][Screening molten salt properties and compositions]
• https://arxiv.org/abs/2408.15681[Towards a unified benchmark and framework][Deep learning-based prediction][Nuclear magnetic resonance chemical shift]
• https://arxiv.org/abs/2410.22371[Error bound][Deep learning-based uncertainty propagation][SDE]
• https://arxiv.org/abs/2411.18259[Transfer learning][Deep learning-based prediction][Lattice thermal conductivity]
• https://arxiv.org/abs/2502.10245[Deep learning-based holography][T-linear resistivity]
• https://arxiv.org/abs/1906.02130[Predicting compressive strength][Consolidated molecular solid][Computer vision][Deep learning]
• https://arxiv.org/abs/1906.11220[Deep learning][Enabled fast optical characterization][Two-dimensional material]
• https://arxiv.org/abs/1909.04648[Deep learning][Automated classification][characterization][Amorphous material]
• https://arxiv.org/abs/1909.04784[Deep learning][Topological phase transition][Entanglement aspect]
• https://arxiv.org/abs/1911.12566[Deep Learning][Inverse design][Mid-infrared graphene plasmon]
• https://arxiv.org/abs/2011.12603[Deep learning][Nano-photonics][Inverse design and beyond]
• https://arxiv.org/abs/2108.12019[Generative deep learning][Inverse design][High-entropy refractory alloy]
• https://arxiv.org/abs/2409.19124[Generative deep learning][Inverse design of materials]
• https://arxiv.org/abs/2312.15136[Towards end-to-end structure solution][Information-compromised diffraction data][Generative deep learning]
• https://arxiv.org/abs/2109.03114[Deep learning][Modeling][Inverse design][Radiative heat transfer]
• https://arxiv.org/abs/1910.00617[Predicting materials properties][Without crystal structure][Deep representation learning][Stoichiometry]
• https://arxiv.org/abs/1910.10467[Deep learning][Supersampled scanning transmission electron microscopy]
• https://arxiv.org/abs/1910.13551[Deep learning][Optoelectronic][Organic semiconductor]
• https://arxiv.org/abs/1912.03296[Rapid exploration][Topological band structure][Deep learning]
• https://arxiv.org/abs/1912.09027[Density functional theory][Deep-learning][Accelerate data analytics][Scanning tunneling microscopy]
• https://arxiv.org/abs/1912.05916[Deep-learning estimation][Band gap][Reading-periodic-table method][Periodic convolution layer]
• https://arxiv.org/abs/2209.13026[Electron energy loss spectroscopy database synthesis][Automation][Core-loss edge recognition][Deep-learning neural network]
• https://arxiv.org/abs/2211.10604[Deep-learning electronic-structure calculation][Magnetic superstructure]
• https://arxiv.org/abs/2005.09634[Automated][Copper alloy grain size evaluation][Deep-learning CNN]
• https://arxiv.org/abs/2010.09547[Deep-learning interatomic potential][Irradiation damage simulation][MoS₂][Ab initial accuracy]
• https://arxiv.org/abs/2403.03515[Lattice thermal conductivity][Mechanical properties][Single-layer penta-NiN₂][Deep-learning interatomic potential]
• https://arxiv.org/abs/2205.08366[Deep-learning model][Fast Prediction][Vacancy formation][Diverse material]
• https://arxiv.org/abs/2409.15784[Deep-learning real-time phase retrieval][Imperfect diffraction pattern][X-ray free-electron laser]
• https://arxiv.org/abs/2410.13594[Deep-learning recognition][Tracking][Individual nanotube][Low-contrast microscopy video]
• https://arxiv.org/abs/1910.12750[Deep-learning-based image segmentation][Integrated][Optical microscopy][Automatically searching][Two-dimensional material]
• https://arxiv.org/abs/2205.11407[Deep-learning-based prediction][Nanoparticle phase transition][in situ transmission electron microscopy]
• https://arxiv.org/abs/2401.14588[Deep-learning-based prediction][Tetragonal --> cubic transition][Davemaoite]
• https://arxiv.org/abs/2501.14382[Integrating deep-learning-based magnetic model][Non-collinear spin-constrained method][Methodology][Implementation][Application]
• https://arxiv.org/abs/2205.00060[Designing high-Tc superconductor][BCS-inspired screening][Density functional theory][Deep-learning]
• https://arxiv.org/abs/2311.14557[Augmentation][Scarce data][New approach][Deep-learning modeling][Composites]
• https://arxiv.org/abs/2401.17015[DeepH-2][Enhancing deep-learning electronic structure][Equivariant local-coordinate transformer]
• https://arxiv.org/abs/2302.06389[Deep-learning][Quantitative structural characterization][Additive manufacturing]
• https://arxiv.org/abs/2404.06449[Deep-learning database][Density functional theory Hamiltonian][Twisted materials]
• https://arxiv.org/abs/2405.05975[Deep-learning design][Graphene metasurface][Quantum control][Dirac electron holography]
• https://arxiv.org/abs/2405.17089[Improved penalty-based excited-state][Variational Monte Carlo approach][Deep-learning ansatzes]
• https://arxiv.org/abs/2406.14585[Deep-learning-assisted reconfigurable metasurface antenna][Real-time holographic beam steering][Nanophotonics 12.13 (2023): 2415-2423]
• https://arxiv.org/abs/1912.11582[Deep learning][Aided rational design][Oxide glass]
• https://arxiv.org/abs/2001.02050[Deep learning][Surrogate interacting Markov chain Monte Carlo][Full wave inversion scheme][Materials quantification]
• https://arxiv.org/abs/2001.08233[Deep learning enabled strain mapping][Single-atom defect][2D transition metal dichalcogenide][Sub-picometer precision]
• https://arxiv.org/abs/2002.04716[Robust multi-scale multi-feature deep learning][Atomic and defect identification][STM][H-Si(100) 2x1 surface]
• https://arxiv.org/abs/2002.06763[Optimization][Validation][Deep learning][CuZr atomistic potential][Robust][Application][Crystalline][Amorphous][Near-DFT Accuracy]
• https://arxiv.org/abs/2002.07055[Optical lattice experiment][Unobserved conditions and scales][Generative adversarial deep learning]
• https://arxiv.org/abs/2004.01396[Generative adversarial network][Crystal structure prediction]
• https://arxiv.org/abs/2002.09039[Deep learning][Interface structure][4D STEM data][Cation intermixing vs. roughening]
• https://arxiv.org/abs/2002.10632[Genetic algorithm-guided deep learning][Grain boundary diagram][Addressing][Challenge][Five degrees of freedom]
• https://arxiv.org/abs/2003.12259[0.71-][Resolution electron tomography][Deep learning][Information recovery]
• https://arxiv.org/abs/2003.12402[Fast design][Plasmonic metasurface][Enabled by deep learning]
• https://arxiv.org/abs/2004.04814[Deep learning][Synthetic microstructure generation][Mmaterials-by-design framework][Heterogeneous energetic material]
• https://arxiv.org/abs/2005.01144[Deep learning][Understand and mitigate][Qubit noise environment]
• https://arxiv.org/abs/2005.03759[DeePore][Deep learning workflow][Rapid and comprehensive characterization][Porous material]
• https://arxiv.org/abs/2401.17788[Prompt-engineered large language model][Deep learning workflow][Materials classification]
• https://arxiv.org/abs/2005.10672[Insight][One-body density matrices][Deep learning]
• https://arxiv.org/abs/2006.03532[Quantifying][Dynamics][Protein self-organization][Deep learning analysis][Atomic force microscopy data]
• https://arxiv.org/abs/2006.09441[Real-time 3D nanoscale coherent imaging][Physics-aware deep learning]
• https://arxiv.org/abs/2009.00661[Transfer and confusion deep learning][Frustrated spin system]
• https://arxiv.org/abs/2009.08328[Review][Deep learning][Electron microscopy]
• https://arxiv.org/abs/2009.11810[Deep learning][Enabled design][Complex transmission matrix][Universal optical component]
• https://arxiv.org/abs/2010.16082[Deep learning][Accurate force field][Ferroelectric HfO₂]
• https://arxiv.org/abs/2011.10505[Synthetic image rendering][Annotation problem][Deep learning][Nanoparticle segmentation]
• https://arxiv.org/abs/2011.10227[StressNet][Deep learning][Predict stress][Fracture propagation][Brittle material]
• https://arxiv.org/abs/2011.10883[Supervised deep learning prediction][Formation enthalpy][Full set][Configuration][Complex phase][¦Ò¡Ýphase as an example]
• https://arxiv.org/abs/2012.01478[Leveraging uncertainty][Deep learning][Trustworthy materials discovery workflow]
• https://arxiv.org/abs/2012.03184[dPOLY][Deep learning][Polymer phase][Phase transition]
• https://arxiv.org/abs/2012.05322[Deep learning segmentation][Complex feature][Atomic-resolution phase contrast transmission electron microscopy image]
• https://arxiv.org/abs/2012.06056[Predicting orientation-dependent plastic susceptibility][Static structure][Amorphous solid][Deep learning]
• https://arxiv.org/abs/2012.09093[TEMImageNet][AtomSegNet][Deep learning training library][High-precision][Atom segmentation][Localization][Denoising][Super-resolution processing][Atom-resolution scanning TEM Image]
• https://arxiv.org/abs/2101.01178[Advance][Electron microscopy][Deep learning]
• https://arxiv.org/abs/2102.01620[AlphaCrystal][Contact map based crystal structure prediction][Deep learning]
• https://arxiv.org/abs/2102.12009[Deep learning order parameter][Polymer phase transition]
• https://arxiv.org/abs/2103.12526[Effectiveness of data augmentation][Porous substrate][Nanowire][Fiber][Tip image][Deep learning intelligence]
• https://arxiv.org/abs/2104.03574[Deep learning][Topological phase transition][Entanglement aspect][Two-dimensional chiral p-wave superconductor]
• https://arxiv.org/abs/2104.04392[Deep learning][Visualization][Novelty detection][Large X-ray diffraction dataset]
• https://arxiv.org/abs/2104.05408[Orbital-free density functional theory][Small dataset][Deep learning]
• https://arxiv.org/abs/2105.03870[Deep learning][Topological phase transition][Entanglement aspect][Unsupervised way]
• https://arxiv.org/abs/2105.07125[Disentangling ferroelectric wall dynamics][Identification][Pinning mechanism][Deep learning]
• https://arxiv.org/abs/2105.09866[Deep learning][Dielectric quasi-cubic particle][Uniform electric field]
• https://arxiv.org/abs/2105.10564[Deep learning prediction][Stress field][Additively manufactured metal][Intricate defect network]
• https://arxiv.org/abs/2106.12730[Deep learning][Crystal plasticity][Preconditioning approach][Accurate orientation evolution prediction]
• https://arxiv.org/abs/2107.13711[Bio-inspired vascularized electrode][High-performance fast-charging battery][Designed by deep learning]
• https://arxiv.org/abs/2108.01057[Deep learning][Deformation-dependent conductance][Thin film][Nanobubble][Graphene]
• https://arxiv.org/abs/2108.07222[Deep learning analysis][Polaritonic waves image]
• https://arxiv.org/abs/2108.07614[Electronic response quantities of solids][Deep Learning]
• https://arxiv.org/abs/2108.08882[Deep learning][Automatic defect analysis framework][In-situ TEM Ion Irradiation]
• https://arxiv.org/abs/2108.08883[Multi defect detection][Analysis][Electron microscopy image][Deep learning]
• https://arxiv.org/abs/2108.13830[Bridging the gap][Deep learning][Frustrated quantum spin system][Extreme-scale simulation][New generation of Sunway supercomputer]
• https://arxiv.org/abs/2109.07104[Accurate force field][Two-dimensional ferroelectrics][Deep learning]
• https://arxiv.org/abs/2109.14053[AutoPhaseNN][Unsupervised physics-aware][Deep learning][3D nanoscale coherent imaging]
• https://arxiv.org/abs/2110.02109[Deep learning][Modelling][Complex aperiodic plasmonic metasurface][Arbitrary size]
• https://arxiv.org/abs/2110.08244[Performance][Success][Limitation][Deep learning][Semantic segmentation][Multiple defects][Transmission electron micrograph]
• https://arxiv.org/abs/2103.16664[Probabilistic deep learning approach][Automate][Interpretation][Multi-phase diffraction spectra]
• https://arxiv.org/abs/2110.12958[Probabilistic deep learning approach][Targeted hybrid organic-inorganic perovskite]
• https://arxiv.org/abs/2111.05271[Stress field prediction][Fiber-reinforced composite material][Deep learning approach]
• https://arxiv.org/abs/2201.07342[Deep learning approach][Semantic segmentation][Unbalanced data][Electron tomography][Catalytic material]
• https://arxiv.org/abs/2203.06820[Edge detection][Image filter algorithm][Spectroscopic analysis][Deep learning application]
• https://arxiv.org/abs/2303.16412[Comprehensive][Versatile][Multimodal deep learning approach][Predicting][Diverse properties][Advanced material]
• https://arxiv.org/abs/2304.09606[Screening spin lattice interaction][Deep learning approach]
• https://arxiv.org/abs/2309.09355[Structure to property][Chemical element embedding][Deep learning approach][Accurate prediction][Chemical properties]
• https://arxiv.org/abs/2310.06852[DeepCrysTet][Deep learning approach][Tetrahedral mesh][Predicting properties][Crystalline material]
• https://arxiv.org/abs/2310.15188[Deep learning approaches][Dynamic mechanical analysis][Viscoelastic fiber composite]
• https://arxiv.org/abs/1802.03008[Deep learning approach][Identify local structure][Atomic-resolution transmission electron microscopy image]
• https://arxiv.org/abs/1901.00915[Deep learning approach][Structural analysis of proteins]
• https://arxiv.org/abs/2003.11505[Deep learning approach][Determining the chiral indices][Carbon nanotube][High-resolution transmission electron microscopy image]
• https://arxiv.org/abs/2409.07721[Deep learning approach][Search for superconductors][Electronic bands]
• https://arxiv.org/abs/2411.02604[Computing critical exponent][3D Ising model][Pattern recognition/deep learning approach]
• https://arxiv.org/abs/2502.19151[Design][Resistive frequency selective surface][Radar absorbing structure][Deep learning approach]
• https://arxiv.org/abs/2112.01971[Dynamic fracture][Bicontinuously nanostructured copolymer][Deep learning analysis][Big-data-generating experiment]
• https://arxiv.org/abs/2112.03072[Deep learning][Automated phase segmentation][EBSD map][Dual phase steel microstructure]
• https://arxiv.org/abs/2304.10382[Conditional generative model][Learning stochastic processes]
• https://arxiv.org/abs/2004.12512[Guided search][Desired functional response][Bayesian optimization][Generative model][Hysteresis loop shape engineering][Ferroelectrics]
• https://arxiv.org/abs/2312.03687[MatterGen][Generative model][Inorganic materials design]
• https://arxiv.org/abs/2012.09314[Computational discovery][New 2D material][Deep learning generative model]
• https://arxiv.org/abs/2112.03528[Physics guided deep learning generative model][Crystal materials discovery]
• https://arxiv.org/abs/2403.10846[Deep learning generative model][Crystal structure prediction]
• https://arxiv.org/abs/2409.06560[Primer on variational inference][Physics-informed deep generative modelling]
• https://arxiv.org/abs/2503.18012[Scalable physics-informed deep generative model][Solving forward and inverse stochastic differential equations]
• https://arxiv.org/abs/2304.08446[Inverse design][Next-generation superconductor][Data-driven deep generative model]
• https://arxiv.org/abs/2311.13328[MagGen][Graph aided deep generative model][Inverse design][Stable][Permanent magnet]
• https://arxiv.org/abs/2312.03690[Inverse design][Vitrimeric polymer][Molecular dynamics][Generative modeling]
• https://arxiv.org/abs/2407.04557[Structural constraint integration][Generative model][Discovery of quantum material candidates]
• https://arxiv.org/abs/2312.14485[Self-supervised generative model][Crystal structure]
• https://arxiv.org/abs/2501.02059[Active learning enables extrapolation][Molecular generative model]
• https://arxiv.org/abs/1910.11499[Deep generative model][Inorganic chemical composition]
• https://arxiv.org/abs/2206.12159[Data-driven discovery][Novel 2D material][Deep generative model]
• https://arxiv.org/abs/2501.08998[CrystalGRW][Generative modeling][Crystal structure][Targeted properties][Geodesic random walk]
• https://arxiv.org/abs/2503.04776[GrainPaint][Multi-scale diffusion-based generative model][Microstructure reconstruction][Large-scale object]
• https://arxiv.org/abs/2112.03364[Scalable geometric deep learning][Molecular graph]
• https://arxiv.org/abs/2303.10140[Geometric deep learning][Molecular crystal structure prediction]
• https://arxiv.org/abs/2409.05169[Learning polycrystal plasticity][Mesh-based subgraph geometric deep learning]
• https://arxiv.org/abs/2112.04977[Bringing atomistic deep learning][Prime time]
• https://arxiv.org/abs/2112.14429[Leveraging large-scale computational database][Deep learning][Accurate prediction]
• https://arxiv.org/abs/2201.13306[Deep learning][Disordered topological insulator][Entanglement spectrum]
• https://arxiv.org/abs/2202.00204[Disentangling multiple scattering][Deep learning][Application][Strain mapping][Electron diffraction pattern]
• https://arxiv.org/abs/2202.00574[Identifying Pauli spin blockade][Deep learning]
• https://arxiv.org/abs/2202.12611[Phase object reconstruction][4D-STEM][Deep learning]
• https://arxiv.org/abs/2202.13268[Deep learning][Functional renormalization group][Phys. Rev. Lett. 129, 136402 (2022)]
• https://arxiv.org/abs/2203.16676[Exploring][Hysteresis properties][Nanocrystalline permanent magnet][Deep learning]
• https://arxiv.org/abs/2204.01769[Deep learning][Rare-event rational design][3D printed multi-material][Mechanical metamaterial]
• https://arxiv.org/abs/2401.00065[Accelerating process development][3D printing][New metal alloy]
• https://arxiv.org/abs/2204.04740[Melting temperature prediction][First principles][Deep learning]
• https://arxiv.org/abs/2204.08157[Deep learning][Coherent diffractive imaging]
• https://arxiv.org/abs/2205.05426[RustSEG][Automated segmentation][Corrosion][Deep learning]
• https://arxiv.org/abs/2205.09075[Predicting failure characteristics][Structural material][Deep learning][nondestructive void topology]
• https://arxiv.org/abs/2206.04272[STEM image analysis][Deep learning][Identification][Vacancy defect][Polymorph][MoS₂]
• https://arxiv.org/abs/2206.14670[Deep learning][Spin-orbit torque characterization][Projected vector field magnet]
• https://arxiv.org/abs/2205.08367[Deep learning density functional][Gradient descent optimization]
• https://arxiv.org/abs/2407.14379[Deep learning density functional theory Hamiltonian][Real space]
• https://arxiv.org/abs/2401.17892[Deep-learning density functional][Perturbation theory][Phys. Rev. Lett. 132, 096401 (2024)]
• https://arxiv.org/abs/2403.09788[Solving deep-learning density functional theory][Variational autoencoder]
• https://arxiv.org/abs/2406.10536[Universal materials model][Deep-learning density functional theory Hamiltonian]
• https://arxiv.org/abs/2206.11457[Exploring physics][Ferroelectric domain wall][Real time][Deep learning][Scanning probe microscopy]
• https://arxiv.org/abs/1905.03928[Deep learning model][Atomic structures prediction][X-ray absorption spectroscopic data]
• https://arxiv.org/abs/2302.00261[Deep learning model][Inverse design][Semiconductor heterostructure][Desired electronic band structure]
• https://arxiv.org/abs/1812.01995[Deep learning model][Finding new superconductors]
• https://arxiv.org/abs/1912.06077[Understanding important features][Deep learning model][Transmission electron microscopy image segmentation]
• https://arxiv.org/abs/2105.12638[Predicting aqueous solubility][Organic molecule][Deep learning model][Varied molecular representation]
• https://arxiv.org/abs/2203.14326[DeepXRD][Deep learning model][Predicting][XRD spectrum][Materials composition]
• https://arxiv.org/abs/2211.11543[Leveraging orbital information][Atomic feature][Deep learning model]
• https://arxiv.org/abs/2211.04561[Physics-aware deep learning model][Energy localization][Multiscale shock-to-detonation simulation][Heterogeneous energetic material]
• https://arxiv.org/abs/2207.00243[Bridging fidelities][Predict nanoindentation tip radii][Interpretable deep learning model][JOM volume 74, pages 21952205 (2022)]
• https://arxiv.org/abs/2412.05948[Exploring lattice thermal conductivity model][Interpretable deep learning][Accelerate][Discovery][Novel material]
• https://arxiv.org/abs/2207.05013[Boosting heterogeneous catalyst discovery][Structurally constrained deep learning model]
• https://arxiv.org/abs/2207.10173[Benchmark test][Atom segmentation deep learning model][Consistent dataset]
• https://arxiv.org/abs/2503.17592[Benchmark dataset][Pore-scale CO₂-water interaction]
• https://arxiv.org/abs/2403.08596[Patching-based deep learning model][Inpainting][Bragg coherent diffraction pattern][Detectors' gaps]
• https://arxiv.org/abs/2408.06156[Graph-based descriptor][Condensed matter]
• https://arxiv.org/abs/2002.11315[Assessing graph-based deep learning Model][Predicting flash point]
• https://arxiv.org/abs/2309.14638[Deep charge][Deep learning model][Electron density][One-shot density functional theory calculation]
• https://arxiv.org/abs/2310.01350[Peridynamic-informed deep learning model][Brittle damage prediction]
• https://arxiv.org/abs/2402.10931[Enabling discovery][Enhanced generalisability][Deep learning model]
• https://arxiv.org/abs/2401.03004[SAPNet][Deep learning model][Identification][Single-molecule peptide post-translational modification][Surface enhanced Raman spectroscopy]
• https://arxiv.org/abs/2405.18944[Predicting many properties][Crystal][Single deep learning model]
• https://arxiv.org/abs/2408.01558[Accelerating domain-aware electron microscopy analysis][Deep learning model][Synthetic data][Image-wide confidence scoring]
• https://arxiv.org/abs/2207.08681[Deep learning potential][Mean force][Polymer grafted nanoparticle]
• https://arxiv.org/abs/2210.03430[Monitoring MBE substrate deoxidation][RHEED image-sequence analysis][Deep learning]
• https://arxiv.org/abs/2210.06310[Determining band structure parameters][Two-dimensional material][Deep learning]
• https://arxiv.org/abs/2210.06622[When does deep learning fail and how to tackle it?][Critical analysis][Polymer sequence-property surrogate model]
• https://arxiv.org/abs/2112.10254[Inverse deep learning method][Benchmark][Artificial electromagnetic material design]
• https://arxiv.org/abs/2001.06762[Development of interatomic potential][Al-Tb alloy][Deep learning method]
• https://arxiv.org/abs/2008.00442[Identifying][Elastic isotropy][Architectured material][Deep learning method]
• https://arxiv.org/abs/2110.14820[Recent advance][Application][Deep learning method][Materials Science]
• https://arxiv.org/abs/2204.07816[2^1296][Exponentially complex quantum many-body simulation][Scalable deep learning method]
• https://arxiv.org/abs/2210.10494[Spectroscopic data de-noising][Training-set-free deep learning method]
• https://arxiv.org/abs/2210.11200[Removing grid structure][Angle-resolved photoemission spectra][Deep learning method]
• https://arxiv.org/abs/2311.07059[Application][Deep learning method][Study of magnetic phenomena]
• https://arxiv.org/abs/2402.11434[Deep learning method][Hamiltonian parameter estimation][Magnetic domain image generation][Twisted van der Waals magnet]
• https://arxiv.org/abs/2404.06206[Deep learning method][Computing committor function][Adaptive sampling]
• https://arxiv.org/abs/2404.10891[Deep learning method][Colloidal silver nanoparticle concentration][Size distribution determination][UV-Vis extinction spectra]
• https://arxiv.org/abs/2211.01490[Deep learning Hamiltonian][Disordered image data][Quantum material]
• https://arxiv.org/abs/2211.09050[Deep learning][Spatial densities][Inhomogeneous correlated quantum system]
• https://arxiv.org/abs/2211.09866[Fast uncertainty estimate][Deep learning interatomic potential]
• https://arxiv.org/abs/2404.16187[Deep learning interatomic potential][Molecular structural ordering][Macroscale properties][Polyacrylonitrile (PAN) polymer]
• https://arxiv.org/abs/2406.09011[Finite-temperature properties][Antiferroelectric perovskite PbZrO₃][Deep learning interatomic potential]
• https://arxiv.org/abs/2211.15895[Composition based oxidation state prediction][Deep learning]
• https://arxiv.org/abs/2212.02643[Architected material][Mechanical compression][Design][Simulation][Deep learning][Experimentation]
• https://arxiv.org/abs/2105.07485[AtomAI][Deep learning framework][Analysis][Image][Spectroscopy data][(Scanning) transmission electron microscopy and Beyond]
• https://arxiv.org/abs/2007.04898[Difference-based deep learning framework][Stress prediction][Heterogeneous media]
• https://arxiv.org/abs/2212.14601[Accelerated multiscale mechanics modeling][Deep learning framework]
• https://arxiv.org/abs/2304.00616[Robust deep learning framework][Constitutive-relation modeling]
• https://arxiv.org/abs/2307.04121[Deep learning framework][Solving hyperbolic partial differential equation][Part I]
• https://arxiv.org/abs/2407.20384[Unified deep learning framework][Many-body quantum chemistry][Green's function]
• https://arxiv.org/abs/2407.20251[Uncertainty-aware deep learning framework-based robust design optimization][Metamaterial unit]
• https://arxiv.org/abs/2409.19552[Universal deep learning framework][Materials X-ray absorption spectra]
• https://arxiv.org/abs/2502.15483[MoMa][Modular deep learning framework][Material property prediction]
• https://arxiv.org/abs/2212.06444[Predicting thermoelectric transport properties][Composition][Attention-based deep learning]
• https://arxiv.org/abs/2301.05875[Diatom-inspired architected material][Language-based deep learning][Perception][Transformation][Manufacturing]
• https://arxiv.org/abs/2212.13809[Atomic force microscopy simulation][CO-functionalized tip][Deep learning]
• https://arxiv.org/abs/2302.08221[Efficient hybrid density functional calculation][Deep learning]
• https://arxiv.org/abs/2303.05869[Three-dimensional damage characterisation][Dual phase steel][Deep learning]
• https://arxiv.org/abs/2303.17345[Mapping microstructure][Shock-induced temperature field][Deep learning]
• https://arxiv.org/abs/2304.02048[Deep learning][Automated experimentation][Scanning transmission electron microscopy]
• https://arxiv.org/abs/2304.04986[Deep learning][Experimental electrochemistry][Battery cathode][Diverse composition]
• https://arxiv.org/abs/2304.08616[Exploring exotic configuration][Anomalous feature][Deep learning][Application][Classical][Quantum-classical hybrid anomaly detection]
• https://arxiv.org/abs/2305.02917[Bridging theory][Experiment][Digital twin][Deep learning segmentation][Defect][Monolayer MX2 phase]
• https://arxiv.org/abs/2305.05634[Deep learning][Genetic algorithm framework][Tailoring mechanical properties][Inverse microstructure optimization]
• https://arxiv.org/abs/2305.06981[Local structure][Thermodynamics][Melting curve][Boron phosphide][High pressure][Deep learning-driven ab initio simulation]
• https://arxiv.org/abs/2409.02952[Deep learning-driven evaluation][Prediction][Ion-doped NASICON material][Enhanced solid-state battery performance]
• https://arxiv.org/abs/2410.20402[Deep learning-driven microstructure characterization][Vickers hardness prediction][Mg-Gd alloy]
• https://arxiv.org/abs/2305.07790[Automated grain boundary][Segmentation][Microstructural Analysis][347H stainless steel][Deep learning][Multimodal microscopy]
• https://arxiv.org/abs/2305.15370[Deep learning][Non-local and scalable energy functionals][Quantum Ising model]
• https://arxiv.org/abs/2305.19516[Deep learning inter-atomic potential][Irradiation damage][3C-SiC]
• https://arxiv.org/abs/2305.19302[Smooth][Exact rotational symmetrization][Deep learning][Point cloud]
• https://arxiv.org/abs/2306.00797[Microstructure quality control][Steel][Deep learning]
• https://arxiv.org/abs/2306.08285[Bonding-aware materials representation][Deep learning atomistic model]
• https://arxiv.org/abs/2405.04967[MatterSim][Deep learning atomistic model][Across elements, temperatures and pressures]
• https://arxiv.org/abs/2408.02292[Learning atoms][Crystal structure]
• https://arxiv.org/abs/2307.04283[Deep learning][CALPHAD modeling][Universal parameter learning][Solely][Chemical formula]
• https://arxiv.org/abs/2307.06322[Deep learning][Crystalline defect][TEM image][Solution][Problem]["Never Enough Training Data"]
• https://arxiv.org/abs/2308.11221[Ferroelectric domain][Switching dynamics][Curved In₂Se₃][First principles][Deep learning molecular dynamics simulation]
• https://arxiv.org/abs/2309.01995[Photonic structures optimization][Highly data-efficient deep learning][Application][Nanofin][Annular groove phase mask]
• https://arxiv.org/abs/2310.01117[Predicting emergence][Crystal][Amorphous matter][Deep learning]
• https://arxiv.org/abs/2310.08302[Deep learning][EELS hyperspectral images unmixing][Autoencoder]
• https://arxiv.org/abs/2310.08618[Deep learning][Nano-photonic material][The solution to everything!?]
• https://arxiv.org/abs/2310.18907[Topological, or Non-topological?][Deep learning based prediction]
• https://arxiv.org/abs/2412.13012[Deep learning based superconductivity][Prediction and experimental tests]
• https://arxiv.org/abs/2311.11665[Enhancing crystal structure prediction][Leveraging computational][Experimental data][Combination][Deep learning][Optimization algorithm]
• https://arxiv.org/abs/2311.13170[Iterative deep learning procedure][Determining electron scattering cross-section][Transport coefficient]
• https://arxiv.org/abs/2311.14936[Single-image][Deep learning][Precise atomic defects identification]
• https://arxiv.org/abs/2212.04457[Spatio-temporal super-resolution][Dynamical system][Physics-informed deep-learning]
• https://arxiv.org/abs/2312.02871[Attention-enhanced neural differential equation][Physics-informed deep learning][Ion transport]
• https://arxiv.org/abs/2410.23667[Projected neural differential equation][Learning constrained dynamics]
• https://arxiv.org/abs/2405.08580[Importance][Hyper-parameter optimization][Training][Physics-informed deep learning network]
• https://arxiv.org/abs/2411.02411[SK-PINN][Accelerated physics-informed deep learning][Smoothing kernel gradient]
• https://arxiv.org/abs/2312.05160[Detecting atomic scale surface defect][STM][TMDs][Ensemble deep learning]
• https://arxiv.org/abs/2312.09133[Deep learning][Plasma plume image sequences][Anomaly detection][Prediction][Growth kinetics][Pulsed laser deposition]
• https://arxiv.org/abs/2401.02021[Accelerated band offset prediction][Semiconductor interface][DFT][Deep learning]
• https://arxiv.org/abs/2401.16611[Accelerating superconductor discovery][Tempered deep learning][Electron-phonon spectral function]
• https://arxiv.org/abs/2401.17513[PNP ion channel deep learning solver][Local neural network][Finite element input data]
• https://arxiv.org/abs/2402.01508[Deep learning path-like collective variable][Enhanced sampling molecular dynamics]
• https://arxiv.org/abs/2402.01578[Predictive model][Deep learning algorithm][Tensile deformation][AlCoCuCrFeNi high-entropy alloy]
• https://arxiv.org/abs/2405.18489[Predicting ground state properties][Constant sample complexity][Deep learning algorithm]
• https://arxiv.org/abs/2402.18830[Training-set-free two-stage deep learning][Spectroscopic data de-noising]
• https://arxiv.org/abs/2403.07163[Water isotope separation][Deep learning][Catalytically active ultrathin membrane]
• https://arxiv.org/abs/2403.12659[Zeolite adsorption property prediction][Deep learning]
• https://arxiv.org/abs/2403.13675[Bridging deep learning force field][Electronic structure][Physics-informed approach]
• https://arxiv.org/abs/2404.04810[AlphaCrystal-II][Distance matrix][Crystal structure prediction][Deep learning]
• https://arxiv.org/abs/2404.17130[Ultralow-power single-sensor-based E-nose system][Powered][Duty cycling][Deep learning][Real-time gas identification]
• https://arxiv.org/abs/2406.08191[Deep learning][Otical spectra][Semiconductors and insulators]
• https://arxiv.org/abs/2406.07761[Deep learning][Structural morphology][Imaged by scanning X-ray diffraction microscopy]
• https://arxiv.org/abs/2406.11134[Emergent Wigner phase][Moire superlattice][Deep learning]
• https://arxiv.org/abs/2406.17561[Improving density matrix electronic structure method][Deep learning]
• https://arxiv.org/abs/2407.00707[Deep learning quantum Monte Carlo][Solid]
• https://arxiv.org/abs/2407.12972[Deep learning][Quantitative dynamic fragmentation analysis]
• https://arxiv.org/abs/2407.13994[Evidential deep learning][Interatomic potential]
• https://arxiv.org/abs/2407.15214[Physical encoding][OOD Performance][Deep learning materials property prediction]
• https://arxiv.org/abs/2407.17290[Searching][New heavy fermion][Deep learning]
• https://arxiv.org/abs/2408.06237[Deep learning accelerated phase prediction][Refractory multi-principal element alloy]
• https://arxiv.org/abs/2411.08800[Deep learning accelerated quantum transport simulation][Nanoelectronics][From break junctions to field-effect transistors]
• https://arxiv.org/abs/2408.06657[Physics informed deep learning][Strain gradient continuum plasticity]
• https://arxiv.org/abs/2408.13111[Time series forecasting][Multiphase microstructure evolution][Deep learning]
• https://arxiv.org/abs/2409.04603[Colloidoscope][Detecting dense colloid][3d with Deep Learning]
• https://arxiv.org/abs/2410.04765[Molecular topological deep learning][Polymer property prediction]
• https://arxiv.org/abs/2410.14614[Temperature-dependent design][BCC high entropy alloy][Integrating deep learning][CALPHAD][Experimental validation]
• https://arxiv.org/abs/2410.20417[Large scale Raman spectrum calculation][Defective 2D material][Deep learning]
• https://arxiv.org/abs/2411.19124[Deep learning][GWP prediction][Framework][PCA][Quantile transformation][Ensemble modeling]
• https://arxiv.org/abs/2412.00819[Formation energy prediction][Material crystal structure][Deep learning]
• https://arxiv.org/abs/2412.06768[Vision transformer][Deep learning][Topological indicator][Majorana nanowire]
• https://arxiv.org/abs/2412.10838[Deep learning model][Colloidal nanocrystal synthesis]
• https://arxiv.org/abs/2412.13852[RadField3D][Data generator][Data format][Deep learning][Radiation-protection dosimetry][Medical application]
• https://arxiv.org/abs/2412.14795[Taming Landau level mixing][Fractional quantum Hall state][Deep learning]
• https://arxiv.org/abs/2501.10481[Domain knowledge][Deep learning][Solve applied inverse problems]
• https://arxiv.org/abs/2501.10911[Nanowire design][Deep learning][Energy efficient photonic technologies]
• https://arxiv.org/abs/2501.18495[Comprehensive enumeration][Three-dimensional photonic crystal][Deep learning assisted Fourier synthesis]
• https://arxiv.org/abs/2503.17945[Deep learning assisted denoising][Experimental micrograph]
• https://arxiv.org/abs/2501.18523[Deep learning][Reflection high-energy electron diffraction image][Predict cation ratio][Sr_xTi_1¡ÝxO₃ thin film]
• https://arxiv.org/abs/2502.14599[Enhancing nuclear cross-section prediction][Deep learning][DINo algorithm]
• https://arxiv.org/abs/2502.16558[Discovery][High-temperature superconducting ternary hydrides][Deep learning]
• https://arxiv.org/abs/2503.00470[Rapid morphology characterization][Two-dimensional TMDs][Lateral heterostructure][Deep learning]
• https://arxiv.org/abs/2503.08913[Deciphering][Scattering][Mechanically driven polymer][Deep learning]
• https://arxiv.org/abs/2503.09073[Pseudo-analytical density solution][Parameterized Fokker-Planck equation][Deep learning]
• https://arxiv.org/abs/2503.11568[Probing][Limit of heat transfer][Inorganic crystal][Deep learning]
• https://arxiv.org/abs/2503.11756[Deep learning sheds light][Integer and fractional topological insulators]
• https://arxiv.org/abs/1703.02309[Chemical composition alone][Predict Vibrational Free Energy][Entropies of Solids]
• https://arxiv.org/abs/2209.02362[Proper orthogonal descriptor][Efficient][Accurate interatomic potential]
• https://arxiv.org/abs/1612.04285[Learning physical descriptors][Materials science][Compressed sensing]
• https://arxiv.org/abs/2407.20273[Learning physics-consistent material behavior][Prior knowledge]
• https://arxiv.org/abs/2206.04117[Physics-guided descriptor][Prediction][Structural polymorph]
• https://arxiv.org/abs/2203.12353[Low-energy electron microscopy intensity-voltage data][Factorization][Sparse sampling][Classification]
• https://arxiv.org/abs/1807.10753[L₀L₁-norm compressive sensing paradigm][Construction of sparse predictive lattice model][Mixed integer quadratic programming]
• https://arxiv.org/abs/1911.04116[Sparse modeling][Quantum many-body Problem]
• https://arxiv.org/abs/2004.02426[Sparse modeling approach][Shear viscosity][Smeared correlation function]
• https://arxiv.org/abs/2007.03955[Sparse modeling][Large-scale quantum impurity model][Low symmetry]
• https://arxiv.org/abs/2106.12685[Efficient ab initio many-body calculation][Sparse modeling][Matsubara Green's function]
• https://arxiv.org/abs/2204.04475[Adaptive sparse sampling][Quasiparticle interference imaging]
• https://arxiv.org/abs/2205.14800[Sparse modeling approach][Quasiclassical theory][Superconductivity][J. Phys. Soc. Jpn. 92, 034703 (2023)]
• https://arxiv.org/abs/2206.04701[Efficient tensor network simulation][Quantum many-body physics][Sparse graph]
• https://arxiv.org/abs/2302.06844[Sparse random Fourier feature][Interatomic potential][High entropy alloy]
• https://arxiv.org/abs/2503.05993[SODAs][Sparse optimization][Discovery][Differential and algebraic equations]
• https://arxiv.org/abs/1612.04307[Uncovering structure-property relationships of materials][Subgroup discovery]
• https://arxiv.org/abs/2312.13289[Stoichiometry representation learning][Polymorphic crystal structure]
• https://arxiv.org/abs/2205.15071[Incorporation][Density scaling constraint][Density functional design][Contrastive representation learning]
• https://arxiv.org/abs/2406.16853[GeoMFormer][General architecture][Geometric molecular representation learning]
• https://arxiv.org/abs/2408.01426[MolTRES][Improving chemical language representation learning][Molecular property prediction]
• https://arxiv.org/abs/2110.11444[Density of states prediction][Materials discovery][Contrastive learning][Probabilistic embedding]
• https://arxiv.org/abs/2205.05607[Simple framework][Contrastive learning][Phases of matter]
• https://arxiv.org/abs/2211.13408[Graph contrastive learning]
• https://arxiv.org/abs/1711.06387[Compositional descriptor-based recommender system][Accelerating the materials discovery]
• https://arxiv.org/abs/1710.00659[Matrix- and tensor-based recommender systems][Discovery of currently unknown inorganic compounds]
• https://arxiv.org/abs/2207.10747[Transferable recommender approach][Selecting][Best density functional approximation][Chemical discovery]
• https://arxiv.org/abs/1701.03966[Descriptor][Thermal expansion][Solid]
• https://arxiv.org/abs/1802.02957[Simple descriptor][Energetics][fcc-bcc metal interface]
• https://arxiv.org/abs/1805.08155[Physical descriptor][Gibbs energy][Inorganic crystalline solid][Prediction][Temperature-dependent materials chemistry]
• https://arxiv.org/abs/1809.04750[Important descriptors][descriptor groups][Curie temperature][Rare-earth transition-metal binary alloy]
• https://arxiv.org/abs/1810.03368[Electronic structure based descriptor][Characterizing][Local atomic environment]
• https://arxiv.org/abs/1811.07730[High-entropy][High-hardness][Metal carbide][Discovered][Entropy descriptor]
• https://arxiv.org/abs/2304.12880[Hardness descriptor][Derived][Symbolic regression]
• https://arxiv.org/abs/1905.06048[Materials property prediction][Symmetry-labeled graph][Atomic-position independent descriptor]
• https://arxiv.org/abs/1902.05867[Assessing correlation][Perovskite catalytic performance][Electronic structure descriptor]
• https://arxiv.org/abs/2009.06461[Maximum volume simplex method][Automatic selection][Classification][Atomic environment][Environment descriptor compression]
• https://arxiv.org/abs/2405.08137[LATTE][Atomic environment descriptor][Cartesian tensor contraction]
• https://arxiv.org/abs/2206.12129[Advancing descriptor search][Materials science][Feature engineering][Selection strategies]
• https://arxiv.org/abs/2301.06554[Descriptor-enabled rational design][High-entropy material][Over vast chemical space]
• https://arxiv.org/abs/2304.06282[High-performance descriptor][Magnetic material][Accurate discrimination][Magnetic symmetries]
• https://arxiv.org/abs/2305.02620[Ultra-high-density double-atom catalyst][Spin moment][Activity descriptor][Oxygen reduction reaction]
• https://arxiv.org/abs/2305.11617[Structural dynamics descriptor][Metal halide perovskite]
• https://arxiv.org/abs/2306.08316[Fast reconstruction][Microstructure][Ellipsoidal inclusion][Analytical descriptor]
• https://arxiv.org/abs/2307.00978[Neighbors Map][Efficient atomic descriptor][Structural analysis]
• https://arxiv.org/abs/2404.12853[Perspective on descriptors][Mechanical behavior][Cubic transition-metal carbides and nitrides]
• https://arxiv.org/abs/2411.05179[Electronic density of states][Descriptor][Elastic bond strength][Ductility][Local lattice distortion][BCC refractory alloy]
• https://arxiv.org/abs/2501.14420[Chemical bonding based descriptor][Predicting][Impact of quantum nuclear and anharmonic effects][Hydrogen-based superconductor]
• https://arxiv.org/abs/2008.08818[Ensemble learning][Dissimilarity][Rare-earth transition metal binary alloy][Curie temperature]
• https://arxiv.org/abs/2206.08011[Hardness prediction][Age-hardening aluminum alloy][Ensemble learning]
• https://arxiv.org/abs/2308.10818[Interpretable ensemble learning][Materials property prediction][Classical interatomic potential][Carbon as an example]
• https://arxiv.org/abs/2002.10649[Data analytics approach][Predict the hardness][Copper matrix composite]
• https://arxiv.org/abs/1906.01085[Unavoidable disorder][Entropy][Multi-component systems]
• https://arxiv.org/abs/1811.06060[Hybrid generative-discriminative model][Inverse materials design]
• https://arxiv.org/abs/2405.03680[AtomGPT][Atomistic generative pre-trained transformer][Forward and inverse materials design]
• https://arxiv.org/abs/2401.06779[VAE][Modified 1-hot generative materials modeling][Inverse material design]
• https://arxiv.org/abs/2503.02978[Integrating predictive][Generative capabilities][Latent space design][DKL-VAE model]
• https://arxiv.org/abs/2409.06740[Data-efficient][Interpretable inverse materials design][Disentangled variational autoencoder]
• https://arxiv.org/abs/2502.18127[Inverse materials design][Large language model-assisted generative framework]
• https://arxiv.org/abs/2106.10557[Learning][Delayed reward][Inverse defect design][2D material]
• https://arxiv.org/abs/2201.11591[Inversion][Chemical environment representation]
• https://arxiv.org/abs/2203.07157[Inverse Hamiltonian design][Automatic differentiation]
• https://arxiv.org/abs/2005.07609[Inverse design of crystal][Generalized invertible crystallographic representation]
• https://arxiv.org/abs/1908.07959[Inverse structural design][Graphene/Boron nitride hybrid][Regressional GAN]
• https://arxiv.org/abs/2310.07836[Revolutionising inverse design][Magnesium alloy][Generative adversarial network]
• https://arxiv.org/abs/2005.04840[Inverse design][Lightweight broadband reflector][Efficient lightsail propulsion]
• https://arxiv.org/abs/1803.06061[Inverse design][Compact multi-wavelength waveguide][Cavity coupler]
• https://arxiv.org/abs/1904.10329[Classification scheme][Inverse design][Molecule][Targeted electronic properties][Atomicity]
• https://arxiv.org/abs/2004.01579[Inverse design][Ultralow lattice thermal conductivity][Lone pair cation coordination environment]
• https://arxiv.org/abs/2006.03413[Accelerating][Copolymer][Inverse design][AI Gaming algorithm]
• https://arxiv.org/abs/2007.07070[Inverse design][Graphene-based quantum transducer][Neuroevolution]
• https://arxiv.org/abs/2007.11228[CCDCGAN][Inverse design][Crystal structure]
• https://arxiv.org/abs/2008.02349[Inverse design][Surface][Deployable origami]
• https://arxiv.org/abs/2008.05298[Forward and inverse design][Kirigami][Supervised autoencoder]
• https://arxiv.org/abs/2008.10618[Inverse design][Composite metal oxide optical material][Deep transfer learning]
• https://arxiv.org/abs/2503.12099[Automatic characterization][Fluxonium superconducting qubits parameter][Deep transfer learning]
• https://arxiv.org/abs/2204.00433[Inverse design][Experimental verification][Bianisotropic metasurface][Optimization and machine learning]
• https://arxiv.org/abs/2104.08040[Inverse design][Crystal structure][Multicomponent system]
• https://arxiv.org/abs/2109.05484[Inverse design][Reconfigurable piezoelectric topological phononic plate]
• https://arxiv.org/abs/2112.00865[Inverse design][Strained graphene surface][Electron control]
• https://arxiv.org/abs/2106.03013[Inverse design][Two-dimensional material][Invertible neural networks]
• https://arxiv.org/abs/2104.06632[Inverse design][Glass structure][Deep graph neural network]
• https://arxiv.org/abs/2405.18891[Inverse design][Promising alloy][Electrocatalytic CO₂ reduction][Generative graph neural network][Combined][Bird swarm algorithm]
• https://arxiv.org/abs/2205.07601[Volumetric-mapping-based inverse design][3D architected material][Mobility control][Topology reconstruction]
• https://arxiv.org/abs/2206.01043[Multi-objective inverse design][Solid-state quantum emitter][Single-photon source]
• https://arxiv.org/abs/2209.06608[Generation][Spin-wave pulse][Inverse design]
• https://arxiv.org/abs/2212.12106[Continuous action space tree search][Inverse design (CASTING) Framework][Materials discovery]
• https://arxiv.org/abs/2304.05422[Differentiable graph-structured model][Inverse design][Lattice material]
• https://arxiv.org/abs/2305.10020[Inverse design][All-dielectric metasurface][Bound state][Continuum]
• https://arxiv.org/abs/2306.06585[Inverse design][Power-law nonlinear constitutive response][Stiffness normalization]
• https://arxiv.org/abs/2307.13581[Comparing forward][Inverse design paradigms][Case study][Refractory high-entropy alloy]
• https://arxiv.org/abs/2310.03537[Inverse design][Self-folding 3D shell][Phys. Rev. Lett. 132, 118201(2024)]
• https://arxiv.org/abs/2310.10995[Inverse design][Pyrochlore lattice][DNA origami][Model-driven experiment]
• https://arxiv.org/abs/2310.14775[Many-body quantum interference route][Two-channel Kondo effect][Inverse design][Molecular junction]
• https://arxiv.org/abs/2501.02393[Graph-aware isomorphic attention][Adaptive dynamics][Transformer]
• https://arxiv.org/abs/2311.13812[Mechanical characterization][Inverse design][Stochastic architected metamaterial][Neural operator]
• https://arxiv.org/abs/2402.09031[Inverse design][Casimir-Lifshitz force][Heterogeneous gapped metal surface]
• https://arxiv.org/abs/2402.13054[Inverse design][Spinodoid structure][Bayesian optimization]
• https://arxiv.org/abs/2402.11600[AI-assisted inverse design][Sequence-ordered high intrinsic thermal conductivity polymer]
• https://arxiv.org/abs/2403.15887[Tutorial][AI-assisted exploration][Active design][Polymer][High intrinsic thermal conductivity]
• https://arxiv.org/abs/2402.16452[Multiscale experiment][Predictive modelling][Inverse design][Failure mitigation][Additively manufactured lattice]
• https://arxiv.org/abs/2403.15725[Customizable wave tailoring material][Coupling nonlinear inverse design][Two scales]
• https://arxiv.org/abs/2406.09263[Generative inverse design][Crystal structure][Diffusion model][Transformer]
• https://arxiv.org/abs/2502.06485[WyckoffDiff][Generative diffusion model][Crystal symmetry]
• https://arxiv.org/abs/2406.10566[Inverse design][Programmable shape-morphing kirigami structure]
• https://arxiv.org/abs/2407.00729[Discovering one molecule][Out of a million][Inverse design][Molecular hole transporting semiconductor][Perovskite solar cell]
• https://arxiv.org/abs/2407.10273[Quantized inverse design][Photonic integrated circuit]
• https://arxiv.org/abs/2407.16736[Inverse design][Polaritonic device]
• https://arxiv.org/abs/2408.06300[Inverse designing metamaterial][Programmable nonlinear functional responses][Graph space]
• https://arxiv.org/abs/2408.07608[MatterGPT][Generative transformer][Multi-property inverse design][Solid-state material]
• https://arxiv.org/abs/2409.03275[Inverse design][Winding tuple][Non-Hermitian topological edge mode]
• https://arxiv.org/abs/2409.18284[Inverse design][Unitary transmission matrices][Silicon photonic coupled waveguide array][Neural adjoint model]
• https://arxiv.org/abs/2410.05833[dCG][Differentiable connected geometries][Multi-domain optimization][Inverse design]
• https://arxiv.org/abs/2410.22111[Inverse design method][Enhanced sampling][Complex open crystal][Application][Novel zeolite self-assembly][Coarse-grained model]
• https://arxiv.org/abs/2410.24132[invrs-gym][Toolkit][Nanophotonic inverse design research]
• https://arxiv.org/abs/2411.08210[BOSON¡Ý1][Understanding][Enabling physically-robust photonic inverse design][Adaptive variation-aware subspace optimization]
• https://arxiv.org/abs/2411.09429[AI-driven inverse design][Materials: Past, present and future]
• https://arxiv.org/abs/2412.12122[Inverse design][Potential metastructure][Inspired][Indian medieval architectural element]
• https://arxiv.org/abs/2412.14071[Inverse design][Nonlinear mechanics][Bio-inspired material][Interface engineering][Bayesian optimization]
• https://arxiv.org/abs/2501.05900[Inverse design][3D nanophotonic device][Structural integrity][Auxiliary thermal solver]
• https://arxiv.org/abs/2501.12825[Inverse design][Chiral structure][Giant helical dichroism]
• https://arxiv.org/abs/2501.18054[Ultrafast inverse design][Electromagnetic device]
• https://arxiv.org/abs/2502.11934[Evaluation][Machine learning technique][Conditional generative adversarial network][Inverse design]
• https://arxiv.org/abs/2502.18857[Physics-aware inverse design][Nanowire single-photon avalanche detector][Deep learning]
• https://arxiv.org/abs/2202.06699[Inverse-designed metaphotonics][Hypersensitive detection]
• https://arxiv.org/abs/2403.17724[Magnonic inverse-design processor]
• https://arxiv.org/abs/2411.17546[Realization][Inverse-design magnonic logic gate]
• https://arxiv.org/abs/2411.19109[Inverse-design topology optimization][Magnonic device][Level-set method]
• https://arxiv.org/abs/2409.16572[Efficient and generalizable nested Fourier-DeepONet][Three-dimensional geological carbon sequestration]
• https://arxiv.org/abs/2203.04491[Towards large-scale][Spatio-temporally resolved diagnosis][Electronic density of states][DNN]
• https://arxiv.org/abs/2408.03198[Coercivity influence][Nanostructure][SmCo-1:7 magnet][Machine learning][High-throughput micromagnetic data]
• https://arxiv.org/abs/2408.17023[Physics-integrated neural network][Quantum transport prediction][Field-effect transistor]
• https://arxiv.org/abs/1809.03555[Deep neural network][Inverse design][Integrated nanophotonic device]
• https://arxiv.org/abs/2201.10387[Deep neural network][Prediction][Optical properties][Free-form inverse design][Metamaterial]
• https://arxiv.org/abs/2204.10430[Simple denoising approach][Exploit multi-fidelity data][Machine learning materials properties]
• https://arxiv.org/abs/2302.02303[Inorganic synthesis recommendation][Machine learning materials similarity][Scientific literature]
• https://arxiv.org/abs/2406.15650[Machine learning materials properties][Accurate prediction][Uncertainty estimate][Domain guidance][Persistent online accessibility]
• https://arxiv.org/abs/1901.00081[Machine learning materials physics][Integrable deep neural network][Scale bridging][Learning free energy function]
• https://arxiv.org/abs/1901.00524[Machine learning materials physics][Deep neural network][Trained][Elastic free energy data][Martensitic microstructure][Predict homogenized stress field][High accuracy]
• https://arxiv.org/abs/2311.14972[Leveraging neural network][Attention mechanism][High-order accuracy][Charge density][Particle-in-cell simulation]
• https://arxiv.org/abs/2410.14986[NeuralMAG][Fast and generalizable micromagnetic simulation][Deep neural net]
• https://arxiv.org/abs/2503.10646[Phase diagrams construction][Mean-field renormalization][Neural network fitting]
• https://arxiv.org/abs/2010.04905[Accelerating finite-temperature Kohn-Sham density functional theory][Deep neural network]
• https://arxiv.org/abs/1811.08425[Fast classification][Small X-ray diffraction dataset][Data augmentation][Deep neural network]
• https://arxiv.org/abs/1902.02528[In-Memory][Error-Immune][Differential RRAM implementation][Binarized Deep Neural Network]
• https://arxiv.org/abs/1906.11434[Deep neural network][Wannier function center]
• https://arxiv.org/abs/1909.05529[Deep neural network][X-ray photoelectron spectroscopy][Data analysis]
• https://arxiv.org/abs/1910.00252[Coarse-grained deep neural network model][Liquid water]
• https://arxiv.org/abs/1912.05044[Unified deep neural network][Potential capable][Predicting thermal conductivity][Silicon in different phases]
• https://arxiv.org/abs/2004.07369[Raman spectrum][Polarizability][Liquid water][Deep neural network]
• https://arxiv.org/abs/2004.08247[Real-time sparse-sampled Ptychographic imaging][Deep neural network]
• https://arxiv.org/abs/2010.1353[Learning hidden elasticity][Deep neural network]
• https://arxiv.org/abs/2012.03019[Deep neural network][Ppredict][Quantum many-body Hamiltonian][Learning visualized wave-function]
• https://arxiv.org/abs/2101.07770[Developing][Deep neural network][Denoise time-resolved][In situ][ETEM][Catalyst nanoparticle]
• https://arxiv.org/abs/2101.11902[Learning hidden chemistry][Deep neural network]
• https://arxiv.org/abs/2103.14174[Copolymer informatics][Multi-task deep neural network]
• https://arxiv.org/abs/2104.03786[Deep neural network representation][Density functional theory Hamiltonian]
• https://arxiv.org/abs/2105.01384[Deep neural network][Predictive-generative framework][Designing composite materials]
• https://arxiv.org/abs/2106.03126[Predicting quantum potential][Deep neural network][Metropolis sampling]
• https://arxiv.org/abs/2111.05603[Pairwise interaction][Potential energy surface][Atomic force][Deep neural network]
• https://arxiv.org/abs/2111.07284[Energy efficient learning][Low resolution stochastic domain wall synapse][Deep neural network]
• https://arxiv.org/abs/2212.06084[Hardware-efficient learning][Quantum many-body state]
• https://arxiv.org/abs/2403.14131[Efficient learning strategy][Predicting glass forming ability][Imbalanced dataset][Bulk metallic glasses]
• https://arxiv.org/abs/2111.13956[Understanding anharmonic effect][Hydrogen desorption characteristics][Mg_nH_2n nanocluster][Ab initio trained deep neural network]
• https://arxiv.org/abs/2112.06142[Semi-supervised teacher-student][Deep neural network][materials discovery]
• https://arxiv.org/abs/2203.12033[Bioplastic design][Multitask deep neural network]
• https://arxiv.org/abs/2204.13912[Quantitative prediction][Fracture toughness (KIc)][Polymer][Fractography][Deep neural network]
• https://arxiv.org/abs/2208.00349[Deep neural network][Disordered Structure][Glasses?]
• https://arxiv.org/abs/2211.07749[High-accuracy variational Monte Carlo][Frustrated magnet][Deep neural network]
• https://arxiv.org/abs/2211.08482[DyFraNet][Forecasting][Backcasting][Dynamic fracture mechanics][Space and time using a 2D-to-3D deep neural network]
• https://arxiv.org/abs/2212.13678[HubbardNet][Efficient prediction][Bose-Hubbard model spectrum][Deep neural network]
• https://arxiv.org/abs/2302.08965[A simple method][Multi-body wave function][Ground and low-lying excited states][Deep neural network]
• https://arxiv.org/abs/2302.12030[Back-end][Flexible substrate compatible analog ferroelectric field effect transistor][Accurate online training][Deep neural network accelerator]
• https://arxiv.org/abs/2303.15538[GlassNet][Multitask deep neural network][Predicting many glass properties]
• https://arxiv.org/abs/2304.12400[Generative discovery][Novel chemical design][Diffusion modeling][Transformer][Deep neural network][Application][Deep eutectic solvent]
• https://arxiv.org/abs/2305.02954[Quantifying][Magnetic interaction][Governing][Chiral spin texture][Deep neural network]
• https://arxiv.org/abs/2311.16135[Deep neural network][Uncertain stress function][Extension][Impact mechanics]
• https://arxiv.org/abs/2311.18686[Highly efficient][Transferable interatomic potential][alpha-iron][alpha-iron/hydrogen binary system][Deep neural network]
• https://arxiv.org/abs/2405.15502[Unsupervised deep neural network approach][To solve Fermionic systems]
• https://arxiv.org/abs/2405.15488[Unsupervised deep neural network approach][To Solve Bosonic systems]
• https://arxiv.org/abs/2412.03090[Deep neural network approach][Solve the Dirac equation]
• https://arxiv.org/abs/2502.05201[Harnessing artificial intelligence][Modeling amorphous][Amorphous porous palladium][Deep neural network approach]
• https://arxiv.org/abs/2406.01222[Symmetry enforced solution][Many-body Schrödinger equation][Deep neural network]
• https://arxiv.org/abs/2407.00294[Deep neural network][Symplectic preservation properties]
• https://arxiv.org/abs/2407.12293[Multi evolutional deep neural network][Multi-EDNN]
• https://arxiv.org/abs/2409.02339[Data-driven 2D stationary quantum droplet][Wave propagation][Amended GP equation][Two potentials][Deep neural networks learning]
• https://arxiv.org/abs/2412.08822[Predicting performance][Microfluidic-based alginate microfiber][Feature-supplemented deep neural network]
• https://arxiv.org/abs/2502.00798[Deep neural network][Phonon-assisted optical spectra][Semiconductor]
• https://arxiv.org/abs/2503.13067[Deep neural network-based voltage prediction][Alkali-metal-ion battery material]
• https://arxiv.org/abs/2503.13585[Solving fractional electron state][Twisted MoTe₂][Deep neural network]
• https://arxiv.org/abs/2209.09247[Weak-signal extraction][Enabled][Deep-neural-network denoising][Diffraction data]
• https://arxiv.org/abs/2203.01262[Viscosity][Water][First-principles][Deep-neural-network simulation]
• https://arxiv.org/abs/2303.18055[Deep neural operator][Learning transient response][Interpenetrating phase composite][Subject to dynamic loading]
• https://arxiv.org/abs/2111.05885[Predicting lattice phonon vibrational frequencies][Deep graph neural network]
• https://arxiv.org/abs/2502.13025[Agentic deep graph reasoning yield][Self-organizing knowledge network]
• https://arxiv.org/abs/2010.05851[Graph neural network][Accurate][Interpretable prediction][Polycrystalline material]
• https://arxiv.org/abs/2010.159[Graph neural network][Metal organic framework][Potential energy approximation]
• https://arxiv.org/abs/2102.11023[Predicting material properties][3D Graph neural network][Invariant local descriptor]
• https://arxiv.org/abs/2106.01829[Atomistic line graph neural network][Improved materials property prediction]
• https://arxiv.org/abs/2107.05142[Graph neural network][Emulating crack coalescence][Propagation][Brittle material]
• https://arxiv.org/abs/2109.12283[Scalable deeper graph neural network][High-performance materials property prediction]
• https://arxiv.org/abs/2501.03278[DenseGNN][Universal and scalable deeper graph neural network][High-performance property prediction][Crystals and molecules]
• https://arxiv.org/abs/2109.14012[Atomistic graph neural network][Application][bcc iron]
• https://arxiv.org/abs/2111.05557[Piezoelectric modulus prediction][Machine learning][Graph neural network]
• https://arxiv.org/abs/2108.02077[Entropy-based active learning][Graph neural network surrogate model][Materials Properties]
• https://arxiv.org/abs/2111.14712[Prediction][Large magnetic moment][Graph neural network][Random forest]
• https://arxiv.org/abs/2409.19209[Boosting SISSO performance][Small sample dataset][Random forests prescreening][Complex feature selection]
• https://arxiv.org/abs/2112.10231[Graph neural network prediction][Metal organic framework][CO₂ Adsorption]
• https://arxiv.org/abs/2201.05770[Edge-based tensor prediction][Graph neural network]
• https://arxiv.org/abs/2201.08348[Prediction][Electron density of states][Crystalline compound][Atomistic line graph neural network][ALIGNN]
• https://arxiv.org/abs/2202.01954[Multi-task graph neural network][Simultaneous prediction][Global and atomic properties][Ferromagnetic system]
• https://arxiv.org/abs/2203.10177[Characterizing disorder][Atomic environment][Physics-preserving graph neural network]
• https://arxiv.org/abs/2205.05475[Efficient prediction][Density functional theory Hamiltonian][Graph neural network]
• https://arxiv.org/abs/2205.06324[Graph neural network modeling][Grain-scale anisotropic elastic behavior][Simulated][Measured][Microscale data]
• https://arxiv.org/abs/2501.18189[Neural network modeling][Microstructure complexity][Digital libraries]
• https://arxiv.org/abs/2206.12867[Edge direction-invariant][Graph neural network][Molecular dipole moments prediction]
• https://arxiv.org/abs/2207.12510[Rapid prediction][Phonon structure][Atomistic line graph neural network][ALIGNN]
• https://arxiv.org/abs/2208.03296[Accelerating discrete dislocation dynamics simulation][Graph neural network]
• https://arxiv.org/abs/2208.05039[Examining graph neural network][Crystal structure][Limitation][Opportunities][Capturing periodicity]
• https://arxiv.org/abs/2208.09481[Graph neural network][Materials science and chemistry]
• https://arxiv.org/abs/2208.14364[Dynamic][Adaptive mesh-based graph neural network framework][Simulating displacement][Crack field][Phase field model]
• https://arxiv.org/abs/2411.13670[Graph neural network framework][Energy mapping][Hybrid monte-carlo molecular dynamics simulation][Medium entropy alloy]
• https://arxiv.org/abs/2209.05554[Unified graph neural network force-field][Periodic table]
• https://arxiv.org/abs/2209.05583[Polycrystal graph neural network]
• https://arxiv.org/abs/2209.07300[Multi-task mixture density graph neural network][Predicting][Cu-based single-atom alloy catalyst][CO₂ reduction reaction]
• https://arxiv.org/abs/2209.07567[Prediction][CO₂ adsorption][Nano-pore][Graph neural network]
• https://arxiv.org/abs/2209.13557[Polymer informatics][At-scale][Multitask graph neural network]
• https://arxiv.org/abs/2211.03563[Self-supervised representation][Node embedding graph neural network][Accurate][Multi-scale analysis]
• https://arxiv.org/abs/2212.10948[Prediction][Steel nanohardness][Graph neural network][Surface polycrystallinity map]
• https://arxiv.org/abs/2301.12059[Potential energy surface prediction][Alumina polymorph][Graph neural network]
• https://arxiv.org/abs/2101.03164[SE(3)-equivariant graph neural network][Data-efficient and accurate interatomic potential]
• https://arxiv.org/abs/2306.12818[StrainNet][Predicting crystal structure elastic properties][SE(3)-equivariant graph neural network]
• https://arxiv.org/abs/2403.11347[Phonon prediction][E(3)-equivariant graph neural network]
• https://arxiv.org/abs/2306.14238[GPT-assisted learning][Structure-property relationship][Graph neural network][Application][Rare-earth doped phosphor]
• https://arxiv.org/abs/2307.15242[Universal equivariant graph neural network][Elasticity tensor][Any crystal system]
• https://arxiv.org/abs/2310.02428[EGraFFBench][Evaluation][Equivariant graph neural network force field][Atomistic simulation]
• https://arxiv.org/abs/2409.09931[Generalizability][Graph neural network force field][Predicting solid-state properties]
• https://arxiv.org/abs/2405.07915[Discovery][Highly anisotropic dielectric crystal][Equivariant graph neural network]
• https://arxiv.org/abs/2406.03563[Equivariant graph neural network][Prediction][Tensor material properties][Crystal]
• https://arxiv.org/abs/2412.03541[Dielectric tensor][Perovskite oxide][Finite temperature][Equivariant graph neural network potential]
• https://arxiv.org/abs/2412.18263[Free][Design space][Equivariant graph neural network][High-rank irreducible cartesian tensor decomposition][Bases of equivariant space]
• https://arxiv.org/abs/2501.05341[Discovery][Spin-crossover candidate][Equivariant graph neural network]
• https://arxiv.org/abs/2307.03907[Relationship][Activated H₂ bond length][Adsorption distance][MXene][Identified][Graph neural network][Resonating valence bond theory]
• https://arxiv.org/abs/2307.05299[Discovering symbolic laws directly][Trajectories][Hamiltonian graph neural network]
• https://arxiv.org/abs/2308.11160[Predicting transition temperature][Superconductor][Graph neural network]
• https://arxiv.org/abs/2309.04811[Chemical Properties][Graph neural network-predicted electron densities]
• https://arxiv.org/abs/2410.13768[Rapid and automated alloy design][Graph neural network-powered LLM-driven multi-agent system]
• https://arxiv.org/abs/2309.06423[Accelerating defect prediction][Semiconductor][Graph neural network]
• https://arxiv.org/abs/2310.06995[Accelerated modelling][Interface][Electronic device][Graph neural network]
• https://arxiv.org/abs/2310.15153[Accelerate microstructure evolution simulation][Graph neural network][Adaptive spatiotemporal resolution]
• https://arxiv.org/abs/2310.19274[Prediction][Effective elastic moduli][Rocks using graph neural network]
• https://arxiv.org/abs/2310.19500[Coarse-grained crystal graph neural network][Reticular materials design]
• https://arxiv.org/abs/2311.00939[Accelerated data-driven discovery][Screening][Two-dimensional magnet][Graph neural network]
• https://arxiv.org/abs/2311.02143[Pairing-based graph neural network][Simulating quantum material]
• https://arxiv.org/abs/2402.18379[Embracing disorder][Quantum materials design]
• https://arxiv.org/abs/2311.07548[Interpretable fine-tuning][Graph neural network surrogate model]
• https://arxiv.org/abs/2403.07795[Fine-tuning neural network][Quantum state]
• https://arxiv.org/abs/2401.11768[ADA-GNN][Atom-distance-angle graph neural network][Crystal material property prediction]
• https://arxiv.org/abs/2401.16271[AnisoGNN][Graph neural network][Generalizing][Anisotropic properties][Polycrystal]
• https://arxiv.org/abs/2401.16565[Towards accurate prediction][Configurational disorder properties][Material][Graph neural network]
• https://arxiv.org/abs/2403.15266[Graph neural network coarse-grain force field][Molecular crystal RDX]
• https://arxiv.org/abs/2404.08782[Phase transition][Correlated system][Graph neural network][Quantum embedding techniques]
• https://arxiv.org/abs/2405.02078[CatTSunami][Accelerating transition state energy calculation][Pre-trained graph neural network]
• https://arxiv.org/abs/2503.01227[Pre-training graph neural network][Structural fingerprint][Materials discovery]
• https://arxiv.org/abs/2405.00814[Solving Maxwell's equation][Non-trainable graph neural network message passing]
• https://arxiv.org/abs/2405.05205[Hybrid quantum graph neural network][Molecular property prediction]
• https://arxiv.org/abs/2405.08628[Chemical-motif characterization][Short-range order][E(3)-equivariant graph neural network]
• https://arxiv.org/abs/2405.11502[CTGNN][Crystal transformer graph neural network][Crystal material property prediction]
• https://arxiv.org/abs/2411.16483[Graph transformer network][Accurate band structure prediction][End-to-end approach]
• https://arxiv.org/abs/2405.16511[SE3Set][Harnessing equivariant hypergraph neural network][Molecular representation learning]
• https://arxiv.org/abs/2406.08682[FIP-GNN][Graph neural network][Scalable prediction][Grain-level fatigue indicator parameter]
• https://arxiv.org/abs/2406.16654[Ensemble-embedding graph neural network][Direct prediction][Optical spectra][Crystal structure]
• https://arxiv.org/abs/2407.05204[Leveraging persistent homology feature][Accurate defect formation energy prediction][Graph neural network]
• https://arxiv.org/abs/2407.10458[Predicting doping strategies][Ternary nickel-cobalt-manganese cathode material][Enhance battery performance][Graph neural network]
• https://arxiv.org/abs/2407.10844[Rotationally invariant latent distance][Uncertainty estimation][Relaxed energy prediction][Graph neural network potential]
• https://arxiv.org/abs/2410.01650[Accessing numerical energy Hessians][Graph neural network potential][Their application][Heterogeneous catalysis]
• https://arxiv.org/abs/2408.16337[Graph neural network][Work][High entropy alloy]
• https://arxiv.org/abs/2408.16698[SympGNNs][Symplectic graph neural network][Identifiying high-dimensional Hamiltonian system][Node classification]
• https://arxiv.org/abs/2409.05306[Investigating material interface diffusion phenomena][Graph neural network][Applied material]
• https://arxiv.org/abs/2409.07339[Descriptors-free collective variable][Geometric graph neural network]
• https://arxiv.org/abs/2409.07664[Rapid assessment][Stable crystal structure][Single phase high entropy alloy][Graph neural network][Surrogate modelling]
• https://arxiv.org/abs/2409.13851[Learning ordering][Crystalline material][Symmetry-aware graph neural network]
• https://arxiv.org/abs/2409.15800[MGNN][Moment graph neural network][Universal molecular potential]
• https://arxiv.org/abs/2410.01657[Scalable and consistent graph neural network][Distributed mesh-based data-driven modeling]
• https://arxiv.org/abs/2502.06323[Physics-based data-driven model][CO₂ gas diffusion electrode][Drive automated laboratories]
• https://arxiv.org/abs/2410.16066[Accelerating discovery][Extreme lattice thermal conductivity][Crystal attention graph neural network][CATGNN][Chemical bonding intuitive descriptor]
• https://arxiv.org/abs/2410.17631[Exploring structure diversity][Atomic resolution microscopy][Graph neural network]
• https://arxiv.org/abs/2411.01848[Physics-constrained graph neural network][Spatio-temporal prediction][Drop impact][OLED display panel]
• https://arxiv.org/abs/2411.02331[Graph neural network][Based deep learning][Predicting structural and electronic properties]
• https://arxiv.org/abs/2411.01578[Integrating graph neural network][Many-body expansion theory][Potential energy surface]
• https://arxiv.org/abs/2411.02579[EOSnet][Embedded overlap structure][Graph neural network][Predicting material properties]
• https://arxiv.org/abs/2411.14159[Predicting rigidity][Connectivity percolation][Disordered particulate network][Graph neural network]
• https://arxiv.org/abs/2411.17164[X-MeshGraphNet][Scalable multi-scale graph neural network][Physics simulation]
• https://arxiv.org/abs/2412.02967[Graph neural network approach][Dispersed system]
• https://arxiv.org/abs/2502.13797[Extending][RANGE of graph neural network][Relaying attention node][Global encoding]
• https://arxiv.org/abs/2502.16189[Co-evolution-based metal-binding residue prediction][Graph neural network]
• https://arxiv.org/abs/1702.05771[Predicting electronic structure properties][Transition metal complexes][Neural Network]
• https://arxiv.org/abs/1803.03039[Design][Nickel-base superalloy][Neural network]
• https://arxiv.org/abs/2011.07929[Equivalence][Molecular graph convolution][Molecular wave function][Poor basis set]
• https://arxiv.org/abs/2303.04791[Ewald-based long-range message passing][Molecular graph]
• https://arxiv.org/abs/2312.16473[MolSets][Molecular graph deep sets learning][Mixture property modeling]
• https://arxiv.org/abs/2307.05392[Simplicial message passing][Chemical property prediction]
• https://arxiv.org/abs/1807.04955[Self-learning Monte Carlo method][Behler-Parrinello neural network]
• https://arxiv.org/abs/2306.11527[Self-learning Monte Carlo][Equivariant transformer]
• https://arxiv.org/abs/2501.19216[E2Former][Linear-time efficient and equivariant transformer][Scalable molecular modeling]
• https://arxiv.org/abs/1905.10407[De novo exploration][Self-guided learning][Potential-energy surface]
• https://arxiv.org/abs/2412.16736[Automated framework][Exploring][Learning potential-energy surface]
• https://arxiv.org/abs/2301.11543[Machine-guided design][Oxidation resistant superconductor][Quantum information application]
• https://arxiv.org/abs/1808.01696[Physically-informed artificial][Neural network][Atomistic modeling]
• https://arxiv.org/abs/1809.08221[Simulated Bloch oscillation][Strained graphene][Neural network]
• https://arxiv.org/abs/2207.11096[Seeing moire][Convolutional network learning][Twistronics]
• https://arxiv.org/abs/2403.19214[Convolutional network learning][Self-consistent electron density][Grid-projected atomic fingerprint]
• https://arxiv.org/abs/2411.12616[Crystal hypergraph convolutional network]
• https://arxiv.org/abs/1910.11516[Leveraging legacy data][Accelerate materials design][Preference learning]
• https://arxiv.org/abs/2305.01101[Leveraging language representation][Material recommendation][Ranking][Exploration]
• https://arxiv.org/abs/1807.09422[Solving frustrated quantum many-particle model][Convolutional neural network]
• https://arxiv.org/abs/2010.09485[Determination][Interface][Amorphous insulator][Crystalline 4H-SiC][Transmission electron microscope image][Convolutional neural network]
• T. Xie and J. C. Grossman, Phys. Rev. Lett. 120, 145301(2018)[Crystal graph convolutional][Neural network][Accurate][Interpretable][Prediction material properties]
• https://arxiv.org/abs/1807.03404[Hierarchical visualization][Materials space][Graph convolutional][Neural network]
• https://arxiv.org/abs/1810.06118[Learning to fail][Predicting fracture evolution][Brittle material][Recurrent graph convolutional neural network]
• https://arxiv.org/abs/1811.05660[MT-CGCNN][Integrating crystal graph convolutional neural network][Multitask learning][Material property prediction]
• https://arxiv.org/abs/2008.06415[Orbital graph convolutional neural network][Material property prediction]
• https://arxiv.org/abs/1906.05267[Developing][Improved crystal graph convolutional neural network framework][Accelerated materials discovery]
• https://arxiv.org/abs/2001.10908[Super resolution convolutional neural network][Feature extraction][Spectroscopic data]
• https://arxiv.org/abs/1811.06231[Graph convolutional Neural Network][Polymers Property Prediction]
• https://arxiv.org/abs/2003.08749[Reliable quality monitoring system][Additive manufacturing process][Deep convolutional neural network]
• https://arxiv.org/abs/2003.13425[Predicting elastic property][Electronic charge density][3D deep convolutional neural network]
• https://arxiv.org/abs/2107.04287[Iterative peak-fitting][Frequency-domain data][Deep convolution neural network]
• https://arxiv.org/abs/2003.13379[Global attention][Graph convolutional neural network][Improved materials property prediction]
• https://arxiv.org/abs/2006.15895[Ionic surfactant][Nafion][Convolutional neural network]
• https://arxiv.org/abs/2007.09932[Transfer learning][Materials informatics][Crystal graph convolutional neural network]
• https://arxiv.org/abs/2007.14144[Temperature-transferable coarse-graining][Ionic liquid][Dual graph convolutional neural network]
• https://arxiv.org/abs/2404.17584[Equivariant graph convolutional neural network][Representation][Homogenized anisotropic microstructural mechanical response]
• https://arxiv.org/abs/2402.11179[Uncertainty quantification][Graph convolution neural network model][Evolving Processes]
• https://arxiv.org/abs/2409.08940[Representing Born effective charge][Equivariant graph convolutional neural network]
• https://arxiv.org/abs/2410.11178[Combining reinforcement learning][Graph convolutional neural network][Efficient design][TiAl/TiAlN atomic-scale interface]
• https://arxiv.org/abs/1812.02949[Slab gaph convolutional neural ntwork][Discovery][N2 Electroreduction Catalyst]
• https://arxiv.org/abs/2009.14370[Hybrid convolutional neural network][PEPS wave function][Quantum many-particle state]
• https://arxiv.org/abs/2010.03675[Predicting][Mechanical Property][Microstructure image][Fiber-reinforced polymer][Convolutional neural network]
• https://arxiv.org/abs/2011.03474[Correlator convolutional neural network][Interpretable architecture][Image-like quantum matter data]
• https://arxiv.org/abs/2407.21502[Interpretable correlator transformer][Image-like quantum matter data]
• https://arxiv.org/abs/2011.12911[Learning crystal field parameter][Convolutional neural network]
• https://arxiv.org/abs/2103.00001[3D coherent x-ray imaging][Deep convolutional neural network]
• https://arxiv.org/abs/2103.02638[On-lattice voxelated convolutional neural network][Prediction][Phase diagram][Diffusion barrier][Cubic alloy]
• https://arxiv.org/abs/2106.16179[Convolutional neural network][Defect classification][Bragg coherent X-ray diffraction]
• https://arxiv.org/abs/2109.03020[Deep convolutional neural network][Predict][Elasticity tensor][Homogenization]
• https://arxiv.org/abs/2303.17025[Deep convolutional neural network][Restore][Single-shot electron microscopy image]
• https://arxiv.org/abs/2102.03877[Noise reduction][X-ray photon correlation spectroscopy][Convolutional neural network][Encoder-decoder model]
• https://arxiv.org/abs/2201.09672[DH-Net][Deformed microstructure homogenization][3D convolutional neural network]
• https://arxiv.org/abs/2203.06503[Convolutional neural network][Enable high-fidelity prediction][Path-dependent diffusion barrier spectra][Multi-principal element alloy]
• https://arxiv.org/abs/2205.01167[3D convolutional neural network][Dendrite segmentation][Fine-tuning][Hyperparameter optimization]
• https://arxiv.org/abs/2207.14603[Orthogonal spin current][Injected magnetic tunnel junction][Convolutional neural network]
• https://arxiv.org/abs/2210.00692[Interpreting convolutional neural networks' low dimensional approximation][Quantum spin system]
• https://arxiv.org/abs/2211.12596[Application][Convolutional neural network][TSOM Image][Classification][6 nm node patterned defect]
• https://arxiv.org/abs/1810.03787[Quantum convolutional][Neural network]
• https://arxiv.org/abs/2211.11786[Model-independent learning][Quantum phases of matter][Quantum convolutional neural network]
• https://arxiv.org/abs/2407.04114[Quantum convolutional neural network][Phase recognition][Two dimension]
• https://arxiv.org/abs/2212.02805[Interdisciplinary discovery][Nanomaterial][Convolutional neural network]
• https://arxiv.org/abs/2302.01390[Controlling][Skyrmion density and size][Quantized convolutional neural network]
• https://arxiv.org/abs/2306.11833[Convolutional neural network][Large-scale dynamical modeling][Itinerant magnet]
• https://arxiv.org/abs/2306.12874[Charting nanocluster structure][Convolutional neural network]
• https://arxiv.org/abs/2310.18035[High throughput screening][Ternary nitride][Convolutional neural network]
• https://arxiv.org/abs/2310.19128[Prediction][Local elasto-plastic stress][Strain field][Two-phase composite microstructure][Deep convolutional neural network]
• https://arxiv.org/abs/2312.09223[Many-body mobility edge][1D and 2D revealed][Convolutional neural network]
• https://arxiv.org/abs/2402.03876[Convolutional neural network][Volcano plot][Screening][Prediction][Two-dimensional single-atom catalyst]
• https://arxiv.org/abs/2404.05901[Quantum-inspired activation function][Convolutional neural network]
• https://arxiv.org/abs/2405.03049[Quantitative analysis][Prediction performance][Convolutional neural network][Evaluating][Surface elastic energy][Strained film]
• https://arxiv.org/abs/2405.05156[Crystal structure identification][3D convolutional neural network][Application][High-pressure phase transition][SiO₂]
• https://arxiv.org/abs/2405.17696[Physics-guided full waveform inversion][Encoder-solver convolutional neural network]
• https://arxiv.org/abs/2412.05329[Mapping][Layer][Ocean floor][Convolutional neural network]
• https://arxiv.org/abs/2412.06631[Recurrent convolutional neural network][Non-adiabatic dynamics][Quantum-classical system]
• https://arxiv.org/abs/2502.03572[Multi-output convolutional neural network][Improved parameter extraction][Time-resolved electrostatic force microscopy data]
• https://arxiv.org/abs/2204.07234[Physics-aware recurrent convolutional][PARC][Neural network][Assimilate meso-scale][Reactive mechanics][Energetic material]
• https://arxiv.org/abs/2009.09684[Determining electronic properties][L-edge X-ray absorption spectra][Transition metal compound][Artificial neural network]
• https://arxiv.org/abs/1803.00133[Materials data validation][Imputation][Artificial neural network]
• https://arxiv.org/abs/1806.05773[Notes on derivation][Streamline field][Artificial neural network][Automatic simulation][Material forming process]
• https://arxiv.org/abs/1807.09527[Adiabatic superconducting][Artificial neural network][Basic cell]
• https://arxiv.org/abs/1808.02069[Generalized transfer matrix state][Artificial neural network]
• https://arxiv.org/abs/1809.05519[Identifying quantum phase transition][Artificial neural network][Experimental data]
• https://arxiv.org/abs/1902.07443[On-the-fly adaptivity][Nonlinear twoscale simulation][Artificial neural network][Reduced order modeling]
• https://arxiv.org/abs/1811.03774[Artificial neural network][Density-functional optimization][Fermionic system]
• https://arxiv.org/abs/1812.04394[Gap prediction][Hybrid graphene][Hexagonal boron nitride][Nanoflake][Artificial neural network]
• https://arxiv.org/abs/1905.07440[Accelerating lattice quantum Monte Carlo simulation][Artificial neural network][Application to the Holstein model]
• https://arxiv.org/abs/2004.10167[Grain boundary slip transfer classification][Metric selection][Artificial neural network]
• https://arxiv.org/abs/2005.02789[Predicting][Porosity][Freeze casting][Artificial neural network]
• https://arxiv.org/abs/2009.00019[Solving the Liouvillian gap][Artificial neural network][Phys. Rev. Lett. 126, 160401(2021)]
• https://arxiv.org/abs/2009.04473[Unitary long-time evolution][Quantum renormalization group][Artificial neural network]
• https://arxiv.org/abs/2009.09684[Determining electronic properties][L-edge X-ray absorption spectra][Transition metal compound][Artificial neural network]
• https://arxiv.org/abs/2011.14505[Atomistic mechanism][Underlying the Si(111)-(7x7) surface reconstruction][Revealed][Artificial neural-network potential]
• https://arxiv.org/abs/2012.11586[Analyzing non-equilibrium quantum state][Snapshot][Artificial neural network][Phys. Rev. Lett. 127, 150504 (2021)]
• https://arxiv.org/abs/2012.11137[Structural phase transition][Two dimensional single-layer SnTe][Artificial neural network]
• https://arxiv.org/abs/2102.02345[LAMMPS implementation][Rapid artificial neural network][Interatomic potential]
• https://arxiv.org/abs/2103.03367[Training artificial neural network][Precision orientation][Strain mapping][4D electron diffraction dataset]
• https://arxiv.org/abs/2104.00013[Time-dependent variational principle][Open quantum system][Artificial neural network][Phys. Rev. Lett. 127, 230501 (2021)]
• https://arxiv.org/abs/2105.15193[Artificial neural network state][Non-additive system]
• https://arxiv.org/abs/2106.12745[Artificial neural network molecular mechanics][Iron grain boundary]
• https://arxiv.org/abs/2108.13137[Thermodynamics-based artificial neural networks][TANN][Multiscale modeling][Inelastic microstructure]
• https://arxiv.org/abs/2110.00724[Complex spin Hamiltonian][Artificial neural network]
• https://arxiv.org/abs/2110.09815[Microstructure reconstruction][Artificial neural network][Combination of causal and non-causal approach]
• https://arxiv.org/abs/2204.11126[Near-real-time diagnosis][Electron optical phase aberration][Scanning transmission electron microscopy][Artificial neural network]
• https://arxiv.org/abs/2205.08378[Machine learning][Atomic layer deposition][Predicting][Saturation time][Reactor growth profile][Artificial neural network]
• https://arxiv.org/abs/2206.00166[Spectral recognition][Magnetic nanoparticle][Artificial neural network]
• https://arxiv.org/abs/2206.02388[Extracting electronic many-body correlation][Local measurement][Artificial neural network]
• https://arxiv.org/abs/2209.06001[Integrated artificial neural network][Trainable activation function][Enabled][Topological insulator-based spin-orbit torque device]
• https://arxiv.org/abs/2210.00365[Efficient solution][Fermionic System][Artificial neural network]
• https://arxiv.org/abs/2210.02202[New family][Constitutive artificial neural network][Automated model discovery]
• https://arxiv.org/abs/2303.12164[Viscoelastic constitutive artificial neural networks (vCANNs)][Framework][Data-driven anisotropic nonlinear finite viscoelasticity]
• https://arxiv.org/abs/2311.06380[Theory][Implementation][Inelastic constitutive artificial neural network]
• https://arxiv.org/abs/2502.17490[Generalized dual potential][Inelastic constitutive artificial neural network][JAX implementation][Finite strain]
• https://arxiv.org/abs/2210.16994[Comparison][Two artificial neural networks][Trained][Surrogate modeling][Stress][Materially heterogeneous elastoplastic solid]
• https://arxiv.org/abs/2412.07126[FE-PINNs][Finite-element-based physics-informed neural network][Surrogate modeling]
• https://arxiv.org/abs/2501.18137[Tensor completion][Surrogate modeling][Material property prediction]
• https://arxiv.org/abs/2211.01779[Exploring explicit coarse-grained structure][Artificial neural network]
• https://arxiv.org/abs/2211.04796[Physics-separating artificial neural network][Predicting initial stage][Al sputtering][Thin film deposition][Ar plasma discharge]
• https://arxiv.org/abs/2301.01521[Artificial neural network][Effective tool][Positron annihilation lifetime spectra]
• https://arxiv.org/abs/2301.03524[Physics-separating artificial neural network][Predicting sputtering and thin film deposition][AlN][Ar/N₂][Discharge][Experimental timescale]
• https://arxiv.org/abs/2301.12262[Arrhenius crossover temperature][Glass-forming liquid][Predicted][Artificial Neural Network]
• https://arxiv.org/abs/2304.07899[Artificial neural network-based density functional approach][Adiabatic energy difference][Transition metal complexes]
• https://arxiv.org/abs/2404.15658[Neural network-based recognition][Multiple nanobubble][Graphene]
• https://arxiv.org/abs/2410.09177[From Ferminet to PINN][Connection][Neural network-based algorithm][High-dimensional Schrödinger Hamiltonian]
• https://arxiv.org/abs/2307.10139[Artificial neural network][Predicting mechanical properties][Crystalline polyamide12][Molecular dynamics simulation]
• https://arxiv.org/abs/2310.20398[Hybrid approach][Solving the gravitational N-body problem][Artificial neural network][Journal of Computational Physics, Volume 496, 112596 (2024)]
• https://arxiv.org/abs/2401.17116[Quantum error mitigation][Correction][Mediated by Yang-Baxter equation][Artificial neural network]
• https://arxiv.org/abs/2410.02633[Quantum many-body solver][Artificial neural network][Application][Strongly correlated electron system][J. Phys. Soc. Jpn. 94, 031001 (2025)]
• https://arxiv.org/abs/2410.19924[Prediction][Final phosphorus content][Steel][Scrap-based electric arc furnace][Artificial neural network]
• https://arxiv.org/abs/2411.07960[Predicting][Ti-Al binary phase diagram][Artificial neural network potential]
• https://arxiv.org/abs/2411.19632[PACMANN][Point adaptive collocation method][Artificial neural network]
• https://arxiv.org/abs/2412.01947[Iterative variational learning][Committor-consistent transition pathway][Artificial neural network]
• https://arxiv.org/abs/2412.14216[Artificial neural network][Modelling][Variational effect][Double metal double gate negative capacitance FET]
• https://arxiv.org/abs/2501.05831[Hierarchical serpentine-like organic crystal][optical waveguide][Artificial neural network]
• https://arxiv.org/abs/2501.08341[Dissecting][Small artificial neural network]
• https://arxiv.org/abs/1812.09329[QuCumber][Wavefunction reconstruction][Neural network]
• https://arxiv.org/abs/1901.00032[Inorganic materials synthesis planning][Literature-trained neural network]
• https://arxiv.org/abs/1902.01845[Probing topological property][3D lattice dimer model][Neural network]
• https://arxiv.org/abs/2207.09056[Learning quantum dissipation][Neural ordinary differential equation]
• https://arxiv.org/abs/1902.09483[Variational quantum Monte Carlo][Neural network ansatz][Open quantum system]
• https://arxiv.org/abs/2203.15472[Ab initio calculation][Real solid][Neural network ansatz]
• https://arxiv.org/abs/1903.09101[Scanning probe state recognition][Multi-class neural network ensembles]
• https://arxiv.org/abs/1904.13165[Neural network setup][Precise detection][Many-body localization transition][Finite-size scaling][limitation]
• https://arxiv.org/abs/1905.01536[Neural network][Path collective variable][Enhanced sampling of phase transformation]
• https://arxiv.org/abs/1905.06371[Roadmap on material-function mapping][Photonic-electronic hybrid neural network]
• https://arxiv.org/abs/1905.09168[Predicting quantum many-body dynamics][Long short-term memory][Neural network]
• https://arxiv.org/abs/1910.02898[Fast fitting of reflectivity data][Growing thin film][Neural network]
• https://arxiv.org/abs/1910.03172[Prediction][Evolution of the stress field][Polycrystal][Elastic-plastic deformation][Hybrid neural network model]
• https://arxiv.org/abs/1910.12919[Domain wall synapse][Non volatile memory device][On-chip learning][Analog hardware neural network]
• https://arxiv.org/abs/1911.01365[Interatomic potential][Simple dense neural network representation]
• https://arxiv.org/abs/1912.02777[Automated tuning][Double quantum dot][Specific charge state][Neural network]
• https://arxiv.org/abs/2312.14322[Data needs][Challenge][Quantum dot device][Automation][Workshop report]
• https://arxiv.org/abs/2002.00054[Implementing a neural network interatomic model][Performance portability][Emerging exascale architecture]
• https://arxiv.org/abs/2002.03452[Estimation][Critical temperature][High-temperature superconductor][AC susceptibility measurement][A pair of Neural Networks]
• https://arxiv.org/abs/2002.04613[Neural network wave function][Sign problem]
• https://arxiv.org/abs/2411.19938[Probing quantum critical phase][Neural network wavefunction]
• https://arxiv.org/abs/2401.10190[Kaczmarz-inspired approach][Accelerate][Optimization][Neural network wavefunction]
• https://arxiv.org/abs/2502.17144[Leveraging recurrence][Neural network wavefunction][Large-scale simulation][Heisenberg antiferromagnet][Square lattice]
• https://arxiv.org/abs/2305.06989[Neural wave function[Superfluid]
• https://arxiv.org/abs/2002.08666[Determination][Semion code threshold][Neural decoder]
• https://arxiv.org/abs/2003.02423[Gate-tunable van der Waals heterostructure][Reconfigurable neural network vision sensor]
• https://arxiv.org/abs/2003.14328[CRYSPNet][Crystal structure prediction][Neural network]
• https://arxiv.org/abs/2004.10994[Physics based approach][Neural network][Enabled design][All-dielectric metasurface]
• https://arxiv.org/abs/2005.06847[Structure prediction][Two-dimensional material][Neural network-driven evolutionary technique]
• https://arxiv.org/abs/2005.12131[MAISE][Construction][Neural network interatomic model][Evolutionary structure optimization]
• https://arxiv.org/abs/2407.08469[Comprehensive convolutional neural network architecture design][Magnetic skyrmion][Domain wall]
• https://arxiv.org/abs/1907.03055[PANNA][Artificial neural network architecture]
• https://arxiv.org/abs/2006.05708[Image reconstruction][Multimode fiber][Simple neural network architecture]
• https://arxiv.org/abs/2407.19483[Nearest-neighbours neural network architecture][Efficient sampling][Statistical physics model]
• https://arxiv.org/abs/2104.02529[Active learning][Element embedding approach][Neural network][Infinite-layer][Perovskite oxide]
• https://arxiv.org/abs/2008.00966[Neural network][Predict icephobic performance]
• https://arxiv.org/abs/1903.04366[Atomic energy mapping][Neural network potential]
• https://arxiv.org/abs/1909.10253[Anharmonic thermodynamics][Vacancy][Neural network potential]
• https://arxiv.org/abs/1909.10134[Hybrid neural network potential][Multilayer graphene]
• https://arxiv.org/abs/2002.04172[Efficient training][Accurate neural network potential][Including atomic force][Taylor expansion][Application to water and a transition-metal oxide]
• https://arxiv.org/abs/2005.09591[Transferability of neural network potential][Varying stoichiometry][Phonon][Thermal conductivity][Mn_xGe_y compounds]
• https://arxiv.org/abs/2007.06459[Global optimization][Copper Cluster][ZnO(10-10) Surface][DFT-based neural network potential][Genetic algorithm]
• https://arxiv.org/abs/2008.02572[Enabling ab initio configurational sampling][Multicomponent solid][Long-range interaction][Neural network potential][Active learning]
• https://arxiv.org/abs/2008.05094[Phase stability][Au-Li binary system][Neural network potential]
• https://arxiv.org/abs/2007.00335[Predicting][Oxidation][Spin state][High-dimensional neural network][Lithium manganese oxide spinel]
• https://arxiv.org/abs/2007.00327[Closing the Gap][Theory and experiment][Lithium manganese oxide spinel][High-dimensional neural network potential]
• https://arxiv.org/abs/2009.06484[Fourth-generation high-dimensional neural network potential][Accurate electrostatics][Non-local charge transfer]
• https://arxiv.org/abs/2409.11037[High-dimensional neural network potential][Co₃O₄]
• https://arxiv.org/abs/2502.07907[Iterative charge equilibration][Fourth-generation high-dimensional neural network potential]
• https://arxiv.org/abs/2405.00290[Message-passing interatomic potential][Learn non-local electrostatic interaction]
• https://arxiv.org/abs/2011.04604[Systematic approach][Generating accurate neural network potential][Carbon]
• https://arxiv.org/abs/2102.12404[Accurate large-scale simulation][Siliceous zeolite][Neural network potential]
• https://arxiv.org/abs/2106.14583[PFP][Universal neural network potential][Material discovery]
• https://arxiv.org/abs/2107.00963[Real-system nanoparticle][Universal neural network potential][PFP]
• https://arxiv.org/abs/2302.14231[CHGNet][Pretrained universal neural network potential][Charge-informed atomistic modeling]
• https://arxiv.org/abs/2312.01290[Evolutionary search][Superconducting phase][Lanthanum-nitrogen-hydrogen system][Universal neural network potential]
• https://arxiv.org/abs/2503.13789[Carbonic anhydrase II][Simulated][Universal neural network potential]
• https://arxiv.org/abs/2103.10846[Universal neural network][Learning phases and criticalities]
• https://arxiv.org/abs/2408.04497[SchrödingerNet][Universal neural network solver][Schrödinger equation]
• https://arxiv.org/abs/2008.12331[Neural network solver][Small quantum cluster]
• https://arxiv.org/abs/2502.05318[Diagonal symmetrization][Neural network solver][Many-electron Schrödinger equation]
• https://arxiv.org/abs/2108.05748[Quality of uncertainty estimates][Neural network potential ensemble]
• https://arxiv.org/abs/2203.06283[Prediction][Stable Li-Sn compound][Boosting ab initio searche][Neural network potential]
• https://arxiv.org/abs/2203.16789[Neural network potential][Study point defect properties][Multiple charge state][GaN][Nitrogen vacancy]
• https://arxiv.org/abs/2212.14667[Origin][Performance degradation][High-delithiation][Li_xCoO₂][Insight][Direct atomic simulation][Global neural network potential]
• https://arxiv.org/abs/2301.11612[Neural network potential][Self-trained atomic fingerprint][Test with the mW water potential]
• https://arxiv.org/abs/2304.10812[First-principles modeling][Equilibration dynamics][Hyperthermal product][Surface reaction][Scalable neural network potential]
• https://arxiv.org/abs/2410.22570[Orb][Fast][Scalable neural network potential]
• https://arxiv.org/abs/2307.11296[Structural analysis][Zirconium oxynitride/water interface][Neural network potential]
• https://arxiv.org/abs/2309.06710[Crystal structure prediction][Neural network potential][Age-fitness pareto genetic algorithm]
• https://arxiv.org/abs/2308.05163[Neural network potential][Modeling nonstoichiometric material][Chromium sulfides Cr_(1¡Ýx)S]
• https://arxiv.org/abs/2311.05407[Data distillation][Neural network potential][Foundational dataset]
• https://arxiv.org/abs/2402.14640[Structure and thermodynamics][Defect][Na-feldspar][Neural network potential]
• https://arxiv.org/abs/2402.17660[TorchMD-Net 2.0][Fast neural network potential][Molecular simulation]
• https://arxiv.org/abs/2403.09529[General-purpose neural network potential][Ti-Al-Nb alloy][Large-scale molecular dynamics][Ab initio accuracy]
• https://arxiv.org/abs/2404.08413[Lowering][Exponential wall][Accelerating high-entropy alloy catalysts screening][Local surface energy descriptor][Neural network potential]
• https://arxiv.org/abs/2404.12036[Exploring][Premelting transition][Molecular simulation][Neural network potential]
• https://arxiv.org/abs/2407.04526[Peering inside the black box][Learning the relevance][Many-body function][Neural network potential]
• https://arxiv.org/abs/2407.06615[SG-NNP][Species-separated Gaussian neural network potential][Linear elemental scaling][Optimized dimensions][Multi-component materials]
• https://arxiv.org/abs/2407.17452[Thermodynamics][Alkali feldspar solid solution][Varying Al-Si order][Atomistic simulation][Neural network potential]
• https://arxiv.org/abs/2408.11538[Structure and dynamics][Magnetite(001)/water interface][Molecular dynamics simulation][Neural network potential]
• https://arxiv.org/abs/2409.03253[SpinMultiNet][Neural network potential][Incorporating spin degrees of freedom][Multi-task learning]
• https://arxiv.org/abs/2411.17191[Generator][Neural network potential][Molecular dynamics][Constructing robust and accurate potential][Active learning][Nanosecond-scale simulation]
• https://arxiv.org/abs/2412.05773[Scalable training][Neural network potential][Complex interface][Through data augmentation]
• https://arxiv.org/abs/2501.01811[QuantumBind-RBFE][Accurate relative binding free energy calculation][Neural network potential]
• https://arxiv.org/abs/2502.02408[Neural network potential molecular dynamics simulation][(La,Ce,Pr,Nd)_0.95(Mg,Zn,Pb,Cd,Ca,Sr,Ba)_0.05F_2.95][J. Phys. Chem. B 128, 12171(2024)]
• https://arxiv.org/abs/2503.01932[General neural network potential][Energetic material][C, H, N, and O elements]
• https://arxiv.org/abs/2405.03797[Tensor network computation][Capture strict variationality][Volume law behavior][Efficient representation][Neural network states]
• https://arxiv.org/abs/2008.05488[Solving quantum master equation][Deep quantum neural network]
• https://arxiv.org/abs/1907.11333[Entanglement area law][Shallow and deep quantum neural network States]
• https://arxiv.org/abs/2407.05978[Zero-temperature Monte Carlo simulation][Two-dimensional quantum spin glasses][Neural network state]
• https://arxiv.org/abs/2301.07542[Quantum neural network][Inspired hardware adaptable ansatz][Efficient quantum simulation][Chemical system]
• https://arxiv.org/abs/2503.08904[Towards efficient parametric state estimation][Circulating fuel reactor][Shallow recurrent decoder network]
• https://arxiv.org/abs/2204.04502[Predicting aggregation][Sequence-defined macromolecule][Recurrent neural network]
• https://arxiv.org/abs/1909.03549[Prediction of optical spectra][Coarse-grained polymer][Sequence generation problem][Recurrent neural networks solution]
• https://arxiv.org/abs/2002.02973[Recurrent neural network][Wavefunction]
• https://arxiv.org/abs/2502.17238[Training and re-training liquid crystal elastomer metamaterial][Pluripotent functionality]
• https://arxiv.org/abs/2003.06228[Iterative retraining][Quantum spin model][Recurrent neural network]
• https://arxiv.org/abs/2002.05817[Random telegraph signal analysis][Recurrent neural network]
• https://arxiv.org/abs/2008.07658[Self-supervised learning][Prediction][Microstructure evolution][Recurrent neural network]
• https://arxiv.org/abs/2206.08110[Morphological evolution][Surface diffusion][Convolutional][Recurrent neural network][Extrapolation][Prediction uncertainty]
• https://arxiv.org/abs/2206.12363[Tensor network quantum state][Tensorial recurrent neural network]
• https://arxiv.org/abs/2205.00449[Molecular identification][AFM image][IUPAC nomenclature][Attribute multimodal recurrent neural network]
• https://arxiv.org/abs/2207.14314[Supplementing recurrent neural network wave function][Symmetry][Annealing][Improve accuracy]
• https://arxiv.org/abs/2210.00842[Micromechanics-based recurrent neural networks model][Path-dependent cyclic deformation][Short fiber composite]
• https://arxiv.org/abs/2303.11207[Investigating topological order][Recurrent neural network]
• https://arxiv.org/abs/2310.09434[Learning nonlinear integral operator][Recurrent neural network][Application][Solving integro-differential equation]
• https://arxiv.org/abs/2311.13434[Recurrent neural network][Transfer learning][Elasto-plasticity][Woven composites]
• https://arxiv.org/abs/2404.17583[Physically recurrent neural network][Rate and path-dependent heterogeneous materials][Finite strain framework]
• https://arxiv.org/abs/2503.04901[Multiscale analysis][Woven composite][Hierarchical physically recurrent neural network]
• https://arxiv.org/abs/2409.14042[Recurrent neural network][Prediction][Electronic excitation dynamics]
• https://arxiv.org/abs/2412.06686[Some best practice][Operator learning]
• https://arxiv.org/abs/2501.07882[Algorithm][Generate hierarchical structure][Desiccation crack pattern]
• https://arxiv.org/abs/2501.00016[Predicting crack nucleation][Propagation][Brittle material][Deep operator network][Diverse trunk architecture]
• https://arxiv.org/abs/2309.04482[Addressing][Accuracy-cost tradeoff][Material property prediction][Teacher-student strategy]
• https://arxiv.org/abs/2407.00671[Establishing deep InfoMax][Effective self-supervised learning methodology][Materials informatics]
• https://arxiv.org/abs/2205.01893[Crystal Twins][Self-supervised learning][Crystalline material property prediction]
• https://arxiv.org/abs/2206.04109[Self-supervised graph neural network][Accurate prediction][Neel temperature]
• https://arxiv.org/abs/2203.10204[Inferring topological transition][Pattern-forming process][Self-supervised learning]
• https://arxiv.org/abs/2402.18286[Self-supervised learning][Electron microscopy][Foundation model][Advanced image analysis]
• https://arxiv.org/abs/2203.13875[Self-supervised machine learning model][Analysis][Nanowire morphologies][Transmission electron microscopy image]
• https://arxiv.org/abs/2311.16652[Augmenting x-ray single particle imaging reconstruction][Self-supervised machine learning]
• https://arxiv.org/abs/2401.05223[Physics guided dual self-supervised learning][Structure-based materials property prediction]
• https://arxiv.org/abs/2405.10135[Self-supervised feature distillation][Design of experiments][Efficient training][Micromechanical deep learning surrogate]
• https://arxiv.org/abs/2408.17255[Self-supervised learning][Crystal property prediction][Denoising]
• https://arxiv.org/abs/2409.08891[Self-supervised learning][Denoising quasiparticle interference data]
• https://arxiv.org/abs/2411.06447[Multi-parameter molecular MRI quantification][Physics-informed self-supervised learning]
• https://arxiv.org/abs/2411.09892[Deep learning robotics][Self-supervised spatial differentiation][Drive autonomous contact-based semiconductor characterization]
• https://arxiv.org/abs/2008.12028[Single-atom level determination][3-dimensional surface atomic structure][Neural network-assisted atomic electron tomography]
• https://arxiv.org/abs/2011.04584[Interpretable][Calibrated neural network][Analysis][Understanding][Inelastic neutron scattering data]
• https://arxiv.org/abs/2011.05199[Random sampling neural network][Quantum many-body Problem]
• https://arxiv.org/abs/2011.11214[Learning the ground state][Non-stoquastic quantum Hamiltonian][Rugged neural network landscape]
• https://arxiv.org/abs/2011.13774[Neural network representation][Electronic structure][Ab initio molecular dynamics]
• https://arxiv.org/abs/2012.00404[Directed graph attention neural network][Utilizing 3D coordinate][Molecular property prediction]
• https://arxiv.org/abs/2101.03016[Hopfield neural network][Magnetic film][Natural learning]
• https://arxiv.org/abs/2307.12111[Noise tailoring][Noise annealing][External noise injection strategies][Memristive Hopfield neural network]
• https://arxiv.org/abs/2502.17665[Effective field neural network]
• https://arxiv.org/abs/2101.03181[Miniaturizing neural network][Charge state autotuning][Quantum dot]
• https://arxiv.org/abs/2101.07243[Gauge invariant autoregressive neural network][Quantum lattice model]
• https://arxiv.org/abs/2402.16579[Sparse autoregressive neural network][Classical spin system]
• https://arxiv.org/abs/2304.01996[Autoregressive neural TensorNet][Bridging neural network][Tensor network][Quantum many-body simulation]
• https://arxiv.org/abs/2411.07144[Autoregressive neural quantum state][Fermi Hubbard model]
• https://arxiv.org/abs/2101.11099[Neural network][Quantum many-body physics][Hands-on tutorial]
• https://arxiv.org/abs/2103.17244[XY Neural Network]
• https://arxiv.org/abs/2104.02741[Conditional physics][Neural network]
• https://arxiv.org/abs/1910.10675[Classical quantum optimization][Neural network quantum state]
• https://arxiv.org/abs/2111.04243[Learning][Compass spin model][Neural network quantum state]
• https://arxiv.org/abs/2206.14307[Systematic improvement][Neural network quantum state][Lanczos recursion]
• https://arxiv.org/abs/2210.16493[Neural network quantum state][Proximal optimization][Ground-state searching scheme][Variational Monte Carlo]
• https://arxiv.org/abs/2301.09923[Lee-Yang theory][Quantum phase transition][Neural network quantum state]
• https://arxiv.org/abs/2304.09504[Ground state properties][Quantum skyrmion][Neural network quantum state]
• https://arxiv.org/abs/2310.05715[Simple linear algebra identity][Optimize large-scale neural network quantum state]
• https://arxiv.org/abs/2403.08184[Quantum skyrmion dynamics][Neural network quantum state]
• https://arxiv.org/abs/2405.04472[Neural network quantum state][Interacting Hofstadter model][Higher local occupation][Long-range interaction]
• https://arxiv.org/abs/2407.20065[Learning eigenstates][Quantum many-body Hamiltonian][Symmetric subspace][Neural network quantum state]
• https://arxiv.org/abs/1802.02944[Neural-network][Kohn-Sham exchange-correlation potential][Out-of-training transferability]
• https://arxiv.org/abs/1909.12852[Fermionic neural-network state][Ab-initio electronic structure]
• https://arxiv.org/abs/2403.11287[Neural-network density functional theory][Variational energy minimization][Phys. Rev. Lett. 133, 076401 (2024)]
• https://arxiv.org/abs/1807.03325[Symmetry][Mny-body excited state][Neural-network quantum state]
• https://arxiv.org/abs/1902.05131[Neural-Network approach][Dissipative quantum many-body dynamics]
• https://arxiv.org/abs/1910.07596[Precise measurement][Quantum observable][Neural-network estimator]
• https://arxiv.org/abs/2002.09246[Efficient neural-network][Variational Monte Carlo scheme][Direct optimization][Excited energy state][Frustrated quantum system]
• https://arxiv.org/abs/2101.05867[Neural-networks model][Force prediction][Multi-principal-element alloy]
• https://arxiv.org/abs/1912.08831[Real time evolution][Neural-network quantum state]
• https://arxiv.org/abs/2008.00118[Two-dimensional spinless lattice fermion][First-quantized deep neural-network][Quantum state]
• https://arxiv.org/abs/2010.14514[U(1) symmetric recurrent neural network][Quantum state reconstruction]
• https://arxiv.org/abs/2010.01358[Neural-network quantum state][Electronic structure][Real solid]
• https://arxiv.org/abs/2103.05017[Correlation-enhanced neural network][Interpretable variational quantum state]
• https://arxiv.org/abs/2103.09146[Compact neural-network][Quantum state representation][Jastrow and stabilizer state]
• https://arxiv.org/abs/2104.10696[Scaling][Neural-network quantum state][Time evolution]
• https://arxiv.org/abs/2105.01054[Stochastic noise][Generalization error][Time propagation][Neural-network quantum state]
• https://arxiv.org/abs/2105.03129[Neural network][Enhanced hybrid quantum many-body dynamical distribution]
• https://arxiv.org/abs/2105.08579[Neural-network quantum state][Spin-1 system][Spin-basis][Parameterization effect][Compactness of representations]
• https://arxiv.org/abs/2112.11957[Neural-network quantum state][Periodic system][Continuous space]
• https://arxiv.org/abs/2202.01704[Investigating network parameter][Neural-network quantum state]
• https://arxiv.org/abs/2412.12398[Polynomially efficient quantum][Variational Monte Carlo][Training neural-network quantum state][Physico-chemical application]
• https://arxiv.org/abs/2305.13132[Neural-network-designed three-qubit gate][Robust][Against charge noise][Crosstalk][Silicon]
• https://arxiv.org/abs/2308.09664[Variational optimization][Amplitude][Neural-network quantum many-body ground state]
• https://arxiv.org/abs/2502.01264[Generalized Lanczos method][Systematic optimization][Neural-network quantum state]
• https://arxiv.org/abs/2502.09488[Foundation neural-network quantum state]
• https://arxiv.org/abs/2308.11426[Neural-network force field][Backed nested sampling][Silicon p-T phase diagram]
• https://arxiv.org/abs/2204.10904[Neural-network decoder][Measurement induced phase transition]
• https://arxiv.org/abs/2111.10420[Fermionic wave function][Neural-network][Constrained hidden state]
• https://arxiv.org/abs/2108.10850[Heat transport][Liquid water][First-principles][Deep-neural-network simulation]
• https://arxiv.org/abs/2209.05195[Neural-network quantum state][Two-leg Bose-Hubbard ladder][Magnetic flux]
• https://arxiv.org/abs/2406.00151[Neural-network-supported basis optimizer][Configuration interaction problem][Quantum many-body cluster][Feasibility study][Numerical proof]
• https://arxiv.org/abs/2406.10542[Neural-network-backed effective harmonic potential][Ambient pressure phase][Hafnia][Phys. Rev. B 107, 184111 (2024)]
• https://arxiv.org/abs/2408.02429[Neural-network-enabled molecular dynamics study][HfO₂ phase transition]
• https://arxiv.org/abs/2408.06609[Neural-network-based mapping][Optimization framework][High-precision coarse-grained simulation]
• https://arxiv.org/abs/2501.12558[Structural and mechanical properties][W-Cu compound][Characterized][Neural-network-based potential]
• https://arxiv.org/abs/2402.12199[Molecular dynamics simulation][Anisotropic particle][Accelerated][Neural-net predicted interaction]
• https://arxiv.org/abs/2106.14623[Polyconvex][Anisotropic hyperelasticity][Neural network]
• https://arxiv.org/abs/2404.15562[Polyconvex neural network model][Thermoelasticity]
• https://arxiv.org/abs/2306.16270[S2SNet][Pretrained neural network][Superconductivity discovery]
• https://arxiv.org/abs/2106.16043[Robustness][Neural network][Spintronic neuron]
• https://arxiv.org/abs/2108.04244[Distinguishing][Anderson insulator][Many-body localized phase][Space-time snapshot][Neural network]
• https://arxiv.org/abs/2108.05737[Neural network][Universal probe][Many-body localization][Quantum graph]
• https://arxiv.org/abs/2108.08631[Determinant-free fermionic wave function][Feed-forward neural network]
• https://arxiv.org/abs/2202.05183[Discovering quantum phase transition][Fermionic neural network][Phys. Rev. Lett. 130, 036401 (2023)]
• https://arxiv.org/abs/2109.05598[Neural network][Order parameter][Phase transition][High-entropy alloy]
• https://arxiv.org/abs/2109.08861[Improving][Deconvolution of spectrum][Finite temperature][Neural network]
• https://arxiv.org/abs/2109.09655[Impact][Surface][Pore characteristics][Fatigue life][Laser powder bed fusion][Ti-6Al-4V alloy][Neural Network Model]
• https://arxiv.org/abs/2110.03266[Lagrangian neural network][Differential symmetries][Relational inductive bias]
• https://arxiv.org/abs/2405.14645[Lagrangian neural network][Reversible dissipative evolution]
• https://arxiv.org/abs/2110.10748[Melting temperature database][Neural network model][Melting temperature prediction]
• https://arxiv.org/abs/2111.06411[Neural network evolution strategy][Solving quantum sign structure]
• https://arxiv.org/abs/2111.09997[Exploring glassy dynamics][Markov state model][Graph dynamical neural network]
• https://arxiv.org/abs/2405.13098[How glassy are neural networks?]
• https://arxiv.org/abs/2112.04881[Application][Neural network][Exchange-correlation functional interpolation]
• https://arxiv.org/abs/2404.14258[Quantum-enhanced neural exchange-correlation functional]
• https://arxiv.org/abs/2112.09159[Implementation][Binary neural network][Passive array][Magnetic tunnel junction]
• https://arxiv.org/abs/2201.00225[Neural network][Ultrafast time-delayed effect][Exciton-polariton]
• https://arxiv.org/abs/2503.15391[Modeling crystal defect][Defect-informed neural network]
• https://arxiv.org/abs/2009.13106[Fast predictive molecular dynamics simulation][Multi-fidelity physics][Informed neural network]
• https://arxiv.org/abs/2011.02512[Robust quantum gate][Smooth pulse][Physics-informed neural network]
• https://arxiv.org/abs/2101.06540[Development][Physically-informed neural network][Interatomic potential][Tantalum]
• https://arxiv.org/abs/2103.14104[Physics-informed neural network][Quantifying][Microstructure][Polycrystalline nickel][Ultrasound data]
• https://arxiv.org/abs/2106.03362[Magnetostatics][Micromagnetics][Physics informed neural network]
• https://arxiv.org/abs/2107.08781[Parameter identification][Damage model][Physics informed neural network]
• https://arxiv.org/abs/2401.15485[Constrained Hamiltonian system][Physics informed neural network][Hamilton-Dirac neural net]
• https://arxiv.org/abs/2301.02191[Physics informed neural network][Charged particle][Surrounded][Conductive boundaries]
• https://arxiv.org/abs/2405.04230[Unveiling][Optimization process][Physics informed neural network][How accurate and competitive can PINNs be?]
• https://arxiv.org/abs/2406.05290[Extremization][Fine tune physics informed neural network][Solving boundary value problem]
• https://arxiv.org/abs/2406.06350[Error analysis][Numerical algorithm][PDE approximation][Hidden-layer concatenated physics informed neural network]
• https://arxiv.org/abs/2502.06739[Note][Physical interpretation][Neural PDE's]
• https://arxiv.org/abs/2408.07027[Multi-soliton solution][Data-driven discovery][Higher-order Burgers' hierarchy equation][Physics informed neural network]
• https://arxiv.org/abs/2411.08760[Energy dissipation preserving physics informed neural network][Allen-Cahn equation]
• https://arxiv.org/abs/2411.18240[Physics informed neural network][PINNs][Intelligent computing technique][Solving partial differential equation][Limitation and future prospect]
• https://arxiv.org/abs/2412.13142[Physics informed neural network][Enhanced denoising][Atomic resolution STEM imaging]
• https://arxiv.org/abs/2501.03254[Advanced displacement magnitude prediction][Multi-material architected lattice structure beam][Physics informed neural network architecture]
• https://arxiv.org/abs/2501.07373[Dynami-CAL GraphNet][Physics-informed graph neural network][Conserving linear][Angular momentum][Dynamical system]
• https://arxiv.org/abs/2502.11369[Physics-informed Gaussian process classification][Constraint-aware alloy design]
• https://arxiv.org/abs/2502.19543[High-fidelity multiphysics modelling][Rapid prediction][Physics-informed parallel neural operator]
• https://arxiv.org/abs/2502.02682[Pseudo-physics-informed neural operator][Enhancing operator learning][Limited data]
• https://arxiv.org/abs/2401.03948[Reconstruction][Excitation wave][Mechanical deformation][Physics-informed neural network]
• https://arxiv.org/abs/2201.08363[Physics-informed neural network][Modeling rate- and temperature-dependent plasticity]
• https://arxiv.org/abs/2211.04607[First principles physics-informed neural network][Quantum wavefunction][Eigenvalue surface]
• https://arxiv.org/abs/2211.15423[Effective data sampling strategies][Boundary condition constraint][Physics-informed neural network][Identifying material properties][Solid mechanics]
• https://arxiv.org/abs/2308.15640[Identifying constitutive parameter][Complex hyperelastic solid][Physics-informed neural network]
• https://arxiv.org/abs/2008.13654[Extensible structure-informed prediction][Formation energy][Improved accuracy and usability][Neural network]
• https://arxiv.org/abs/2404.02849[Efficient structure-Informed featurization][Property prediction][Ordered][Dilute][Random atomic structure]
• https://arxiv.org/abs/2310.19474[Structure-informed neural network][Boundary observation problem]
• https://arxiv.org/abs/2311.03746[Enhanced physics-informed neural network][Domain scaling][Residual correction method][Multi-frequency elliptic problem]
• https://arxiv.org/abs/2310.09528[Hypernetwork-based meta-learning][Low-rank physics-informed neural network]
• https://arxiv.org/abs/2312.16038[Physics-informed neural network][Solving functional renormalization group][Lattice]
• https://arxiv.org/abs/2402.05067[Multiscale modelling][Physics-informed neural network][Large-scale dynamics][Small-scale prediction][Complex system]
• https://arxiv.org/abs/2402.07231[Ab initio simulation][Thermodynamic properties][Phase transition][Fermi system][Fictitious identical particle][Physics-informed neural network]
• https://arxiv.org/abs/2403.03223[Exact enforcement][Temporal continuity][Sequential physics-informed neural network]
• https://arxiv.org/abs/2403.04094[Multiple scattering simulation][Physics-informed neural network]
• https://arxiv.org/abs/2404.18780[Optimal time sampling][Physics-informed neural network]
• https://arxiv.org/abs/2405.15603[Kronecker-factored approximate curvature][Physics-informed neural network]
• https://arxiv.org/abs/2406.02645[Astral][Training physics-informed neural network][Error majorant]
• https://arxiv.org/abs/2406.04380[Physics-informed neural network][Numerical modeling][Steady-state][Transient electromagnetic problem][Discontinuous media]
• https://arxiv.org/abs/2407.02230[PINNs-MPF][Physics-informed neural network framework][Multi-phase-field simulation][Interface dynamics]
• https://arxiv.org/abs/2407.10654[Inverse physics-informed neural network][Transport model][Porous material]
• https://arxiv.org/abs/2407.20833[Approximating electromagnetic field][Discontinuous media][Single physics-informed neural network]
• https://arxiv.org/abs/2408.04690[Modelling parametric uncertainty][PDEs model][Physics-informed neural network]
• https://arxiv.org/abs/2408.09446[Parameterized physics-informed neural network][Parameterized PDE]
• https://arxiv.org/abs/2408.10965[Monte Carlo physics-informed neural network][Multiscale heat conduction][Phonon Boltzmann transport equation]
• https://arxiv.org/abs/2409.01124[Two-stage initial-value iterative physics-informed neural network][Simulating solitary wave][Nonlinear wave equation][Journal of Computational Physics 505 (2024) 112917]
• https://arxiv.org/abs/2409.03239[DiffGrad][Physics-informed neural network]
• https://arxiv.org/abs/2409.02959[Physics-informed neural network][Incorporating energy dissipation][Phase-field model][Ferroelectric microstructure evolution]
• https://arxiv.org/abs/2409.14248[Higher-order-ReLU-KANs (HRKANs)][Solving physics-informed neural networks (PINNs)][More accurately][Robustly][Faster]
• https://arxiv.org/abs/2410.01340[Response estimation][System identification][Dynamical system][Physics-informed neural network]
• https://arxiv.org/abs/2410.08452[Kolmogorov-Arnold neural network][High-entropy alloys design]
• https://arxiv.org/abs/2410.04096[Sinc Kolmogorov-Arnold network][Application][Physics-informed neural network]
• https://arxiv.org/abs/2410.05744[PINN-MG][Multigrid-inspired hybrid framework combining iterative method][Physics-informed neural network]
• https://arxiv.org/abs/2410.14342[Dual physics-informed neural network][Topology optimization]
• https://arxiv.org/abs/2411.09704[From mesh to neural nets][Multi-method evaluation][Physics-informed neural network][Galerkin finite element method][Solving nonlinear convection-reaction-diffusion equation]
• https://arxiv.org/abs/2411.10048[Physics-informed neural network][Physicist][To be accurate][Mass and heat transport][Fischer-Tropsch catalyst particles]
• https://arxiv.org/abs/2411.10483[Physics-informed neural network][Electrical circuit analysis][Application][Dielectric material modeling]
• https://arxiv.org/abs/2411.18835[Physics-informed neural network model][Quantum impurity problem][Lehmann representation]
• https://arxiv.org/abs/2412.00225[Meta-learning loss function][Parametric partial differential equation][Physics-informed neural Network]
• https://arxiv.org/abs/2412.08650[Capacitive touch sensor modeling][Physics-informed neural network][Maxwell's Equation]
• https://arxiv.org/abs/2412.20851[Rectified sigmoid function][Enhancing][Accuracy][Physics-informed neural network]
• https://arxiv.org/abs/2412.19235[Two hidden layers][Still enough][Physics-informed neural network]
• https://arxiv.org/abs/2501.07765[PINN-FEM][Hybrid approach][Enforcing dirichlet boundary condition][Physics-informed neural network]
• https://arxiv.org/abs/2502.00604[Gradient alignment][Physics-informed neural network][Second-order optimization perspective]
• https://arxiv.org/abs/2502.11942[Sharp-PINNs][Staggered hard-constrained physics-informed neural network][Phase field modelling][Corrosion]
• https://arxiv.org/abs/2503.06403[Global physics-informed neural network][GPINNs][Local point-wise constraint][Global nodal association]
• https://arxiv.org/abs/2503.06320[Learning and discovering][Multiple solution][Physics-informed neural network][Random initialization][Deep ensemble]
• https://arxiv.org/abs/2202.00572[Two-qubit cz gate][Robust][Charge noise][Silicon][Compensating][Crosstalk][Neural network]
• https://arxiv.org/abs/2202.07669[RG-inspired neural network][Computing topological invariant]
• https://arxiv.org/abs/2401.01801[Quatum inspired neural network][Geometric modeling]
• https://arxiv.org/abs/2407.20126[Extreme time extrapolation capabilities][Thermodynamic consistency][Physics-inspired neural network][3D microstructure evolution]
• https://arxiv.org/abs/2202.11851[Ultrasensitive][Ultrafast][Gate-tunable two-dimensional photodetector][Ternary rhombohedral ZnIn₂S₄][Optical neural network]
• https://arxiv.org/abs/2203.00390[Mitigating][Hubbard sign problem][Complex-valued neural network]
• https://arxiv.org/abs/1909.00669[Ultra-low energy][High speed LIF neuron][Silicon bipolar impact ionization][MOSFET][Spiking neural network]
• https://arxiv.org/abs/2007.15101[Superconducting nanowire][Spiking element][Neural network]
• https://arxiv.org/abs/2009.14462[Nanoscale room-temperature multilayer skyrmionic synapse][Deep spiking neural network]
• https://arxiv.org/abs/2203.02171[Bilayer-skyrmion][Design][Neuron][Synapse][Spiking neural network]
• https://arxiv.org/abs/2211.06630[Design][Spintronics-based neuronal][Synaptic device][Spiking neural network circuit]
• https://arxiv.org/abs/2307.13320[Autonomous neural information processing][Dynamical memristor circuit]
• https://arxiv.org/abs/2302.08458[Solid state neuroscience][Spiking neural network][Time matter]
• https://arxiv.org/abs/2304.04794[Stochastic domain wall-magnetic tunnel junction][Artificial Neuron]Noise-resilient spiking neural network]
• https://arxiv.org/abs/2304.10899[Electromechanical memcapacitive neuron][Energy-efficient spiking neural network]
• https://arxiv.org/abs/2310.07824[On-chip trainable neuron circuit][SFQ-based spiking neural network]
• https://arxiv.org/abs/2311.07787[Hybrid synaptic structure][Spiking neural network realization]
• https://arxiv.org/abs/2402.03767[Magnetic field gated][Current controlled][Spintronic mem-transistor neuron][Spiking neural network]
• https://arxiv.org/abs/2402.19139[Unified evaluation framework][Spiking neural network hardware accelerator][Emerging non-volatile memory device]
• https://arxiv.org/abs/2405.00700[Oxygen vacancies][Modulated VO₂][Neurons and spiking neural network construction]
• https://arxiv.org/abs/2203.07537[Denoising][Feature extraction][Photoemission spectra][Variational auto-encoder neural network]
• https://arxiv.org/abs/2203.08607[Developing potential energy surface][Graphene-based 2D-3D interface][Modified high dimensional neural network][Application][Energy storage]
• https://arxiv.org/abs/2203.09046[Memristive deep belief neural network][Silicon synapses]
• https://arxiv.org/abs/2204.04529[Learning hyperelastic anisotropy][Data via a tensor basis neural network]
• https://arxiv.org/abs/2501.06300[Tensorization][Neural network][Improved privacy and interpretability]
• https://arxiv.org/abs/2204.11904[Neural network representation][Minimally entangled typical thermal state]
• https://arxiv.org/abs/2406.19957[Neural network representation][Multiphase equations of state]
• https://arxiv.org/abs/2503.07619[Physics- and data-driven active learning][Neural network representation][Free energy function][Material][Statistical mechanics]
• https://arxiv.org/abs/2204.13724[Predicting magnetic edge behaviour][Graphene][Neural network]
• https://arxiv.org/abs/2206.01693[Three-dimensional microstructure generation][Generative adversarial neural network][Continuum micromechanics]
• https://arxiv.org/abs/2312.02479[Application][Domain adversarial neural network][Phase transition][3D Potts model]
• https://arxiv.org/abs/2206.02496[Noise signal][Input data][Self-organized neural network]
• https://arxiv.org/abs/2206.03701[Prediction][Electromotive force][Magnetic shape memory alloy][MSMA][Constitutive model][Generalized regression neural network]
• https://arxiv.org/abs/2206.07697[MACE][Higher order equivariant message passing neural network][Fast][Accurate][Force field]
• https://arxiv.org/abs/2411.08911[Message passing neural network surrogate model][Bond-associated peridynamic material correspondence formulation]
• https://arxiv.org/abs/2206.07753[Sample generation][Spin-fermion model][Neural network]
• https://arxiv.org/abs/2206.10908[Neural network][Quick access][Digital twin][Scanning physical properties measurement]
• https://arxiv.org/abs/2207.06512[Generalized framework][Microstructural optimization][Neural network]
• https://arxiv.org/abs/2207.07428[Automatic detection][Equiaxed dendrites][Computer vision neural network]
• https://arxiv.org/abs/2208.02235[Quantum-inspired][Tensor neural network][Partial differential equation]
• https://arxiv.org/abs/2403.06084[pETNNs][Partial evolutionary tensor neural network][Solving time-dependent partial differential equation]
• https://arxiv.org/abs/2403.15073[Inclusion of charge and spin states][Cartesian tensor neural network potential]
• https://arxiv.org/abs/2502.21317[Assessing zero-shot generalisation behaviour][Graph-neural-network interatomic potential]
• https://arxiv.org/abs/2307.02327[Equivariant graph neural network interatomic potential][Green-Kubo thermal conductivity][Phase change material]
• https://arxiv.org/abs/2402.03789[Scalable parallel algorithm][Graph neural network interatomic potential][Molecular dynamics simulation]
• https://arxiv.org/abs/2305.11805[PANNA 2.0][Efficient neural network interatomic potential][New architecture]
• https://arxiv.org/abs/2402.16628[Single neuromorphic memristor][Closely emulates multiple synaptic mechanism][Energy efficient neural network]
• https://arxiv.org/abs/2408.14680[On-chip learning][Memristor-based neural network][Assessing accuracy][Efficiency][Device variation][Conductance error][Input noise]
• https://arxiv.org/abs/2112.04636[Artificial neural network interatomic potential][Dislocation][Fracture][Molybdenum]
• https://arxiv.org/abs/1912.01398[TeaNet][Universal neural network interatomic potential][Inspired][[Iterative electronic relaxation]
• https://arxiv.org/abs/2409.01315[Multi-frequency neural born iterative method][Solving 2-D inverse scattering problem]
• https://arxiv.org/abs/2102.04085[Automated approach][Developing neural network interatomic potential][FLAME]
• https://arxiv.org/abs/2208.11863[Ab initio construction][Full phase diagram][MgO-CaO eutectic system][Neural network interatomic potential]
• https://arxiv.org/abs/2310.02904[Spline-based neural network interatomic potential][Blending classical][Machine learning model]
• https://arxiv.org/abs/2312.10856[Neural network interatomic potential][Open surface nano-mechanics application]
• https://arxiv.org/abs/2402.13984[Stability-aware training][Neural network interatomic potential][Differentiable Boltzmann estimator]
• https://arxiv.org/abs/2404.11587[Entropy-driven polymorphic stability][Aspirin][Accurate neural network interatomic potential]
• https://arxiv.org/abs/2208.12984[Si plate radius influence][Photoacoustic signal][Processed][Neural network]
• https://arxiv.org/abs/2209.01908[Ensemble][Pre-trained neural network][Segmentation][Quality detection][Transmission electron microscopy image]
• https://arxiv.org/abs/2308.08934[Data imbalance][Molecular property prediction][Pre-training]
• https://arxiv.org/abs/2209.05609[Neural network approach][Predict Gibbs free energy][Ternary solid solution]
• https://arxiv.org/abs/2210.00336[Uncertainty-aware prediction][Molecular X-ray absorption spectra][Neural network ensemble]
• https://arxiv.org/abs/2210.10507[Predicting oxide glass properties][Low complexity neural network][Physical and Chemical descriptors]
• https://arxiv.org/abs/2211.02183[Photoacoustic characterization][TiO₂ thin-film][Deposited][Silicon substrate][Neural network]
• https://arxiv.org/abs/2210.13955[General framework][E(3)-equivariant neural network representation][Density functional theory Hamiltonian]
• https://arxiv.org/abs/2305.03804[Equivariant neural network][Spin dynamics simulation][Itinerant magnet]
• https://arxiv.org/abs/2211.03198[Gauge equivariant neural network][2+1D U(1) gauge theory simulation][Hamiltonian formulation]
• https://arxiv.org/abs/2306.01558[Accelerating][Electronic-structure calculation][Magnetic system][Equivariant neural network]
• https://arxiv.org/abs/2312.05388[Higher-order equivariant neural network][Charge density prediction]
• https://arxiv.org/abs/2503.08305[ELECTRA][Symmetry-breaking cartesian network][Charge density prediction][Floating orbital]
• https://arxiv.org/abs/2312.14680[DeepGreen][Equivariant neural network][Green's function][Molecules and materials]
• https://arxiv.org/abs/2402.04864[Equivariant neural network force field][Magnetic material]
• https://arxiv.org/abs/2412.19353[SuperSalt][Equivariant neural network force field][Multicomponent molten salts system]
• https://arxiv.org/abs/2402.16204[Transferable water potential][Equivariant neural network]
• https://arxiv.org/abs/2403.05249[Representing electronic wave function][Sign equivariant neural network]
• https://arxiv.org/abs/2502.15376[Learning Chern number][Topological insulator][Gauge equivariant neural network]
• https://arxiv.org/abs/2309.00195[Uncertainty estimate][Equivariant-neural-network-ensembles interatomic potential]
• https://arxiv.org/abs/2211.16684[Capturing long-range interaction][Reciprocal space neural network]
• https://arxiv.org/abs/2502.02748[ReGNet][Reciprocal space-aware long-range modeling][Multi-property prediction][Crystal]
• https://arxiv.org/abs/2305.13992[Liouville space neural network representation][Density matrices]
• https://arxiv.org/abs/2301.02683[Classifying topological neural network][Quantum state][Diffusion map]
• https://arxiv.org/abs/2302.02523[Principle][Learning sign rule][Neural network][Qubit lattice model]
• https://arxiv.org/abs/2302.04906[High pressure and temperature][Neural network][Reactive force field][Energetic material]
• https://arxiv.org/abs/2302.12629[Quantifying noise limitations][Neural network segmentation][High-resolution transmission electron microscopy]
• https://arxiv.org/abs/2303.02876[Finding metastable skyrmionic structure][Metaheuristic perturbation-driven neural network]
• https://arxiv.org/abs/2302.14397[Neural network][Constitutive modeling][Universal function approximator][Advanced model][Integration of Physics]
• https://arxiv.org/abs/2303.11699[Neural network][Trained][Synthetically generated crystal][Extract structural information][ICSD powder X-ray diffractogram]
• https://arxiv.org/abs/2304.02957[Neural network kinetics][Diffusion multiplicity][B2 ordering][Compositionally complex alloy]
• https://arxiv.org/abs/2305.00776[Characterizing exceptional point][Neural network]
• https://arxiv.org/abs/2305.00003[Neural network accelerated process design][Polycrystalline microstructure]
• https://arxiv.org/abs/2305.07660[Using limited neural network][Assess relative mechanistic influence][Shock heating][Granular solid]
• https://arxiv.org/abs/2305.19546[Prediction][Born effective charge][Neural network][Ion migration][Electric field][Application][Crystalline][Amorphous][Li₃PO₄]
• https://arxiv.org/abs/2306.08383[Neural network][Tool][Design][Amorphous metal alloy][Desired elastoplastic properties][Metals 13(4), 812 (2023)]
• https://arxiv.org/abs/2307.00911[Reactive neural network framework][Water-loaded acidic zeolite]
• https://arxiv.org/abs/2503.10021[DGNet][Neural network framework][Discontinuous Galerkin method]
• https://arxiv.org/abs/2307.02212[Electric polarization][Many-body neural network ansatz][Phys. Rev. Lett. 132, 176401 (2024)]
• https://arxiv.org/abs/2308.12722[Accelerated neural network training][Dimensionality reduction][High-throughput screening][Topological material]
• https://arxiv.org/abs/2310.02122[Minimalist neural networks training][Phase classification][Diluted-Ising model]
• https://arxiv.org/abs/2310.05607[Neural network variational Monte Carlo][Positronic chemistry]
• https://arxiv.org/abs/2310.08578[Neural network approach][Quasiparticle dispersion][Doped antiferromagnet]
• https://arxiv.org/abs/2310.10434[Equivariant matrix function][Neural network]
• https://arxiv.org/abs/2310.10020[Optimized nanodevice fabrication][Clean transfer][Graphene][Polymer mixture][Experiment][Neural network based simulation]
• https://arxiv.org/abs/2405.04524[Neural network based deep learning analysis][Semiconductor quantum dot qubit][Automated control]
• https://arxiv.org/abs/2405.19008[Mechanism and kinetics][Sodium diffusion][Na-feldspar][Neural network based atomistic simulation]
• https://arxiv.org/abs/2310.11245[Neural network approach][Rapid prediction][Metal-supported borophene properties]
• https://arxiv.org/abs/2310.16491[TSONN][Time-stepping-oriented neural network][Solving partial differential equation]
• https://arxiv.org/abs/2311.13799[Neural network thermodynamics]
• https://arxiv.org/abs/2312.05846[Mie sensing][Neural network][Recognition][Nano-object parameter][Invisibility point][Restricted model]
• https://arxiv.org/abs/2312.13511[Symmetry-enforcing neural network][Application][Constitutive modeling]
• https://arxiv.org/abs/2401.06936[Accelerated sampling][Rare event][Neural network bias potential]
• https://arxiv.org/abs/2401.09301[Material informatics][Neural network][Ab-Initio electron charge densities][Role of transfer learning]
• https://arxiv.org/abs/2401.14676[Spintronic virtual neural network][Voltage controlled ferromagnet][Associative memory]
• https://arxiv.org/abs/2402.02362[Unification][Symmetries inside neural network][Transformer][Feedforward][Neural ODE]
• https://arxiv.org/abs/2406.00091[Transformer neural network][Qquantum simulator][Hybrid approach][Simulating strongly correlated system]
• https://arxiv.org/abs/2407.06039[Predicting VCSEL emission properties][Transformer neural network]
• https://arxiv.org/abs/2411.19253[Quantum feedback control][Transformer neural network architecture]
• https://arxiv.org/abs/2403.01776[Hybrid data-driven][Physics-informed regularized learning][Cyclic plasticity][Neural network]
• https://arxiv.org/abs/2403.03286[Neural network backflow][Ab-initio quantum chemistry]
• https://arxiv.org/abs/2502.18843[Efficient optimization][Neural network backflow][Ab-initio quantum chemistry]
• https://arxiv.org/abs/2403.16819[Neural network approach][Two-body system][Spin and isospin degrees of freedom]
• https://arxiv.org/abs/2404.15118[Identifying phase transition][Physical system][Neural network]Neural architecture search perspective]
• https://arxiv.org/abs/2404.03863[Establishing][Relationship][Generalized crystallographic texture][Macroscopic yield surface][Partial input convex neural network]
• https://arxiv.org/abs/2405.16769[Learning phase transition][Siamese neural network]
• https://arxiv.org/abs/2406.08318[Invariant multiscale neural network][Data-scarce scientific application]
• https://arxiv.org/abs/2406.15873[NeuralSCF][Neural network self-consistent field][Density functional theory]
• https://arxiv.org/abs/2406.17645[Simulating moire quantum matter][Neural network]
• https://arxiv.org/abs/2408.03263[Multiscale modeling framework][Constrained fluid][Complex boundaries][Twin neural networks]
• https://arxiv.org/abs/2408.04073[Accelerating crystal structure search][Active learning][Neural network][Rapid relaxation]
• https://arxiv.org/abs/2408.11395[Short introduction][Neural network][Application][Earth and materials science]
• https://arxiv.org/abs/2409.20320[Experimental online quantum dot][Charge autotuning][Neural network]
• https://arxiv.org/abs/2408.16915[SOLAX][Python solver][Fermionic quantum system][Neural network support]
• https://arxiv.org/abs/2409.05240[Physics-enforced neural network][Predict polymer melt viscosity]
• https://arxiv.org/abs/2410.07451[Collective variables][Neural network][Empirical time evolution][Scaling law]
• https://arxiv.org/abs/2410.08882[Unified quantum framework][Electrons and ions][Self-consistent harmonic approximation][Neural network curved manifold]
• https://arxiv.org/abs/2410.23152[Classical neural network][Represent quantum states]
• https://arxiv.org/abs/2411.13185[Recovering Mullins damage hyperelastic behaviour][Physics augmented neural network][Journal of the Mechanics and Physics of Solids, Vol. 193, 105839 (2024)]
• https://arxiv.org/abs/2411.13644[Predicting interacting Green's function][Neural networks]
• https://arxiv.org/abs/2412.01652[Teaching oxidation state][Neural network]
• https://arxiv.org/abs/2412.00618[Solving and visualizing fractional quantum Hall wavefunction][Neural network]
• https://arxiv.org/abs/2412.03319[Comparison][Tensor method][Neural network][Electronic structure calculation]
• https://arxiv.org/abs/2412.06677[Neural network][Nanoscale friction]
• https://arxiv.org/abs/2412.05714[Finite element neural network interpolation][Part II][Hybridisation][Proper generalised decomposition][Non-linear surrogate modelling]
• https://arxiv.org/abs/2412.05719[Finite element neural network interpolation][Part I][Interpretable][Adaptive discretization][Solving PDEs]
• https://arxiv.org/abs/2412.10865[Room temperature exciton-polariton neural network][Perovskite crystal]
• https://arxiv.org/abs/2412.10821[Graph attention Hamiltonian neural network][Lattice system analysis model][Structural learning]
• https://arxiv.org/abs/2412.13200[Forward and inverse simulation][Pseudo-two-dimensional model][Lithium-ion batteries][Neural network]
• https://arxiv.org/abs/2412.16462[Condensed stein variational gradient descent][Uncertainty quantification][Neural network]
• https://arxiv.org/abs/2412.17926[Contemporary implementation][Spiking bio-inspired neural network]
• https://arxiv.org/abs/2412.19381[Reconstruction][Non-trivial magnetization textures][Magnetic field image][Neural network]
• https://arxiv.org/abs/2406.05175[Robust quantum dot][Charge autotuning][Neural network uncertainty][Mach. Learn.: Sci. Technol. 5, 045034 (2024)]
• https://arxiv.org/abs/2502.07076[Neural network][Structural characterization][Polymeric porous material]
• https://arxiv.org/abs/2212.00782[Variational neural-network ansatz][Continuum quantum field theory]
• https://arxiv.org/abs/2401.14243[Variational neural and tensor network approximations][Thermal state]
• https://arxiv.org/abs/2406.00193[Learning topological state][Randomized measurement][Variational tensor network tomography]
• https://arxiv.org/abs/2502.10949[Learning][Exact time integration algorithm][Initial value problem][Randomized neural network]
• https://arxiv.org/abs/2212.02204[Neural quantum state][Volume-law ground state]
• https://arxiv.org/abs/1909.11150[Exascale deep learning][Scientific inverse problem]
• https://arxiv.org/abs/2409.08362[Deep Ritz][Finite element method][Neural network method][Trained with finite elements]
• https://arxiv.org/abs/2106.11623[Deep neural network][Inverse problem][Mesoscopic physics][Characterization][Disorder configuration][Quantum transport property]
• https://arxiv.org/abs/1810.12183[Solving inverse problem][Multi-scale deep convolutional neural network]
• https://arxiv.org/abs/2304.13860[Enhancing inverse problem solution][Accurate surrogate simulator][Promising candidate]
• https://arxiv.org/abs/2107.01584[Decoding][DC and optical conductivities][Disordered MoS₂ film][Inverse problem]
• https://arxiv.org/abs/2402.09338[Neural network][Asymptotic behaviour][Resolution][Inverse problem]
• https://arxiv.org/abs/2412.07010[TAE][Model-constrained Tikhonov autoencoder approach][Forward and inverse problems]
• https://arxiv.org/abs/2503.07070[PIED][Physics-informed experimental design][Inverse problem]
• https://arxiv.org/abs/2404.02387[Inversion problem][Optical spectrum data][Physics-guided machine learning]
• https://arxiv.org/abs/2201.00722[Predicting peak stress][Microstructured Material][Convolutional encoder-decoder learning]
• https://arxiv.org/abs/2002.03032[Automatic design][Mechanical metamaterial actuator]
• https://arxiv.org/abs/2001.11814[Complete solution][Tight binding model][Cayley tree]
• https://arxiv.org/abs/1903.09499[Learning magnetization dynamics]
• https://arxiv.org/abs/1709.07082[Machine learning][Molecular properties][Locality][Active learning]
• https://arxiv.org/abs/2501.14384[Efficiently charting][Space][Mixed vacancy-ordered perovskite][Machine-learning encoded atomic-site information]
• https://arxiv.org/abs/2503.10837[Lessons][Trenches][Evaluating machine-learning system][Materials science]
• https://arxiv.org/abs/2109.10890[Machine-learning enabled search][Next-generation catalyst][Hydrogen evolution reaction]
• https://arxiv.org/abs/2211.10342[Machine-learning enabled optimization][Atomic structure][Fractional existence]
• https://arxiv.org/abs/2405.14776[Kinetics of orbital ordering][Cooperative Jahn-Teller model][Machine-learning enabled large-scale simulation]
• https://arxiv.org/abs/2412.21072[Enhanced coarsening][Charge density wave][Electron correlation][Machine-learning enabled large-scale dynamical simulation]
• https://arxiv.org/abs/2204.00735[Genetic programming-based learning][Carbon interatomic potential][Materials discovery]
• https://arxiv.org/abs/2110.06888[Exploring causal physical mechanism][Non-gaussian linear model][Deep kernel learning][Ferroelectric domain structure]
• https://arxiv.org/abs/2410.03173[Rapid optimization][High dimensional space][Deep kernel learning][Augmented genetic algorithm]
• https://arxiv.org/abs/2303.14554[Deep kernel method][Learn better][From cards to process optimization]
• https://arxiv.org/abs/1810.07310[Prediction][Atomization energy][Graph kernel][Active learning]
• https://arxiv.org/abs/1806.10567[Accelerating high-throughput searche][New alloys][Active learning][Interatomic potential]
• https://arxiv.org/abs/1810.11890[Active learning][Uniformly accurate inter-atomic potential][Materials Simulation]
• https://arxiv.org/abs/1904.10692[Speeding up][Quantum few-body calculation][Active learning]
• https://arxiv.org/abs/1909.11654[Active Learning][Coarse-grained energy landscape][Water cluster][Sparse training data]
• https://arxiv.org/abs/1912.04596[Active-learning-based efficient prediction][Ab-initio atomic energy][Fe random grain boundary model][millions of atoms]
• https://arxiv.org/abs/2004.13158[Operando active learning][Interatomic interaction][Large-scale simulation]
• https://arxiv.org/abs/2005.03014[Active learning][One-dimensional density functional theory]
• https://arxiv.org/abs/2005.11488[Hierarchical active-learning framework][Classifying structural motif][Atomic resolution microscopy]
• https://arxiv.org/abs/2006.03674[Active learning][Neural network model][Gold Cluster][Bulk][Sparse first principles training data]
• https://arxiv.org/abs/2007.08555[MLIP package][Moment tensor potential][MPI][Active learning]
• https://arxiv.org/abs/2010.06896[Efficient estimation][Material property curves and surfaces][Active learning]
• https://arxiv.org/abs/2103.00608[Active learning based generative design][Discovery][Wide bandgap material]
• https://arxiv.org/abs/2108.06037[Experimental discovery][Structure-property relationship][Ferroelectric material][Active learning]
• https://arxiv.org/abs/2110.08136[Active learning][Molecular dynamics simulation][High melting temperature alloy]
• https://arxiv.org/abs/2111.09659[Polymer sequence design][Active learning]
• https://arxiv.org/abs/2203.10181[Active learning][Open experimental environment][Selecting][Right information channel(s)][Predictability][Deep kernel learning]
• https://arxiv.org/abs/2204.05838[Benchmarking active learning strategies][Materials optimization][Discovery]
• https://arxiv.org/abs/2208.05912[Atomistic fracture][bcc iron][Active learning][Gaussian approximation potential]
• https://arxiv.org/abs/2208.05444[Rapid exploration][32.5M compound chemical space][Active learning][Discover density functional approximation][Iinsensitive][Synthetically][Accessible][Transitional metal chromophore]
• https://arxiv.org/abs/2210.16364[Uncertainty driven active learning][Coarse grained free energy model]
• https://arxiv.org/abs/2211.07881[ET-AL][Entropy-targeted active learning][Bias mitigation][Materials data]
• https://arxiv.org/abs/2212.07310[Exploring][Microstructural origin][Conductivity][Hysteresis][Metal halide perovskite][Active learning driven automated scanning probe microscopy]
• https://arxiv.org/abs/2212.08716[Active learning strategies][Atomic cluster expansion model]
• https://arxiv.org/abs/2302.01603[Towards active learning][Stopping criterion][Sequential sampling][Grain boundary degrees of freedom]
• https://arxiv.org/abs/2411.10822[Data-efficient sequential learning framework][Melt pool defect classification][Laser powder bed fusion]
• https://arxiv.org/abs/2303.12924[Active learning][Sensitivity analysis][¦Ã¡ì(L12) precipitate morphology][Ternary Co-based superalloy]
• https://arxiv.org/abs/2309.06786[Lattice thermal conductivity][First principles][Active learning][Gaussian process regression]
• https://arxiv.org/abs/2310.16168[Role of multifidelity data][Sequential active learning][Materials discovery campaign][Case study][Electronic bandgap]
• https://arxiv.org/abs/2311.01987[Generalization][Graph-based active learning][Relaxation strategies][Across materials]
• https://arxiv.org/abs/2312.05343[Active learning approach][Simulation][Strongly correlated matter][Ghost Gutzwiller approximation]
• https://arxiv.org/abs/2401.05568[Phase discovery][Active learning][Application][Structural phase transition][Equiatomic NiTi]
• https://arxiv.org/abs/2401.16487[Active learning][Boltzmann sampler][Potential energies][Quantum mechanical accuracy]
• https://arxiv.org/abs/2402.15582[Probabilistic prediction][Material stability][Integrating convex hull][Active learning]
• https://arxiv.org/abs/2403.06329[Active learning][Rapid targeted synthesis][Compositionally complex alloy]
• https://arxiv.org/abs/2404.07074[Multiscale structure-property discovery][Active learning][Scanning tunneling microscopy]
• https://arxiv.org/abs/2407.06051[Foam][3D spatially programmed mechanics][Autonomous active learning][Viscous thread printing]
• https://arxiv.org/abs/2407.18731[Exploring quantum active learning][Materials design and discovery]
• https://arxiv.org/abs/2412.00504[Quantum active learning][Structural determination][Doped nanoparticle][Case study of 4Al@Si₁₁]
• https://arxiv.org/abs/2408.02071[Scientific exploration][Expert knowledge][SEEK][Autonomous scanning probe microscopy][Active learning]
• https://arxiv.org/abs/2409.07042[Active learning][Discovering complex phase diagram][Gaussian processes]
• https://arxiv.org/abs/2411.02740[Information-matching approach][Optimal experimental design][Active learning]
• https://arxiv.org/abs/2411.13361[Integration][Active learning][MCMC sampling][Efficient Bayesian calibration][Mechanical properties]
• https://arxiv.org/abs/2411.13689[Accelerating active learning materials discovery][FAIR data][Workflow][Alloy melting temperature]
• https://arxiv.org/abs/2501.16475[Symmetry- and gradient-enhanced Gaussian process regression][Active learning][Potential energy surface][Porous material]
• https://arxiv.org/abs/2502.16984[Active learning][Conditional inverse design][Crystal generation][Foundation atomic model]
• https://arxiv.org/abs/2007.04145[Structure motif centric learning framework][Inorganic crystalline system]
• https://arxiv.org/abs/2103.01462[Neural evolution structure generation][High entropy alloy]
• https://arxiv.org/abs/2404.15745[Reconstructing][Magnetic Field][Arbitrary Domain][Data-driven Bayesian Method][Numerical Simulation]
• https://arxiv.org/abs/1704.07423[High-dimensional materials and process optimization][Data-driven experimental design][Well-calibrated uncertainty estimates]
• https://arxiv.org/abs/1806.07989[Data-driven][Magnetic two-dimensional material]
• https://arxiv.org/abs/1901.04832[Exploring the 3D architecture][Deep material network][Data-driven multiscale mechanics]
• https://arxiv.org/abs/2402.11102[Universal design methodology][Printable microstructural material][New deep generative learning model][Application][Piezocomposite]
• https://arxiv.org/abs/2407.06489[T2MAT (text-to-materials)][Universal framework][Generating material structure][Goal properties][Single sentence]
• https://arxiv.org/abs/2403.11686[Crystalformer][Infinitely connected attention][Periodic structure encoding]
• https://arxiv.org/abs/1908.03665[Robust data-driven approach][Predicting][Configurational energy][High entropy alloy]
• https://arxiv.org/abs/1909.00949[Data-driven approach][Encoding][Decoding][3-D crystal structure]
• https://arxiv.org/abs/1902.10282[Prediction of activation energy barrier][Island diffusion process][Data-driven approach]
• https://arxiv.org/abs/2102.01131[Data-driven approach][Parameterize SCAN+U][Accurate description][3d transition metal oxide thermochemistry]
• https://arxiv.org/abs/2108.13206[Data-driven approach][Identification][Novel organic ferroelectrics]
• https://arxiv.org/abs/2408.14804[Data-driven approach][Learning optimal form][Constitutive relation][Models describing lithium plating][Battery cell]
• https://arxiv.org/abs/2501.05762[Development][Comparison][Model-based][Data-driven approache][Prediction][Mechanical properties][Lattice structure][Journal of Materials Engineering and Performance, 2024]
• https://arxiv.org/abs/1902.09770[Data-driven exploration][Pressure-induced superconductivity][AgIn₅Se₈]
• https://arxiv.org/abs/2002.12208[Gaussian process state][Data-driven representation][Quantum many-body physics]
• https://arxiv.org/abs/2005.11596[Data-driven][Kinetic energy density fitting][Orbital-free DFT][Linear vs Gaussian process regression]
• https://arxiv.org/abs/2010.01765[Voting data-driven regression learning][Discovery][Functional material][Application][Two-dimensional ferroelectric material]
• https://arxiv.org/abs/2006.07523[Data-driven determination][Spin Hamiltonian parameter][Uncertainty][Zigzag-chain compound KCu₄P₃O₁₂]
• https://arxiv.org/abs/2007.01831[JARVIS][Integrated infrastructure][Data-driven Materials Design]
• https://arxiv.org/abs/2305.11842[Recent progress][JARVIS infrastructure][Next-generation data-driven materials design]
• https://arxiv.org/abs/2011.03191[Shape-dependent local strain][Gold nanorod][Data-driven atomic-resolution electron microscopy analysis]
• https://arxiv.org/abs/2011.08551[Variation free density functional theory][Data-driven][Stochastic optimization]
• https://arxiv.org/abs/1806.09284[Two pressure-induced superconducting transitions][Explored][Data-driven materials search][New approach][Develop novel functional materials][Thermoelectric][Superconducting]
• https://arxiv.org/abs/1902.09395[Data-driven material Model][Atomistic simulation]
• https://arxiv.org/abs/1907.05644[Data-driven materials science][Status][Challenge][Perspective]
• https://arxiv.org/abs/2102.01154[Optimizing accuracy and efficacy][Data-driven materials discovery][Solar production of hydrogen]
• https://arxiv.org/abs/2102.02263[Robust model benchmarking][Bias-imbalance][Data-driven materials science][MODNet]
• https://arxiv.org/abs/1904.05859[Big-data-driven materials science][FAIR data infrastructure]
• https://arxiv.org/abs/2502.05682[Constitutive Kolmogorov-Arnold networks][CKANs][Combining accuracy][Interpretability][Data-driven material modeling]
• https://arxiv.org/abs/2102.07320[Data-driven analysis][Electronic-structure factors controlling][Work function][Perovskite oxide]
• https://arxiv.org/abs/2103.04875[Data-driven sensitivity analysis][Total-reflection high-energy positron diffraction (TRHEPD) experiment][Si₄O₅N₃/6H-SiC(0001)-(3^1/2x3^1/2)R30]
• https://arxiv.org/abs/2103.07957[JAMIP][Artificial-intelligence][Data-driven infrastructure][Computational materials informatics]
• https://arxiv.org/abs/2103.12396[Data-driven][Rate-dependent][Fracture mechanics]
• https://arxiv.org/abs/2105.12784[Data-driven relationship][Atomic structure][Physical property]Holistic view][Materials science fundamental]
• https://arxiv.org/abs/2107.13147[Mechanical cloak][Data-Driven][Aperiodic metamaterial design]
• https://arxiv.org/abs/2108.02236[Data-driven analysis][Grain-growth kinetics][Duplex and triplex systems]
• https://arxiv.org/abs/2108.04883[Data-driven peridynamic continuum model][Upscaling molecular dynamics]
• https://arxiv.org/abs/2108.06667[Realizing][Data-driven][Computational discovery][Metal-organic framework Catalyst]
• https://arxiv.org/abs/2110.02070[Data-driven electron microscopy][Electron diffraction imaging][Materials structural properties]
• https://arxiv.org/abs/1803.05035[Autonomous data-driven design][Inorganic material][AFLOW]
• https://arxiv.org/abs/2201.11289[High throughput data-driven design][Laser crystallized 2D MoS₂ chemical sensor]
• https://arxiv.org/abs/2101.10773[Data-driven design][New class][Rare-earth free permanent magnet]
• https://arxiv.org/abs/2109.10798[Data-driven design][Novel halide perovskite alloy]
• https://arxiv.org/abs/2211.08014[Data-driven design][New catalytic material][Methane oxidation][Site isolation concept]
• https://arxiv.org/abs/2307.05506[Data-driven design][Metamaterial][Multiscale system][Review]
• https://arxiv.org/abs/2310.03633[Data-driven design][Multilayer hyperbolic metamaterial][Near-field thermal radiative modulator][High modulation contrast]
• https://arxiv.org/abs/2204.09827[Data-driven design][New organic semiconductor][Electronic structure chart]
• https://arxiv.org/abs/2312.12694[Data-driven design][High pressure hydride superconductor][DFT][Deep learning]
• https://arxiv.org/abs/2410.20114[Data-driven design][High-temperature superconductivity][Ternary hydrides under pressure]
• https://arxiv.org/abs/2405.09897[Towards informatics-driven design][Nuclear waste form]
• https://arxiv.org/abs/2110.01366[Data-driven quest][Two-dimensional][Non-van der Waals material]
• https://arxiv.org/abs/2110.12870[Data-driven][Intrinsic localized mode detection][Classification][One-dimensional crystal lattice model]
• https://arxiv.org/abs/2111.05129[Thermodynamics][Dielectric response][BaTiO₃][Data-driven modeling]
• https://arxiv.org/abs/2201.11106[Data-driven][Constrained optimization][Semi-local exchange][Non-local correlation functional][Materials and surface chemistry]
• https://arxiv.org/abs/2201.11647[Data-driven time propagation][Quantum system][Neural network]
• https://arxiv.org/abs/2202.02380[Data-driven materials discovery][Synthesis][Machine learning method]
• https://arxiv.org/abs/2202.11355[Superconductive material][MgB₂-like structure][Data-driven screening]
• https://arxiv.org/abs/2205.06674[Model-free data-driven viscoelasticity][Frequency domain]
• https://arxiv.org/abs/2202.01373[Data-driven discovery][High performance][Layered van der Waals piezoelectric NbOI₂]
• https://arxiv.org/abs/2311.14196[Data-driven discovery][Dynamics][Time-resolved coherent scattering]
• https://arxiv.org/abs/2405.11379[Symmetry-guided data-driven discovery][Native quantum defect][Two-dimensional material]
• https://arxiv.org/abs/2405.12643[Data-driven discovery][Robust optimization][Semiconductor nanowire laser]
• https://arxiv.org/abs/2206.13596[Data-driven][Thiele equation approach][Solving][Full nonlinear spin-torque vortex oscillator dynamics]
• https://arxiv.org/abs/2208.09177[Identification][Dislocation reaction kinetics][Complex dislocation network][Continuum modeling][Data-driven method]
• https://arxiv.org/abs/2209.02046[Data-driven prediction][Room temperature density][Multicomponent silicate-based glasses]
• https://arxiv.org/abs/2302.03843[Data-driven prediction][Complex crystal structure][Dense lithium]
• https://arxiv.org/abs/2408.10814[Data-driven prediction][Structure][Metal-organic framework]
• https://arxiv.org/abs/2411.14034[Assessing data-driven prediction][Band gap][Electrical conductivity][Transparent conducting material]
• https://arxiv.org/abs/2209.10709[Data-driven interpretation][Stability][Molecular crystal]
• https://arxiv.org/abs/2210.06027[Experimental data management platform][Data-driven investigation][Combinatorial alloy thin film]
• https://arxiv.org/abs/2211.12971[Cooperative data-driven modeling]
• https://arxiv.org/abs/2303.06743[Data-driven][Statistical reduced-order modeling][Quantification][Polycrystal mechanics][Leading to porosity-based ductile damage]
• https://arxiv.org/abs/2303.14033[Data-driven estimation][Transfer integral][Undoped cuprate]
• https://arxiv.org/abs/2304.04809[Data-driven framework][Structure-property correlation][Ordered and disordered cellular metamaterials]
• https://arxiv.org/abs/2410.19204[Data-driven framework][Accelerate][Discovery][Hybrid cathode material][Metal-based batteries]
• https://arxiv.org/abs/2412.03970[Data-driven framework][Discovering fractional differential equation][Complex system]
• https://arxiv.org/abs/2304.13897[Physics-informed data-driven discovery][Constitutive model][Application][Strain-rate-sensitive soft material]
• https://arxiv.org/abs/2305.01806[Data-driven][Physics-informed descriptor][Cation ordering][Multicomponent oxide]
• https://arxiv.org/abs/2305.19279[Data-driven game][Computational mechanics]
• https://arxiv.org/abs/2306.11071[ColabFit exchange][Open-access dataset][Data-driven interatomic potential]
• https://arxiv.org/abs/2310.08593[Data-driven method][Diffusivity prediction][Nuclear fuel]
• https://arxiv.org/abs/2310.10695[Data-driven score-based model][Generating stable structure][Adaptive crystal cell]
• https://arxiv.org/abs/2311.05571[Effective data-driven collective variable][Free energy calculation][Metadynamics of path]
• https://arxiv.org/abs/2401.06070[Peridynamic neural operator][Data-driven nonlocal constitutive model][Complex material response]
• https://arxiv.org/abs/2408.15097[Data-driven nonlinear deformation design][3D-printable shell]
• https://arxiv.org/abs/2401.11393[Data-driven compression][Electron-phonon interaction]
• https://arxiv.org/abs/2402.07289[Virtual reassembling][3D fragment][Data-driven analysis][Fracture mechanism][Composite material]
• https://arxiv.org/abs/2402.13685[Data-driven forecasting][Non-equilibrium solid-state dynamics]
• https://arxiv.org/abs/2402.16433[Data-driven acceleration][Multi-physics simulation]
• https://arxiv.org/abs/2402.19462[Accelerating materials discovery][Polymer solar cell][Data-driven insight][Natural language processing]
• https://arxiv.org/abs/2403.15625[Integrated workflows and interfaces][data-driven semi-empirical electronic structure calculation]
• https://arxiv.org/abs/2402.12520[Data-driven study][Composition-dependent phase compatibility][NiTi shape memory alloy]
• https://arxiv.org/abs/2406.11004[Data-driven study][Enthalpy][Mixing][Liquid phase]
• https://arxiv.org/abs/2406.19662[Finite basis Kolmogorov-Arnold network][Domain decomposition][Data-driven][Physics-informed problem]
• https://arxiv.org/abs/2407.00975[Data-driven approximation][Topological insulator system]
• https://arxiv.org/abs/2407.05050[Sparse identification][Quasipotential][Combined data-driven method]
• https://arxiv.org/abs/2409.01989[Improving electrolyte performance][Target cathode loading][Interpretable data-driven approach]
• https://arxiv.org/abs/2409.09092[Data-driven virtual test-bed][Blown powder][Directed energy deposition process]
• https://arxiv.org/abs/2409.11080[Data-driven stochastic 3D modeling][Nanoporous binder-conductive additive phase][Battery cathode]
• https://arxiv.org/abs/2409.11782[Smart Data-driven GRU predictor][SnO₂ thin films Characteristics]
• https://arxiv.org/abs/2412.12846[Data-driven catalyst design][Machine learning approach][Predicting electrocatalytic performance][Hydrogen evolution][Oxygen evolution reaction]
• https://arxiv.org/abs/2503.15339[Data-driven][Azimuthal RHEED construction][In-situ][Crystal growth characterization]
• https://arxiv.org/abs/2502.05635[Data-driven][Low-rank approximation][Electron-hole kernel][Acceleration][Time-dependent GW calculation]
• https://arxiv.org/abs/2209.13700[Data driven approach][Cross-slip modelling][Continuum dislocation dynamics]
• https://arxiv.org/abs/2411.12570[Data driven approach][Classify descriptors][Translating noisy trajectories][Physically-relevant information]
• https://arxiv.org/abs/2403.18441[Physics and data driven model][Prediction][Residual stresses][Machining]
• https://arxiv.org/abs/1812.01966[Managing uncertainty][Data-derived densities][Accelerate density functional theory]
• https://arxiv.org/abs/1808.02114[Definition of a scoring parameter][Identify][low-dimensional material][Component]
• https://arxiv.org/abs/1808.06935[Smart energy model][Atomistic simulation][DFT-driven multifidelity approach]
• https://arxiv.org/abs/2205.12074[Electronic descriptor][Supervised spectroscopic prediction]
• https://arxiv.org/abs/1811.12423[Variational optimization][AI era][Computational graph state][Supervised wave-function optimization]
• https://arxiv.org/abs/2412.16234[Is AI robust enough for scientific research?]
• https://arxiv.org/abs/2207.01079[DiSCoMaT][Distantly supervised composition extraction][Table][Material science article]
• https://arxiv.org/abs/1902.06836[Graph dynamical network][Unsupervised learning][Atomic scale dynamics]
• https://arxiv.org/abs/1903.05755[Unsupervised learning][Eigenstate phases of Matter]
• https://arxiv.org/abs/2004.14271[Universal approach][Unsupervised classification][Univariate data]
• https://arxiv.org/abs/2406.15004[Dislocation cartography][Representation][Unsupervised classification][Dislocation network][Unique fingerprint]
• https://arxiv.org/abs/2412.20882[Unsupervised classification][Non-Hermitian topological phase][Symmetries]
• https://arxiv.org/abs/2311.02442[Quantum transport][Network][Supervised classification]
• https://arxiv.org/abs/2006.12953[Unsupervised learning][Universal critical behavior][Intrinsic dimension]
• https://arxiv.org/abs/2010.14516[Unsupervised Learning][Non-Hermitian topological phase][Phys. Rev. Lett. 126, 240402 (2021)]
• https://arxiv.org/abs/2101.06892[Unsupervised learning][Ferroic variant][Atomically resolved STEM image]
• https://arxiv.org/abs/2102.11328[Observation][Complexity][Quantum state][Unsupervised learning]
• https://arxiv.org/abs/2103.00467[Unsupervised learning][Atomic environments][Simple features]
• https://arxiv.org/abs/2106.13485[Decoding][Conformal field theory][Supervised to unsupervised learning]
• https://arxiv.org/abs/2107.10468[Unsupervised learning][Topological phase diagram][Topological data analysis]
• https://arxiv.org/abs/2107.14311[Unsupervised learning-based structural analysis][Characteristic low-dimensional space][Local structure][Atomistic simulation]
• https://arxiv.org/abs/2109.06179[Unsupervised learning][Characterize][Heterogeneous][X-ray single particle imaging]
• https://arxiv.org/abs/2111.08747[Unsupervised learning][Symmetry Protected Topological Phase Transition]
• https://arxiv.org/abs/2112.11894[High dimensional fluctuation][Liquid water][Combining chemical intuition][Unsupervised learning]
• https://arxiv.org/abs/2112.13785[Experimental unsupervised learning][Non-Hermitian knotted phase][Solid-state spin]
• https://arxiv.org/abs/2201.02187[Density-of-states similarity descriptor][Unsupervised learning][Materials data]
• https://arxiv.org/abs/2203.04449[Unsupervised learning][Two-component nematicity][STM data][Magic angle bilayer graphene]
• https://arxiv.org/abs/2206.03871[Inorganic crystal structure prototype database][Unsupervised learning][Local atomic environment]
• https://arxiv.org/abs/2212.14813[Unsupervised learning][Structure detection][Plastically deformed crystal]
• https://arxiv.org/abs/2302.01465[Unsupervised learning][Representative local atomic arrangement][Molecular dynamics data]
• https://arxiv.org/abs/2308.01524[Unsupervised learning][Part similarity][Goal-guided accelerated experiment design][Metal additive manufacturing]
• https://arxiv.org/abs/2308.15319[ZundEig][Structure][Proton][Liquid water][Unsupervised learning]
• https://arxiv.org/abs/2309.06613[Unsupervised learning][Nanoindentation data][Infer microstructural detail][Complex material]
• https://arxiv.org/abs/2309.15130[Understanding][Structure of QM7b and QM9][Quantum mechanical dataset][Unsupervised learning]
• https://arxiv.org/abs/2401.17968[Unsupervised learning][Topological non-Abelian braiding][Non-Hermitian band]
• https://arxiv.org/abs/2407.16255[Self-reasoning assistant learning][Non-Abelian gauge fields design]
• https://arxiv.org/abs/2404.11980[Unsupervised learning approach][Quantum wavepacket dynamics][Coupled temporal-spatial correlation]
• https://arxiv.org/abs/2404.14601[Unsupervised learning][Individual Kohn-Sham state][Interpretable representations and consequences][Downstream prediction][Many-body effect]
• https://arxiv.org/abs/2202.09281[Unsupervised and supervised learning][Interacting topological phase][Single-particle correlation function]
• https://arxiv.org/abs/2403.03092[Discovering melting temperature prediction model][Inorganic solid][Combining supervised and unsupervised learning]
• https://arxiv.org/abs/2402.13353[Combining unsupervised and supervised learning][Microscopy][Defect analysis][Full 4H-SiC wafer]
• https://arxiv.org/abs/2202.03767[Unsupervised topological learning approach][Crystal nucleation][Pure Tantalum]
• https://arxiv.org/abs/2210.01894[Crystal nucleation][Al-Ni Alloy][Unsupervised chemical][Topological learning approach]
• https://arxiv.org/abs/2008.07268[Unsupervised interpretable learning][Topological indices invariant][Permutation][Atomic band]
• https://arxiv.org/abs/2306.12272[Structure mining][Unsupervised exploration][Atomic octahedral network]
• https://arxiv.org/abs/2411.12612[Reward driven workflow][Unsupervised explainable analysis][Phases and ferroic variants][Atomically resolved imaging data]
• https://arxiv.org/abs/2311.08585[Unsupervised segmentation][Irradiation¡¾induced order¡¾disorder phase transition][Electron microscopy]
• https://arxiv.org/abs/2401.13600[Unsupervised model learning][Quantum impurity system]
• https://arxiv.org/abs/2405.01320[Unsupervised identification][Local atomic environment][Atomistic potential descriptor]
• https://arxiv.org/abs/2410.20999[Unsupervised tracking][Local and collective defects dynamics][Metals under deformation]
• https://arxiv.org/abs/2410.19836[Upsampling DINOv2 feature][Unsupervised vision task][Weakly supervised materials segmentation]
• https://arxiv.org/abs/2210.07371[Pair distribution function analysis][Oxide defect identification][Feature extraction][Supervised learning]
• https://arxiv.org/abs/2305.04035[Supervised learning][Interacting 2D hard-core boson model][Weak topological insulator][Correlation function]
• https://arxiv.org/abs/2201.04493[Interpretable learning][Voltage][Electrode design][Multivalent metal-ion batteries]
• https://arxiv.org/abs/2009.09932[Supervised learning][Projected entangled pair state]
• https://arxiv.org/abs/2104.11237[Supervised learning algorithm][Interacting topological insulator][Local curvature]
• https://arxiv.org/abs/2105.11475[Semi-supervised learning][Image][Strong rotational disorder][Assembling nanoparticle libraries]
• https://arxiv.org/abs/2403.13673[Employing semi-supervised learning][High-resolution segmentation][Crystal defect]
• https://arxiv.org/abs/2211.02431[Supervised learning][High-dimensional millimeter wave transient absorption data][Age prediction][Perovskite thin-film]
• https://arxiv.org/abs/2307.01560[Robust crystal structure identification][Extreme condition][Density-independent spectral descriptor][Supervised learning]
• https://arxiv.org/abs/2309.04474[Weakly supervised learning][Pattern classification][Serial femtosecond crystallography]
• https://arxiv.org/abs/2402.04992[Challenges and opportunities][Supervised learning][Quantum circuit output]
• https://arxiv.org/abs/2111.01287[Semi-supervised approach][Automatic crystal structure classification]
• https://arxiv.org/abs/1901.08620[Atomistic simulation][Nearly defect-free model][Amorphous silicon][Information-based approach]
• https://arxiv.org/abs/2207.00611[FAIR principles][AI model][Practical application][Accelerated high energy diffraction microscopy]
• https://arxiv.org/abs/2502.17357[Explainable AI model][Binary LJ fluid]
• https://arxiv.org/abs/2201.07174[Molecular and materials basic ontology][MAMBO][Development][First step]
• https://arxiv.org/abs/2203.10171[Neuromorphic metamaterial][Mechanosensing][Perceptual associative learning]
• https://arxiv.org/abs/2211.06373[Adaptive programmable network][Materia neuromorphic computing]
• https://arxiv.org/abs/2412.16804[Development][Neuromorphic network][BioSFQ circuits]
• https://arxiv.org/abs/2412.17571[HPCNeuroNet][Neuromorphic approach][Merging SNN temporal dynamics][Transformer attention][FPGA-based particle physics]
• https://arxiv.org/abs/2307.06901[Better sensing][Variable-range interaction]
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• M. R. Carbone, M. Topsakal, D. Lu, and S. Yoo, Phys. Rev. Lett. 124, 156401(2020)[Machine-learning X-ray absorption spectra][Quantitative accuracy][arXiv:1912.13428]
• S. K. Giri, U. Saalmann and J. M. Rost, Phys. Rev. Lett. 124, 113201 (2020)[Purifying electron spectra][Noisy pulse][Machine-learning][Synthetic Hamilton matrices]
• M. K. Bisbo and B. Hammer, Phys. Rev. Lett. 124, 086102 (2020)[Efficient global structure optimization][Machine-learned surrogate model][arXiv:1907.05741]
• Dennis S. Kim, Michael Xu, and James M. LeBeau, Phys. Rev. Lett. 132, 086301 (2024)[Modeling temperature-dependent electron thermal diffuse scattering][Machine-learned interatomic potential][Path-integral molecular dynamics]
• Franz J. Schreiber, Jens Eisert, and Johannes Jakob Meyer, Phys. Rev. Lett. 131, 100803 (2023)[Classical surrogate][Quantum learning model]
• Ryosuke Jinnouchi, Jonathan Lahnsteiner, Ferenc Karsai, Georg Kresse, and Menno Bokdam, Phys. Rev. Lett. 122, 225701 (2019)[Phase transitions of hybrid perovskites][Machine-learning force fields trained on the fly][Bayesian inference][arXiv:1903.09613]
• Kaonan Micadei, John P.S. Peterson, Alexandre M. Souza, Roberto S. Sarthour, Ivan S. Oliveira, Gabriel T. Landi, Roberto M. Serra, and Eric Lutz, Phys. Rev. Lett. 127, 180603 (2021)[Experimental validation][Fully quantum fluctuation theorem][Dynamic Bayesian network]
• Mohammad Mehboudi, Mathias R. Jorgensen, Stella Seah, Jonatan B. Brask, Jan Kolodynski, and Marti Perarnau-Llobet, Phys. Rev. Lett. 128, 130502 (2022)[Fundamental limit][Bayesian thermometry][Attainability][Adaptive strategies]
• Tushar K. Saha, Joseph N.E. Lucero, Jannik Ehrich, David A. Sivak, and John Bechhoefer, Phys. Rev. Lett. 129, 130601 (2022)[Bayesian information engine][Optimally][Noisy measurement]
• P. Baglioni, R. Pacelli, R. Aiudi, F. Di Renzo, A. Vezzani, R. Burioni, and P. Rotondo, Phys. Rev. Lett. 133, 027301 (2024)[Predictive power][Bayesian effective action][Fully connected one hidden layer neural network][Proportional limit]
• M. S. Murillo, M. Marciante, and L. G. Stanton, Phys. Rev. Lett. 125, 085503 (2020)[Machine learning discovery][Computational model efficacy boundary]
• Yuji Umeda, Hiroyuki Hayashi, Hiroki Moriwake and Isao Tanaka, Jpn. J. Appl. Phys. 58, SLLC01 (2019)[Prediction of dielectric constant][Combination][First principles calculation][Machine learning]
• Vojtech Travnicek, Karol Bartkiewicz, Antonin Cernoch, and Karel Lemr, Phys. Rev. Lett. 123, 260501 (2019)[Experimental measurement][Hilbert-Schmidt distance][Two-qubit state][Reducing][Complexity][Machine learning]
• Wenqian Lian, Sheng-Tao Wang, Sirui Lu, Yuanyuan Huang, Fei Wang, Xinxing Yuan, Wengang Zhang, Xiaolong Ouyang, Xin Wang, Xianzhi Huang, Li He, Xiuying Chang, Dong-Ling Deng, and Luming Duan, Phys. Rev. Lett. 122, 210503 (2019)[arXiv:1905.03255][Machine learning topological phases][Solid-state quantum simulator]
• Tomoyuki Morishita1, and Synge Todo, J. Phys. Soc. Jpn. 91, 044001 (2022)[Randomized-gauge test][Machine learning][Ising model order parameter]
• Daisuke Wakabayashi, Kohei Shimamura, Akihide Koura, and Fuyuki Shimojo, J. Phys. Soc. Jpn. 92, 074002 (2023) [Large-scale molecular-dynamics simulation][SiO₂ melt][High pressure][Robust machine-learning interatomic potential]
• Hidetsugu Sakaguchi, J. Phys. Soc. Jpn. 93, 105001 (2024)[Machine learning][Nonlinear dynamical system][Control parameters][Feedforward neural network]
• Sota Nishiyama and Masayuki Ohzeki, J. Phys. Soc. Jpn. 94, 014802 (2025)[Solution space and storage capacity][Fully connected two-layer neural network][Generic activation function]
• Ayu Irie, Kohei Shimamura, Akihide Koura, and Fuyuki Shimojo, J. Phys. Soc. Jpn. 91, 045002 (2022)[Importance][Adjusting coefficient][Cost function][Construction][High-accuracy][Machine-learning interatomic potential]
• Fumitatsu Iwase, J. Phys. Soc. Jpn. 90, 094801 (2021)[Machine learning approach][Prediction][Nuclear quadrupole resonance frequency]
• Yu Hasegawa, Akihiro Haga, , Dousatsu Sakata, Yuki Kanazawa, Masahide Tominaga, Motoharu Sasaki, Toshikazu Imae, and Keiichi Nakagawa, J. Phys. Soc. Jpn. 90, 074801 (2021)[Estimation][X-ray energy spectrum][Cone-beam][Tomography scanner][Percentage depth dose Measurement][Machine learning approach]
• A. Sakamoto, K. Shiina and H. Mori, J. Phys. Soc. Jpn. 90, 065001 (2021)[Image preparation][Machine-learning analysis][Multiple-size spin system]
• Shogo Fukushima, Kohei Shimamura, Akihide Koura, and Fuyuki Shimojo, J. Phys. Soc. Jpn. 92, 054005 (2023)[Efficient training][Machine-learning interatomic potential][Artificial neural network][Estimating][Helmholtz free energy][Alkali metal]
• K. Fuchizaki, K. Nakamura and D. Hiroi, J. Phys. Soc. Jpn. 90, 055001 (2021)[Nonequilibrium relaxation scheme][Machine learning][Detecting][Phase transition]
• H. Araki, T. Yoshida and Y. Hatsugai, J. Phys. Soc. Jpn. 90, 053703 (2021)[Machine learning][Mirror skin effect][Disorder]
• N. Mamada, M. Mizumaki, I. Akai, T. Aonishi, J. Phys. Soc. Jpn. 90, 014705 (2021)[Obtaining underlying parameter][Magnetic domain pattern][Machine learning]
• Djenabou Bayo, Burak Civitcioglu, Joseph J. Webb, Andreas Honecker, and Rudolf A. Römer, J. Phys. Soc. Jpn. 94, 031002 (2025)[Machine learning][Phase][Structure][Model system][Physics].
• Y. Nomura, J. Phys. Soc. Jpn. 91, 054709 (2022)[Investigating network parameter][Neural-network quantum state]
• Y. Nomura, J. Phys. Soc. Jpn. 89, 054706 (2020)[Machine learning][Quantum state][Extension][Fermion-Boson coupled system][Excited-state calculation][arXiv:2001.02106]
• Kazuya Shinjo, et al., J. Phys. Soc. Jpn. 88, 065001 (2019)[Machine learning phase diagram][Half-filled one-dimensional extended Hubbard model]
• Askery Canabarro, Samurai Brito, and Rafael Chaves, Phys. Rev. Lett. 122, 200401 (2019)[Machine Learning][Nonlocal correlation]
• M. Tsubaki and T. Mizoguchi, Phys. Rev. Lett. 125, 206401 (2020)[Quantum deep field][Data-driven wave function][Electron density generation][Atomization energy prediction][Extrapolation][Machine learning][arXiv:2011.07923]
• J. Timmermann, et al., Phys. Rev. Lett. 125, 206101 (2020)[IrO₂ surface][Complexion][Machine learning][Surface investigation]
• Jose A. Garrido Torres, Paul C. Jennings, Martin H. Hansen, Jacob R. Boes, and Thomas Bligaard, Phys. Rev. Lett. 122, 156001 (2019)[Low-scaling algorithm][Nudged elastic band calculation][Surrogate machine learning model]
• Ryo Tamura, Jianbo Lin, and Tsuyoshi Miyazaki, J. Phys. Soc. Jpn. 88, 044601 (2019)[Machine learning force][Trained by Gaussian process][Liquid state][Transferability][Temperature][Pressure]
• Nobuo Nagashima, Masao Hayakawa, Hiroyuki Masuda, Kotobu Nagai, Materials Transactions, Vol.65, No.04, 428-433 (2024)[Estimating][S-N curve][Machine learning random forest method]
• Nobusuke Hasegawa, Shingo Hasegawa, Takafumi Kitaoka and Hiroyasu Ohtsu, Materials Transactions, Vol.60, No.05, 758 (2019)[Applicability][Neural network][Rock classification][Mountain tunnel]
• Takayuki Shiraiwa, Yuto Miyazawa and Manabu Enoki, Materials Transactions, Vol. 60, No.02, 189 (2019)[Prediction of fatigue strength][Steel][Linear regression][Neural network]
• Lucas Foppa, Thomas A.R. Purcell, Sergey V. Levchenko, Matthias Scheffler, and Luca M. Ghringhelli, Phys. Rev. Lett. 129, 055301 (2022)[Hierarchical symbolic regression][Identifying key physical parameter][Correlated][Bulk Properties of Perovskites]
• Kazuki Akiyama, Ilgoo Kang, Toshitake Kanno and Nozomu Uchida, Materials Transactions, Vol. 62, No.03, 461 (2021)[Prediction][Residual Mg content][Ladle][Product][Graphite spheroidizing treatment][Artificial neural network]
• Ivan Lobzenko, Tomohito Tsuru, Hideki Mori, Daisuke Matsunaka, Yoshinori Shiihara, Materials Transactions, Vol. 64, No.10, 2481 (2023)[Implementation][Atomic stress calculation][Artificial neural network potential]
• Parviz Kahhal, Hossein Ghorbani-Menghari, Hwi-Jun Kim, Hyunjoo Choi, Pil-Ryung Cha, Ji Hoon Kim, Materials Transactions, Vol. 64, No.11, 2648 (2023)[Metaheuristic optimization][Powder size distribution][Powder forming process][Multi-particle finite element method][Artificial neural network][Genetic algorithm]
• Qiang Du, Kjerstin Ellingsen, Mohammed M¡ÇHamdi, Astrid Marthinsen, Knut O. Tveito, Materials Transactions, Vol. 64, No.02, p. 360-365 (2023)[Integration][Neural network][High throughput][Multi-scale simulation][Establishing][Digital twin][Aluminium billet DC-casting]
• Xin Zhang, Liming Wu, Qi Liu, Mingjun Wen, Materials Transactions, Vol. 66, No.03, p. 345-352 (2025)[Prediction][Wrinkling][Thin-walled tube][Hydraulic push-pull bending process][Neural network]
• Tomoki Hirosawa, Frank Schäfer, Hideaki Maebashi, Hiroyasu Matsuura, and Masao Ogata, J. Phys. Soc. Jpn. 91, 114603 (2022)[Data-Driven reconstruction][Spectral conductivity][Chemical potential][Thermoelectric transport properties]
• Ayaka Sakata, J. Phys. Soc. Jpn. 89, 084001 (2020)[Bayesian inference][Infected patient][Group testing][Prevalence estimation]
• Ryota Moriguchi, Satoshi Tsutsui, Shun Katakami, Kenji Nagata, Masaichiro Mizumaki, and Masato Okada, J. Phys. Soc. Jpn. 91, 104002 (2022)[Bayesian inference][Hamiltonian selection][Mössbauer spectroscopy]
• Yui Hayashi, Shun Katakami, Shigeo Kuwamoto, Kenji Nagata, Masaichiro Mizumaki, and Masato Okada, J. Phys. Soc. Jpn. 92, 094002 (2023)[Bayesian inference][Small-angle scattering data]
• Hiroki Tanaka and Ken Umeno, J. Phys. Soc. Jpn. 92, 113001 (2023)[Bayesian inference method][Large magnitude event][Spatiotemporal marked point process][Representing seismic activity]
• Hiroki Tanaka and Ken Umeno, J. Phys. Soc. Jpn. 93, 024001 (2024)[Bayesian updating][Time interval][Different magnitude threshold][Marked point process][Application][Synthetic seismic activity]
• K. Iwamitsu, M. Okada and I. Akqai, J. Phys. Soc. Jpn. 89, 104004 (2020)[Spectral decomposition][Component][Weaker][Noise intensity][Bayesian spectroscopy]
• H. Sakamoto, S. Katakami, K. Muto, K. Nagata, T. Arima, and M. Okada, J. Phys. Soc. Jpn. 89, 124002 (2020)[Bayesian parameter estimation][Dispersion relation spectra]
• Kenji Nagata, Rei Muraoka, Yoh-ichi Mototake, Takehiko Sasaki, and Masato Okada, J. Phys. Soc. Jpn. 88, 044003 (2019)[Bayesian spectral deconvolution][Poisson distribution][Bayesian measurement][Virtual measurement Analytics]
• K. Okajima, K. Nagata, and M. Okada, J. Phys. Soc. Jpn. 90, 034001 (2021)[Fast Bayesian deconvolution][Simple reversible jump move]
• M. Ukita, J. Phys. Soc. Jpn. 91, 064002 (2022) [Model design][Bayesian spectral deconvolution]
• Hajime Ueda, Shun Katakami, Shogo Yoshida, Takehide Koyama, Yusuke Nakai, Takeshi Mito, Masaichiro Mizumaki, and Masato Okada, J. Phys. Soc. Jpn. 92, 054002 (2023)[Bayesian approach][T1 analysis][NMR spectroscopy][Application][Solid state physics]
• Y. Yokoyama, T. Uozumi, K. Nagata, M. Okada, and M. Mizumaki, J. Phys. Soc. Jpn. 90, 034703 (2021)[Bayesian integration][Hamiltonian parameter][X-ray photoemission][Absorption spectroscopy]
• Rikuto Fukuma and Kozo Okada, J. Phys. Soc. Jpn. 90, 094709 (2021)[Theory of core level photoemission][Uranium intermetallic compound][Combined with Bayesian data analysis approach]
• Keisuke Morita, Yoshihiko Nishikawa, and Masayuki Ohzeki, J. Phys. Soc. Jpn. 92, 123801 (2023)[Random postprocessing][Combinatorial Bayesian optimization]
• Taro Fukazawa, Tsutomu Ikegami, Masaaki Kawata, and Takashi Miyake, J. Phys. Soc. Jpn. 94, 013801 (2025)[Materials secure computation][Secret sharing][Bayesian optimization scheme and its performance]
• Yuki Sakishita, Fuyuki Nabeshima, Atsutaka Maeda, and Koji Hukushima, J. Phys. Soc. Jpn. 94, 014001 (2025)[Bayesian estimation][Thin-film X-ray Laue oscillation][Markov chain Monte Carlo method]
• Rei Nishimura, Shun Katakami, Kenji Nagata, Masaichiro Mizumaki, and Masato Okada, J. Phys. Soc. Jpn. 93, 034003 (2024)[Bayesian integration][Hamiltonian parameter][Crystal field]
• Yasuhiko Igarashi, et al., J. Phys. Soc. Jpn. 93, 074001 (2024)[Appropriate basis selection][Bayesian inference][Analyzing measured data reflecting photoelectron wave interference]
• Kota Ido, Yuichi Motoyama, Kazuyoshi Yoshimi, and Takahiro Misawa, J. Phys. Soc. Jpn. 92, 064702 (2023)[Data analysis][Ab initio effective Hamiltonian][Iron-based superconductor][Construction][Predictor][Superconducting critical temperature]
• Yuichi Yokoyama, Naruki Tsuji, Ichiro Akai, Kenji Nagata, Masato Okada, and Masaichiro Mizumaki, J. Phys. Soc. Jpn. 90, 094802 (2021)[Bayesian orbital decomposition][Determination of end condition][Magnetic Compton scattering]
• Kazunori Iwamitsu, et al., J. Phys. Soc. Jpn. 90, 104004 (2021)[Replica-exchange Monte Carlo method][Incorporating auto-tuning algorithm][Acceptance ratio][Effective Bayesian spectroscopy]
• T. Konno, J. Phys. Soc. Jpn. 89, 124006 (2020)[Deep-learning estimation][Band gap][Reading-periodic-table method][Periodic convolution layer]
• Dian Wu, Lei Wang, and Pan Zhang, Phys. Rev. Lett. 122, 080602 (2019)[Solving statistical mechanics][Variational autoregressive network]
• D. J. Chang, C. M. O¡ÇLeary, C. Su, D. A. Jacobs, S. Kahn, A. Zettl, J. Ciston, P. Ercius, and J. Miao, Phys. Rev. Lett. 130, 016101 (2023)[Deep-Learning][Electron diffractive imaging]
• Qianke Wang, Dawei Lyu, Jun Liu, and Jian Wang, Phys. Rev. Lett. 133, 140601 (2024)[Polarization][Orbital angular momentum][Encoded quantum Toffoli gate][Diffractive neural network]
• Pavlo Bilous, Adriana Palffy, and Florian Marquardt, Phys. Rev. Lett. 131, 133002 (2023)[Deep-learning approach][Atomic configuration interaction problem][Large basis sets]
• J. R. Moreno, G. Carleo, and A. Georges, Phys. Rev. Lett. 125, 076402 (2020)[Deep learning][Hohenberg-Kohn map][DFT]
• X. Liu, et al., Phys. Rev. Lett. 124, 113202 (2020)[Deep learning][Feynman's path integral][Strong-field time-dependent dynamics]
• Tomi Ohtsuki and Tomohiro Mano, Phys. Soc. Jpn. 89, 022001 (2020)[ Drawing phase diagram][Random quantum system][Deep learning][Wave function][arXiv:1909.09821]
• Or Sharir, Yoav Levine, Noam Wies, Giuseppe Carleo, and Amnon Shashua, Phys. Rev. Lett. 124, 020503 (2020)[Deep autoregressive model][Efficient variational simulation][Many-body quantum system][arXiv:1902.04057]
• Masahiko Demura, Materials Transactions, Vol.62 No.11 (2021) pp.1669-1672[Materials integration][Accelerating research][Development][Structural material]
• [http://www.jim.or.jp/journal/e/61/11/][Many papers of SIP-Materials Integration Projects]
• Junhyub Jeon, Namhyuk Seo, Jae-Gil Jung, Seung Bae Son, Seok-Jae Lee, Materials Transactions, Vol.64 No.09 (2023) pp.2196-2201[Analysis][Prediction mechanisms][Feature importance][Martensite start temperature][Alloy steel][Explainable artificial intelligence]
• Yuichi Okazaki, Yasuaki Tokudome, Shunsuke Yagi, Ikuya Yamada, Materials Transactions, Vol.64 No.09 (2023) pp.2082-2087[High-throughput screening][(La,Sr)(Fe,Co)O₃ perovskite][Oxygen evolution reaction catalysis]
• Megumi Higashi, Hidekazu Ikeno, Materials Transactions, Vol.64 No.09 (2023) pp.2179-2184[Extraction][Local structure information][X-ray absorption near-edge structure][Machine learning approach]
• Junhyub Jeon, Yoonje Sung, Namhyuk Seo, Jae-Gil Jung, Seung Bae Son, Seok-Jae Lee, Materials Transactions, Vol.64 No.09 (2023) pp.2214-2218[Machine learning model][Prediction mechanisms][Bainite start temperature][Low alloy steel]
• Qi Kong, Yasushi Shibuta, Materials Transactions, Vol.64 No.06 (2023) pp.1241-1249[High-precision prediction][Thermal conductivity][Metal][Molecular dynamics simulation][Combination][Machine learning approach]
• Zixiang Qiu, Kenjiro Sugio and Gen Sasaki, Materials Transactions, Vol.62 No.06 (2021) pp.719-725[Classification][Microstructure][Al-Si casting alloy][Different cooling rate][Machine learning technique]
• Yudai Iwamizu, Kota Suzuki, Naoki Matsui, Masaaki Hirayama, Ryoji Kanno, Materials Transactions, Vol.64 No.1 (2023) pp.287-295[Search][Lithium ion conducting oxide][Predicted ionic conductivity][Machine learning]
• Jungjoon Kim, Dongchan Min, Suwon Park, Junhyub Jeon, Seok-Jae Lee, Youngkyun Kim, Hwi-Jun Kim, Youngjin Kim, Hyunjoo Choi, Materials Transactions, Vol.63 No.10 (2022) pp.1304-1309[Optimization][Densification behavior][Soft magnetic powder][Discrete element method][Machine learning]
• Junhyub Jeon, Namhyuk Seo, Jae-Gil Jung, Seung Bae Son, Seok-Jae Lee, Materials Transactions, Vol.63 No.10 (2022) pp.1369-1374[Machine learning prediction][Cementite precipitation][Austenite][Low-alloy steel]
• Qi Kong, Yasushi Shibuta, Materials Transactions, Vol.65 No.7 (2024) pp.790-797[Advancing thermal conductivity prediction][Metallic material][Integrating molecular dynamics simulation][Machine learning]
• Kyohei Hayakawa, Isao Matsui, Yuichi Sekine, Takaharu Maeguchi, Materials Transactions, Vol.65 No.8 (2024) pp.977-986[Machine learning][Predict][Effect of stress][Iron loss][Frequency dependence][Non-oriented electrical steel]
• Tatsuya Maemura, Hidenori Terasaki, Kazumasa Tsutsui, Kyohei Uto, Shogo Hiramatsu, Kotaro Hayashi, Koji Moriguchi and Shigekazu Morito, Materials Transactions, Vol.61 No.08 (2020) pp.1584-1592[Interpretability][Deep learning classification][Low-carbon steel microstructure]
• Masato Shirai and Hiroshi Yamada, Materials Transactions, Vol.61 No.01 (2020) pp.176-180[Mechanical properties prediction][Gray cast iron][Trace Element][Deep Learning]
• Ye Li, Materials Transactions, Vol.64 No.11, 2547 (2023)[Deep learning][Classifying structure][Crystal system][Pure metal]
• Kohta Koenuma, Akinori Yamanaka, Ikumu Watanabe and Toshihiko Kuwabara, Materials Transactions, Vol.61 No.12 (2020) pp.2276-2283[Estimation][Texture-dependent stress-strain curve][r-Value][Aluminum alloy sheet][Deep learning]
• Guancen Liu, Zhiwei Zheng, Rusheng Zhao, Xuezheng Yue, Materials Transactions, Vol.65 No.3 (2024) pp. 308-317[Horseshoe lattice property-structure][Inverse design][Deep learning]
• Takayuki Shiraiwa, Koki Yasuda, Fabien Briffod, Mark Jhon, Fergyanto Gunawan, Rahul Sahay, Nagarajan Raghavan, Arief S. Budiman, Manabu Enoki, Materials Transactions, Vol.65 No.6 (2024) pp. 677-686[Materials informatics approach][Cu/Nb nanolaminate microstructure correlation][Yield strength][Electrical conductivity]
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• Ken-Ichi Aoki Tatsuhiro Fujita and Tamao Kobayashi, J. Phys. Soc. Jpn. 88, 054002 (2019) [Logical reasoning][Revealing][Critical temperature][Deep learning][Configuration ensemble][Statistical system]
• Zhanwei Liu, Shuo Yan, Haigang Liu, and Xianfeng Chen, Phys. Rev. Lett. 123, 183902 (2019)[Superhigh-resolution recognition][Optical vortex mode][Assisted][Deep-learning method]
• M. Bürkle, et al., Phys. Rev. Lett. 126, 177701 (2021)[Deep-learning][First-principles transport simulation]
• Maria Schuld and Nathan Killoran, Phys. Rev. Lett. 122, 040504 (2019)[Quantum machine learning][Feature Hilbert space]
• Tobias Haug and M.-S. Kim, Phys. Rev. Lett. 133, 050603 (2024)[Generalization][Quantum machine learning model][Quantum Fisher Information metric]
• Rodrigo A. Vargas-Hernandez, John Sous, Mona Berciu, and Roman V. Krems, Phys. Rev. Lett. 121, 255702 (2018)[Extrapolating][Quantum observables][Machine learning][Inferring multiple phase transition][Single phase][https://arxiv.org/abs/1803.08195]
• H.-Y. Huang, R. Kueng and J. Preskill, Phys. Rev. Lett. 126, 190505 (2021)[Information-theoretic bound][Quantum advantage][Machine learning]
• Paul Z. Hanakata, Ekin D. Cubuk, David K. Campbell, and Harold S. Park, Phys. Rev. Lett. 121, 255304 (2018)[Accelerated search and design][Stretchable graphene kirigami][Machine learning]
• Z. Liu and M. Tegmark, Phys. Rev. Lett. 126, 180604 (2021)[Machine learning][Conservation law][Trajectry]
• Ye-Fei Li and Zhi-Pan Liu, Phys. Rev. Lett. 128, 226102 (2022)[Smallest stable Si/SiO2 interface][Suppresses quantum tunneling][Machine-learning-based global search]
• Yanxing Zhang, Xinjian Ouyang, Dangqi Fang, Shaojie Hu, Laijun Liu, and Dawei Wang, Phys. Rev. Lett. 133, 266103 (2024)[Machine-learning modeling][Elemental ferroelectric bismuth monolayer]
• Keshav Srinivasan, Nolan Coble, Joy Hamlin, Thomas Antonsen, Edward Ott, and Michelle Girvan, Phys. Rev. Lett. 128, 164101 (2022)[Parallel machine learning][Forecasting][Dynamics of complex network]
• Takahiro Goto, Quoc Hoan Tran, and Kohei Nakajima, Phys. Rev. Lett. 127, 090506 (2021)[Universal approximation property][Quantum machine learning model][Quantum-enhanced feature space]
• King Chun Lai, Patricia Poths, Sebastian Matera, Christoph Scheurer, and Karsten Reuter, Phys. Rev. Lett. 134, 096201 (2025)[Automatic process exploration][Machine learning assisted transition state searches]
• Hao Tang, Boning Li, Guoqing Wang, Haowei Xu, Changhao Li, Ariel Barr, Paola Cappellaro, and Ju iL, Phys. Rev. Lett. 130, 150602 (2023)[Communication-efficient quantum algorithm][Distributed machine learning]
• Soham Chattopadhyay and Dallas R. Trinkle, Phys. Rev. Lett. 132, 186301 (2024)[Contributions to Diffusion][Complex material][Quantified][Machine learning]
• Andrew H. Proppe, Kin Long Kelvin Lee, Alexander E.K. Kaplan, Matthias Ginterseder, Chantalle J. Krajewska, and Moungi G. Bawendi, Phys. Rev. Lett. 131, 053603 (2023)[Time-resolved line shape][Single quantum emitter][Machine learned photon correlation]
• M.F. Kasim and S.M. Vinko, Phys. Rev. Lett. 127, 126403 (2021)[Learning][Exchange-correlation functional][Nature][Fully differentiable DFT]
• Isao Ohkubo, Zhufeng Hou, Jiyeon N. Lee, Takashi Aizawa, Mikk Lippmaa, Toyohiro Chikyow, Koji Tsuda, and Takao Mori, Materials Today Physics 16 (2020) 100296 [Realization][Closed-loop optimization][Epitaxial titanium nitride thin-film growth][Machine learning]
• Yuqing Jin, Takahiro Kozawa and Takao Tamura, Jpn. J. Appl. Phys. 60, 076509 (2021)[Analysis][Mitigating factor][Line edge roughness][Electron beam lithography][Machine learning]
• Sena Suzuki and Jun Kondoh, Jpn. J. Appl. Phys. 60, SDDC09 (2021)[Cantilever damage evaluation][Impedance-loaded SAW sensor][Continuous wavelet analysis][Machine learning]
• N. Tanaka, et al., Jpn. J. Appl. Phys. 60 066503 (2021)[Analysis][Dissolution kinetics][Narrow polydispersity][Alkaline aqueous solution][Machine learning]
• Hongpeng Wang, Shangce Gao, Michiya Mozumi, Masaaki Omura, Ryo Nagaoka and Hideyuki Hasegawa, Jpn. J. Appl. Phys. 60, SDDE21 (2021)[Preliminary investigation][Clutter filtering][Deep learning]
• K. Ando, et al., Jpn. J. Appl. Phys. 59 SKKE06 (2020)[Speckle reduction][Medical ultrasound image][Deep learning][Fully convolutional network]
• N. Takahashi, Y. Liu and C. Kaneta, Jpn. J. Appl. Phys. 59 051005 (2020)[Materials informatics approach][Design][Si/Ge layered][Nanostructure][Low thermal conductivity]
• Y. Kurokawa, T. Aoki, M. Kozuma, H. Kimura, T. Kanemura, Y. Ando and S. Yamazaki, Jpn. J. Appl. Phys. 59 SGGB03 (2020)[Hybrid structured analog multiplier][Vector-by-matrix multiplier[Neural network]
• Y.-H. Lin, et al., Jpn. J. Appl. Phys. 59 SGGB15 (2020)[Impact][Solution][Nonvolatile-memory-induced][Weight error][Computing-in-memory][Neural network system]
• X. Wu, et al., Jpn. J. Appl. Phys. 60 067001 (2021)[Rapid][Accurate][Identification][Colon cancer][Raman spectroscopy][Convolutional neural network]
• S. Ohno, et al., Jpn. J. Appl. Phys. 59 SGGE04 (2020)[Microring resonator][Crossbar array][Deep learning accelerator]
• D. Suzuki, T. Oka and T. Hanyu, J. Appl. Phys. 60 SBBB07 (2021)[Design][Energy-efficient binarized convolutional neural network accelerator][Nonvolatile field-programmable gate array][Only-once-write shifting]
• M. Yamakawa, et al., Jpn. J. Appl. Phys. 59 SKKE09 (2020)[Optimal cropping][Input image][Convolutional neural network][Ultrasonic diagnosis][Liver tumor]
• Kyungchan Son, Wooyoung Jeong, Wonseok Jeon and Hyunseok Yang, Jpn. J. Appl. Phys. 57 09SB02 (2018)[Autofocusing algorithm][Digital holographic imaging system][Convolutional neural network]
• Yutaro Katano, Tetsuhiko Muroi, Nobuhiro Kinoshita, Norihiko Ishii and Naoto Hayashi, Jpn. J. Appl. Phys. 57 09SC01 (2018)[Data demodulation][Convolutional neural network][Holographic data storage]
• Jacobus Dijkman, Marjolein Dijkstra, Rene van Roij, Max Welling, Jan-Willem van de Meent, and Bernd Ensing, Phys. Rev. Lett. 134, 056103 (2025)[Learning neural free-energy functionals][Pair-correlation matching]
• Ya-Dong Wu, Yan Zhu, Ge Bai, Yuexuan Wang, and Giulio Chiribella, Phys. Rev. Lett. 130, 210601 (2023)[Quantum similarity testing][Convolutional neural network]
• Yu-Jie Liu, Adam Smith, Michael Knap, and Frank Pollmann, Phys. Rev. Lett. 130, 220603 (2023)[Model-independent learning][Quantum phase][Matter][Quantum convolutional neural network]
• Volker L. Deringer, Chris J. Pickard, and Gábor Csányi, Phys. Rev. Lett. 120, 156001(2018)[Data-driven learning][Total and local energies][Elemental boron]
• P. Rotondo, M. Pastore and M. Gherardi, Phys. Rev. Lett. 125, 120601(2020)[Beyond the strage capacity][Data-driven][Satisfiability transition]
• R. A. Mansbach and A. L. Ferguson, J. Chem. Phys. 142, 105101(2015)[Machine learning][Free energy surface][Single molecule]
• F. A. Faber, A. Lindmaa, O. A. von Lilienfeld and R. Armiento, Phys. Rev. Lett., Vol. 117, No. 14, 135502(2016)[Machine learning energy][2 Million elpasolite (ABC₂D₆) crystals]
• Hiromi Oda, Shin Kiyohara, Koji Tsuda and Teruyasu Mizoguchi, "Transfer Learning to Accelerate Interface Structure Searches", J. Phys. Soc. Jpn. 86, 123601 (2017).
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• BiGmax(MAX PLANCK RESEARCH NETWORK)
• Berlin Big Data Center
• http://hauleweb.rutgers.edu/database_w2k/¡ÊDFT & DMFT Materials Database¡¢RutgersÂç³Ø¡Ë
• Machine learning for atomistic systems¡ÊMatthias RuppÀèÀ¸¤Ë¤è¤ë¡¢µ¡³£³Ø½¬¤Î¥Ú¡¼¥¸¡Ë
• OQMD(Open Quantum Materials Database)
• Computational Electronic Structure Database(CompES-X, NIMS)
• NIST(*)(National Institute of Standards and Technology)
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• Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach(Computational Chemistry Highlights): ´ØϢʸ¸¥¡§http://arxiv.org/abs/1503.04987
• Automatic chemical design using a data-driven continuous representation of molecules(Computational Chemistry Highlights)
• A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians(Computational Chemistry Highlights): ´ØϢʸ¸¥¡§https://arxiv.org/abs/1808.04526
• DeepSMILES: An adaptation of SMILES for use in machine-learning of chemical structures(Computational Chemistry Highlights)
• Artificial Intelligence Assists Discovery of Reaction Coordinates and Mechanisms from Molecular Dynamics Simulations(Computational Chemistry Highlights)
• A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules(Computational Chemistry Highlights)
• Physical machine learning outperforms "human learning" in Quantum Chemistry(Computational Chemistry Highlights)
• Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning(Computational Chemistry Highlights)
• Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions(Computational Chemistry Highlights)
• Schrödinger-ANI: An Eight-Element Neural Network InteractionPotential with Greatly Expanded Coverage of Druglike Chemical Space(Computational Chemistry Highlights)
• Open Graph Benchmark: Datasets for Machine Learning on Graphs(Computational Chemistry Highlights)
• Learning Molecular Representations for Medicinal Chemistry(Computational Chemistry Highlights)
• What Does the Machine Learn? Knowledge Representations of Chemical Reactivity(Computational Chemistry Highlights)
• OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features(Computational Chemistry Highlights)
• Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors(Computational Chemistry Highlights)
• Identifying domains of applicability of machine learning models for materials science(Computational Chemistry Highlights)
• Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations(Computational Chemistry Highlights)
• Accelerating High-Throughput Virtual Screening Through Molecular Pool-Based Active Learning(Computational Chemistry Highlights)
• Uncertainty Quantification Using Neural Networks for Molecular Property Prediction(Computational Chemistry Highlights)
• Leveraging Uncertainty in Machine LearningAccelerates Biological Discovery and Design(Computational Chemistry Highlights)
• Using attribution to decode binding mechanism in neural network models for chemistry(Computational Chemistry Highlights)
• Bayesian optimization of nanoporous materials(Computational Chemistry Highlights)
• Evidential Deep Learning for Guided Molecular Property Prediction and Discovery(Computational Chemistry Highlights)
• Explaining and avoiding failures modes in goal-directed generation(Computational Chemistry Highlights)
• Pushing the frontiers of density functionals by solving the fractional electron problem(Computational Chemistry Highlights)
• Machine learning potentials always extrapolate, it does not matter.(Computational Chemistry Highlights)
• Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki¡ÝMiyaura Coupling(Computational Chemistry Highlights)
• Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search(Computational Chemistry Highlights)
• Deep Learning Metal Complex Properties with Natural Quantum Graphs(Computational Chemistry Highlights)
• Semiempirical Hamiltonians learned from data can have accuracy comparable to Density Functional Theory(Computational Chemistry Highlights)
• Quantum Chemical Data Generation as Fill-In for Reliability Enhancement of Machine-Learning Reaction and Retrosynthesis Planning(Computational Chemistry Highlights)
• Machine-Learning-Guided Discovery of Electrochemical Reactions(Computational Chemistry Highlights)
• Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability(Computational Chemistry Highlights)
• Real-World Molecular Out-Of-Distribution: Specification and Investigation(Computational Chemistry Highlights)
• Accelerated dinuclear palladium catalyst identification through unsupervised machine learning(Computational Chemistry Highlights)
• Ranking Pareto optimal solutions based on projection free energy(Computational Chemistry Highlights)
• Few-Shot Learning for Low-Data Drug Discovery(Computational Chemistry Highlights)

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