マテリアルズ・インフォマティクス関連情報 [総合索 引][Top]

マテリアルズ・インフォマティクス[先頭]

• 小目次（マテリアルズ・インフォマティクス）
• [NIMS関連プレスリリース情報（含む"NIMS NOW"）]
• [公募等”募集”情報]
• [採用情報]
• [セミナー情報]（含む交流会、講習会、コロキウム、講義）
• [フォーラム情報]（含むスクール）
• [研究会情報]（含むシンポジウム、講演会）
• [Workshop情報]（含む説明会、ワークショップ、ミーティング、キャンプ、学会、短期研究会）
• [ハードウェア関連情報]
• [組織・機関等情報]
• [文献情報]
• （参考文献、参考サイト、参考情報等）
• [その他いろいろ]（含むプレスリリース）

• 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]
• （インフォマティクスとそれに関連しそうな情報）
• 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/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/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/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/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/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/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/2306.11296[ChatGPT chemistry assistant][Text mining][Prediction][MOF synthesis]
• 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/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/2410.21317[MatExpert][Decomposing materials discovery][Mimicking human expert]
• https://arxiv.org/abs/1806.05772[Network analysis][Synthesizable materials discovery]
• https://arxiv.org/abs/2408.04661[MaterioMiner][Ontology-based text mining dataset][Extraction][Process-structure-property entities]
• https://arxiv.org/abs/2409.14572[Evaluating][Performance][Robustness][LLM][Materials science Q&A][Property prediction]
• 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/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/2407.16867[From text to insight][Large language model][Materials science data extraction]
• 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/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関連
• 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/2203.08205[Learning deep implicit Fourier neural operators][IFNOs][Application][Heterogeneous material modeling]
• https://arxiv.org/abs/2408.15852[chemtrain][Learning deep potential model][Automatic differentiation][Statistical physics]
• 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/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/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/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/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/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−J₂ 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/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[AFLOWπ][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/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/1805.09199[High-throughput study][Static dielectric constant][High temperatures][Oxide and fluoride cubic perovskites]
• 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/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/1909.09605[Spin-orbitronic material][Record spin-charge conversion][High-throughput ab initio calculation]
• https://arxiv.org/abs/2011.10905[Discovery][Extreme work function][High-throughput density functional theory][Machine learning]
• https://arxiv.org/abs/1909.10395[Accelerated discovery][Two-dimensional optoelectronic octahedral oxyhalide][High-throughput Ab Initio Calculation][Machine learning]
• 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/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/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/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/2008.06838[High-throughput computational characterization][Two-dimensional][Compositionally complex][Transition-metal chalcogenide alloy]
• 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/2009.08048[High-throughput computational-experimental screening protocol][Discovery][Bimetallic catalyst]
• 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/2012.04790[High-throughput computational search][Two-dimensional binary alloy][Energetic stability][Synthesizability][Three-dimensional counterpart]
• 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/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/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/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/2307.10728[Sampling][Whole materials space][Conventional superconducting material]
• https://arxiv.org/abs/2106.03312[High-throughput study][Antisolvent][Stability][Multicomponent metal halide perovskite][Robotics-based synthesis][Machine learning]
• 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/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/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/2205.14286[High-throughput computation][Structure prototype analysis][Two-dimensional ferromagnetic material]
• https://arxiv.org/abs/2205.14907[High-throughput study][Anomalous Hall effect]
• 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/2207.00364[High-throughput analysis][Fröhlich-type polaron model]
• https://arxiv.org/abs/2207.05353[Absorption][Adsorption][High-throughput computation][Impurities][2D material]
• 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/2209.15423[High-throughput computation][Raman spectra][First principles]
• 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/2211.14688[High-throughput ab initio reaction mechanism exploration][Cloud][Automated multi-reference validation]
• https://arxiv.org/abs/2211.16685[High-throughput][Ab Initio design][Atomic interface][InterMatch]
• https://arxiv.org/abs/2212.07845[Exploration][All-3d Heusler alloy][Permanent magnet][Ab initio][High-throughput study]
• 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/2302.04896[High-throughput computational dataset][Halide perovskite alloy]
• 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/2304.14367[TribChem][Software][First-principles][High-throughput study][Solid interface][Tribological properties]
• 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/2306.11116[Na in Diamond][High spin defect][Revealed][ADAQ high-throughput computational database]
• 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]
• 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/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−x (0 x 1) system]
• https://arxiv.org/abs/2310.16591[Intrinsic piezoelectric anisotropy][Tetragonal ABO₃ perovskite][High-throughput study]
• https://arxiv.org/abs/2311.03163[SurfFlow][High-throughput surface energy calculation][Arbitrary crystal]
• 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/2311.15430[Application][Batch learning][Boosting high-throughput ab initio success rate][Reducing computational effort][Data-driven processes]
• 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/2402.04726[Phase stability][Mechanical property trend][MAB phase][High-throughput ab initio calculation]
• 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/2409.04418[Charting new regions][Cobalt's chemical space][Maximally large magnetic anisotropy][Computational high-throughput study]
• https://arxiv.org/abs/2410.13982[Design of amine-functionalized materials][Direct air capture][Integrated high-throughput calculation][Machine learning]
• https://arxiv.org/abs/2411.10790[Effect of Hubbard U correction][Electronic and magnetic properties][2D material][High-throughput study]
• https://arxiv.org/abs/2412.06449[High-throughput computational screening][Small][Eco-friendly][Molecular crystal][Sustainable piezoelectric material]
• 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/2205.05053[High throughput generative vector autoregression model][Stochastic synapses]
• 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−xCa_xTi_1−yZr_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/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/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/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/1910.10581[Using Bayesian model selection][Advise neutron reflectometry analysis][Langmuir-Blodgett monolayer]
• 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/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/2206.11343[Bayesian model calibration][Block copolymer self-assembly][Likelihood-free inference][Expected information][Gain computation][Measure transport]
• 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−x (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/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/2304.03573[Bayesian][Seebeck coefficient][Liquid water][Equilibrium molecular dynamics]
• https://arxiv.org/abs/2305.04841[AutoEIS][Automated Bayesian model selection][Analysis][Electrochemical impedance spectroscopy]
• 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/2306.05398[Bayesian model calibration][Diblock copolymer][Film self-assembly][Power spectrum][Microscopy data]
• 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/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/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/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/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/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]
• 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/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 β 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/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/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/2410.13820[Enhancing universal machine learning potential][Polarizable long-range interaction]
• 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/2412.10516[CHIPS-FF][Evaluating universal machine learning force field][Material properties]
• 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/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/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/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/1910.04603[Machine learning driven synthesis][Few-layered WTe₂]
• 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−x,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/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/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/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/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/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/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/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/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)]
• 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/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/2403.05729[Systematic assessment][Various universal 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/2501.05211[Application][Pretrained universal machine-learning interatomic potential][Physicochemical simulation][Liquid electrolyte][Li-ion battery]
• 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/2502.03126[Robust machine learned interatomic potential][Nb: collision cascade simulation][Accurate defect configurations]
• 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/2502.03660[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/2412.18198[Driving force of atomic ordering][Fe_1−xPt_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/2501.07485[Machine learning-enhanced characterisation][Structured spectral densities][Leveraging][Reaction coordinate mapping]
• 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/2407.01068[Adaptive energy reference][Machine-learning model][Electronic density of states]
• 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/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][α−RuCl₃][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/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/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/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/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 iInteratomic 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/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/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/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/2212.01432[Machine learned interatomic potential][Dispersion strengthened plasma facing component]
• 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/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/2304.08871[Machine-learning detection][Berezinskii-Kosterlitz-Thouless transition][Second-order phase transition][XXZ model]
• 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/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/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/2304.07592[Leveraging composition-based material descriptor][Machine learning optimization]
• 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/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/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/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/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/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/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/2402.06479[Thermal transport][Glasses][Machine learning driven simulation]
• 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/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 moiré 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/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/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/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/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/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][σ−phase 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/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/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/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/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/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/2501.18523[Deep learning][Reflection high-energy electron diffraction image][Predict cation ratio][Sr_xTi_1−xO₃ thin film]
• 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/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/2409.06740[Data-efficient][Interpretable inverse materials design][Disentangled variational autoencoder]
• 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/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/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−1][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/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/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/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/2412.03090[Deep neural network approach][Solve the Dirac equation]
• 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/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/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/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/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/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/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/2303.12164[Viscoelastic constitutive artificial neural networks (vCANNs)][Framework][Data-driven anisotropic nonlinear finite viscoelasticity]
• 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/2311.06380[Theory][Implementation][Inelastic constitutive artificial neural network]
• 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]
• 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/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/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/2103.10846[Universal neural network][Learning phases and criticalities]
• https://arxiv.org/abs/2312.01290[Evolutionary search][Superconducting phase][Lanthanum-nitrogen-hydrogen system][Universal neural network potential]
• https://arxiv.org/abs/2408.04497[SchrödingerNet][Universal neural network solver][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−x)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/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/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/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/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/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/2409.14042[Recurrent neural network][Prediction][Electronic excitation dynamics]
• 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/2008.12331[Neural network solver][Small quantum cluster]
• 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/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/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/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/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/2502.02682[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/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/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/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/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/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/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/2401.10190[Kaczmarz-inspired approach][Accelerate][Optimization][Neural network wavefunction]
• 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/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/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/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/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/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][γ′(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/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/1806.09284[Two pressure-induced superconducting transitions][Explored][Data-driven materials search][New approach][Develop novel functional materials][Thermoelectric][Superconducting]
• 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/1902.09395[Data-driven material Model][Atomistic simulation]
• https://arxiv.org/abs/1902.09770[Data-driven exploration][Pressure-induced superconductivity][AgIn₅Se₈]
• https://arxiv.org/abs/1907.05644[Data-driven materials science][Status][Challenge][Perspective]
• 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/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/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/1904.05859[Big-data-driven materials science][FAIR data infrastructure]
• 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/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‐induced order‐disorder 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/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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• Carlo Baldassi, Enrico M. Malatesta, and Riccardo Zecchina, Phys. Rev. Lett. 123, 170602 (2019)[Geometry of Solutions][Capacity][Multilayer neural network][Rectified linear unit activation]
• Emi Minamitani, Masayoshi Ogura and Satoshi Watanabe, Applied Physics Express 12[9], 095001 (2019)[Simulating lattice thermal conductivity][Semiconducting material][High-dimensional neural network potential][arXiv:1905.08508]
• Huiling Huang, Yanfeng Liu and Jun Han, Appl. Phys. Express 15, 062002 (2022)[Detecting][Orbital angular momentum][Speckle][Convolutional neural network]
• Takumi Kotooka, Yuichiro Tanaka, Hakaru Tamukoh, Yuki Usami and Hirofumi Tanaka, Appl. Phys. Express 16, 014002 (2023)[Random network device][Fabricated][Ag₂Se nanowire][Data augmentation][Binarized convolutional neural network]
• Jianfeng Li, Hongdong Zhang, and Jeff Z. Y. Chen, Phys. Rev. Lett. 123, 108002 (2019)[Structural prediction][Inverse design][Strongly correlated neural network]
• Di Luo and Bryan K. Clark, Phys. Rev. Lett. 122, 226401 (2019)[Backflow transformation][Neural network][Quantum Many-Body Wave Function][arXiv:1807.10770]
• Alexandra Nagy and Vincenzo Savona, Phys. Rev. Lett. 122, 250501 (2019)[Variational quantum Monte Carlo method][Neural-network ansatz][Open quantum system]
• Michael J. Hartmann and Giuseppe Carleo, Phys. Rev. Lett. 122, 250502 (2019)[Neural-network approach][Dissipative quantum many-body dynamics]
• Filippo Vicentini, Alberto Biella, Nicolas Regnault, and Cristiano Ciuti, Phys. Rev. Lett. 122, 250503 (2019)[Variational neural-network ansatz][Steady state][Open quantum system]
• Corneel Casert, Tom Vieijra, Stephen Whitelam, and Isaac Tamblyn, Phys. Rev. Lett. 127, 120602 (2021)[Dynamical large deviation][Two-dimensional kinetically constrained model][Neural-network state ansatz]
• A. Krishnamoorthy, et al., Phys. Rev. Lett. 126, 216403 (2021)[Dielectric constant][Liquid water][Neural network quantum MD]
• Thomas Wagner, Hermann Kampermann, Dagmar Bru, and Martin Kliesch, Phys. Rev. Lett. 130, 200601 (2023)[Learning logical Pauli noise][Quantum error correction]
• Hsin-Yuan Huang, Yu Tong, Di Fang, and Yuan Su, Phys. Rev. Lett. 130, 200403 (2023)[Learning many-body Hamiltonian][Heisenberg-limited scaling]
• M. Link, K. Gao, A. Kell, M. Breyer, D. Eberz, B. Rauf, and M. Köhl, Phys. Rev. Lett. 130, 203401 (2023)[Machine learning][Phase diagram][Strongly interacting Fermi gas]
• J. Daru, H. Forbert, J. Behler, and D. Marx, Phys. Rev. Lett. 129, 226001 (2022)[Coupled cluster molecular dynamics][Condensed phase system][Machine learning potential][Liquid water benchmark]
• L. Hu, B. Huang, and F. Liu, Phys. Rev. Lett. 126, 176101 (2021)[Atomistic mechanism][Underlying][Si(111)-(7x7)][Surface reconstruction][Artificial neural-network potential]
• Heiko Burau and Markus Heyl, Phys. Rev. Lett. 127, 050601 (2021)[Unitary long-time evolution][Quantum renormalization group][Artificial neural network]
• Yaolong Zhang, Junfan Xia, and Bin Jiang, Phys. Rev. Lett. 127, 156002 (2021)[Physically motivated recursively embedded atom neural network][Incorporating local completeness][Nonlocality]
• Yichen Huang and Joel E. Moore, Phys. Rev. Lett. 127, 170601 (2021)[Neural network representation][Tensor network][Chiral state]
• Di Luo, Giuseppe Carleo, Bryan K. Clark, and James Stokes, Phys. Rev. Lett. 127, 276402 (2021)[Gauge equivariant neural network][Quantum lattice gauge theories]
• Di Luo, Zhuo Chen, Juan Carrasquilla, and Bryan K. Clark, Phys. Rev. Lett. 128, 090501 (2022)[Autoregressive neural network][Simulating open quantum system][Probabilistic formulation]
• Kunal Sharma, M. Cerezo, Lukasz Cincio, and Patrick J. Coles, Phys. Rev. Lett. 128, 180505 (2022)[Trainability][Dissipative perceptron-based][Quantum neural network]
• Kieran A. Murphy and Dani S. Bassett, Phys. Rev. Lett. 132, 197201 (2024)[Machine-learning optimized measurement][Chaotic dynamical system][Information bottleneck]
• 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]
• 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’Hamdi, 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]
• 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’Leary, 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]
• Yoav Levine, Or Sharir, Nadav Cohen, and Amnon Shashua, Phys. Rev. Lett. 122, 065301 (2019)[Quantum entanglement][Deep learning architecture]
• 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.窶%@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]
• 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]
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  ---

• （参考サイト）
• BiGmax(MAX PLANCK RESEARCH NETWORK)
• Berlin Big Data Center
• http://hauleweb.rutgers.edu/database_w2k/（DFT & DMFT Materials Database、Rutgers大学）
• Machine learning for atomistic systems（Matthias Rupp先生による、機械学習のページ）
• OQMD(Open Quantum Materials Database)
• Computational Electronic Structure Database(CompES-X, NIMS)
• NIST(*)(National Institute of Standards and Technology)
• 数理材料科学コミュニティ
• ↑協働例：物質の階層構造をとりいれたマテリアルズ・インフォマティクス（png画像、東北大学WPI-AIMR）
• 科学技術情報プラットフォーム(*)（JST[科学技術振興機構]のページ、ビッグデータ関連）
  ↑”わが国におけるデータシェアリングのあり方に関する”提言あり。

  ---

• [その他いろいろ][小目次]
• Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach(Computational Chemistry Highlights): 関連文献：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): 関連文献：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−Miyaura 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)

  ---

• 日立のプレスリリース記事：「KEK向けに、磁性材料の磁気構造に関するシミュレーションデータや量子ビーム実験データを解析・可視化するシステムの開発を支援 - マテリアルズ・インフォマティクスに基づく材料開発の効率化に貢献 - 」
• （情報）”NEC、ビッグデータの高精度な予測分析に必要な期間を従来比1/3に短縮する「特徴量自動設計技術」を開発”（NECのプレスリリース記事(*)）
• （マイナビニュース記事１）「機械学習によって金属ガラス材料の探索を高速化 - 米SLACなどが発表 - 」←詳細は、当該マイナビニュース記事参照。
• （プレスリリース情報１）「希少元素を使わずに赤く光る新窒化物半導体を発見」←東工大のプレスリリース記事
• （プレスリリース情報２）「「京」でペロブスカイト太陽電池の新材料候補を発見―膨大な数から適切な材料を効率よく探し出す―」←理研のプレスリリース記事
• （プレスリリース情報３）「新材料開発の期間・コストの削減を支援する「材料開発ソリューション」を提供開始 AIを活用したマテリアルズ・インフォマティクスに基づくデータ分析支援サービスなどを提供」←日立のプレスリリース記事
• （プレスリリース情報４）「人工知能が「繰り返し成長すること」で計算コストを１／３６００に削減 〜 界面構造を高速に決定し、高性能な物質開発を加速 〜 」←東大生産研のプレスリリース記事
• （プレスリリース情報５）「Artificial intelligence aids materials fabrication」←MITのプレスリリース（ニュース）記事[MIT News]
• （プレスリリース情報６）「人工知能（AI）で触媒反応の収率を予測 - キャタリストインフォマティクスで触媒の発見に道 -」←産総研のプレスリリース記事
• （プレスリリース情報７）「材料設計におけるＡＩの有用性を実証：高イオン伝導率を有する全固体リチウムイオン電池用固体電解質の開発を効率化」←富士通のプレスリリース記事
• （プレスリリース情報８）「高分子太陽電池、人工知能で性能予測 〜１，２００個の実験データから有効性を実証〜 」←科学技術振興機構(JST)のプレスリリース記事
• （プレスリリース情報９）「２種類の深層学習手法の組み合わせで薬剤とタンパク質の相互作用を予測 - 高速で高精度な予測と相互作用部位の特定・可視化による検証を実現 - 」←産総研のプレスリリース記事
• （プレスリリース情報１０）「人工知能でタンパク質を自動設計 - 様々な機能性タンパク質開発の加速に期待 - 」←産総研のプレスリリース記事
• （プレスリリース情報１１）「人工知能が専門家の約２万倍の速さでスペクトルを解釈 〜知識や職人技なしで、物質の性質を明らかに〜」←東大生産研のプレスリリース記事
• （プレスリリース情報１２）「分子構造を設定するだけで物性値を高速・高精度で予測 - 時間のかかる理論計算を1万倍以上高速化し材料開発のプロセスを加速 - 」←産総研のプレスリリース記事
• （プレスリリース情報１３）「機械学習の関数を用いて量子状態を表現する方法を提唱 - 機械学習の手法を用いて量子多体系の性質に迫る -」←東大工学部のプレスリリース記事
• （プレスリリース情報１４）「組合せ最適化問題を効率的に解くための新しいアナログニューラルネットワーク」←東大生産研のプレスリリース記事
• （プレスリリース情報１５）「理論計算や専門知識いらず！人工知能がスペクトルから物質の機能と構造を定量 〜物質開発の加速に期待〜 」←東大生産研のプレスリリース記事
• （プレスリリース情報１６）「機械学習で新しい超伝導物質を探索する - 21世紀版マティアス則の構築 - 」←九州工業大学のプレスリリース記事
• （プレスリリース情報１６）「バイオエタノールからブタジエンを生成する世界最高の生産性を有する触媒システムを短期間で開発」←産総研のプレスリリース記事
• （プレスリリース情報１７）「化学構造を手掛かりにしたデータサイエンスの手法で、天然物化合物の生合成経路の予測に成功！ 有機分子の特性や薬剤設計の研究への活用期待」←奈良先端科学技術大学院大学のプレスリリース記事
• （プレスリリース情報１８）「ディープラーニングモデルの新たな軽量化技術を開発 - 高度なAIの小規模・省電力運用に期待、データ利活用社会の実現に貢献 - 」←NEDOのプレスリリース記事
• （プレスリリース情報１９）「NECと東北大学、「開発者が解釈可能なマテリアルズ ・インフォマティクス」で特性向上の主要因を抽出する手法を開発」←東北大学のプレスリリース記事
• （プレスリリース情報２０）「新物質の合成条件を効率よく推薦する手法を開発」←京都大学のプレスリリース記事
• （プレスリリース情報２１）「機械学習を活用したゼオライト合成の理解〜ほしい材料を思いのままに。究極の材料合成を目指して〜」←東京大学のプレスリリース記事
• （プレスリリース情報２２）「AIを活用し、フレキシブル透明フィルム開発の実験回数を1/25以下に大幅低減 - 相反する複数の要求特性がある機能性材料開発への応用展開に期待 -」←NEDOのプレスリリース記事
• （プレスリリース情報２３）「電子状態が変化する前の姿から、変化後の姿をAIが正確に予想 〜電子の励起状態を高速で計算、構造解析のアクセルに〜 」←東大生産研のプレスリリース記事
• （プレスリリース情報２４）「AIに電子の物理を学習させる方法を開発」←東大物性研のプレスリリース記事
• （プレスリリース情報２５）「最適化したナノ構造により結晶性材料の熱伝導率を最小に 〜ＭＩを駆使して熱機能材料の開発へ応用期待〜 」←科学技術振興機構(JST)のプレスリリース記事
• （プレスリリース情報２６）「IGZOと不揮発性メモリを三次元集積した新デバイスの開発に成功 〜ディープラーニングの高効率ハードウェア化へ期待〜 」←科学技術振興機構(JST)のプレスリリース記事
• （プレスリリース情報２７）「大阪大学、兵庫県立大学、兵庫県による「関西におけるマテリアルズ・インフォマティクス推進にかかる連携・協力に関する覚書」の締結」←大阪大学産業科学研究所のプレスリリース記事
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• （プレスリリース情報２９）「単一のAIに多彩な材料データを学習させる手法を開発」←早稲田大学のプレスリリース記事
• （プレスリリース情報３０）「AIを使い生体材料（バイオマテリアル）の設計に成功 - 機械学習で生体分子の吸着を予測し、材料を高速スクリーニング - 」←東工大のプレスリリース記事
• （プレスリリース情報３１）「量子化学理論とAI解析を組み合わせた電子状態インフォマティクス技術開発により温室効果ガスを効率的に吸収するイオン液体の混合方法を発見」←中央大のプレスリリース記事
• （プレスリリース情報３２）「専門知識と機械学習を融合した最適化手法 ―最適な成膜条件により生成効率を約２倍に―」←理研のプレスリリース記事
• （プレスリリース情報３３）「次世代有機ELの高効率化・長寿命化に成功 〜マテリアルズ・インフォマティクスで開発期間を短縮〜 」←科学技術振興機構(JST)のプレスリリース記事
• （プレスリリース情報３４）「量子物理学の理論や波動関数に基づく新たな深層学習技術を開発 - 学習データにはない、分子構造が大きく異なる未知化合物に対する物性の外挿予測が可能 - （発表主体：産総研）」←東大生産研のプレスリリース記事
• （プレスリリース情報３５）「自律的に物質探索を進めるロボットシステムを開発 - 物質・材料研究開発の進め方について革新を起こす - 」←東京工業大学のプレスリリース記事
• （プレスリリース情報３６）「Ｘ線回折パターンからの対称性予測における知識発見 〜熟練者の勘・コツの定式化に成功〜 」←科学技術振興機構(JST)のプレスリリース記事
• （プレスリリース情報３７）「ハイスループット実験と触媒インフォマティクスが実現するゼロからの触媒設計」←北陸先端科学技術大学院大学のプレスリリース記事
• （プレスリリース情報３８）「人工知能を利用して磁石の磁気パラメータの推定に成功 - スピントロニクスの研究を人工知能で効率化 - 」←東大 理学系研究科・理学部のプレスリリース記事
• （プレスリリース情報３９）「優れた高分子太陽電池材料を見いだす人工知能 - 仮想の２０万種類から選び合成した新材料で有効性を実証 - 」←大阪大学のプレスリリース記事
• （プレスリリース情報４０）「計算シミュレーションとAIを連携させ、仮想実験環境を構築 - 材料ビッグデータの創出と、それを用いるAI材料設計へ - 」←産総研のプレスリリース記事
• （プレスリリース情報４１）「失敗は成功の母!創薬AIの精度向上 - 失敗例を1,000倍学ばせると誤分類が100分の1以下に - 」←関西医科大学のプレスリリース記事（PDF形式ページ）
• （プレスリリース情報４２）「結晶の世界をのぞくニューラルネットワーク ―固体系のミクロな量子多体物性に迫る―」←理研のプレスリリース記事
• （プレスリリース情報４３）「Snを添加したIGZO材料を用いた三次元集積メモリデバイスを開発 〜機械学習ハードウェアの高エネルギー効率化へ期待〜 」←東大生産研のプレスリリース記事
• （プレスリリース情報４４）「触媒活性理論の実証に前進 ―実験、数理、機械学習の融合による非平衡触媒活性の解析―」←理研のプレスリリース記事
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• （プレスリリース情報４６）「機械学習で準結晶を形成する化学組成を同定 準結晶の安定化メカニズムの解明に向けた第一歩」←東京理科大学のプレスリリース記事
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• （プレスリリース情報４８）「限定性・偏向性のあるデータから新材料を推薦するシステムを開発 - 証拠理論を用いたシステムを開発し、新規合金薄膜材料合成で実証 - 」←北陸先端科学技術大学院大学のプレスリリース記事
• （プレスリリース情報４９）「ベイズ推定を用いた新たな電子構造の解析法を開発 - トポロジカル絶縁体などを巡る数々の論争の決着へ - 」←産総研のプレスリリース記事
• （プレスリリース情報５０）「触媒遺伝子「触媒シークエンシング」を発見 〜触媒インフォマティクスを駆使した新しい触媒開発に成功〜」←科学技術振興機構(JST)のプレスリリース記事
• （プレスリリース情報５１）「AIモデルの開発により、たった1回の実験で新規プロトン伝導性電解質を発見！ 〜中温動作燃料電池に用いる電解質材料の開発加速化に期待〜」←九州大学のプレスリリース記事
• （プレスリリース情報５２）「熱と量子の揺らぎを発現する深層学習モデルを発見 - 「富岳」などにより自然界の根源的なシミュレーションが加速 -」←理研のプレスリリース記事
• （プレスリリース情報５３）「機械学習手法により物理の難問「量子スピン液体」を解明 - スーパーコンピュータ「富岳」も用いた最先端の計算により実現 -」←理研のプレスリリース記事
• （プレスリリース情報５４）「AIが生成した材料の構造画像を用い、物性を予測する技術を開発 - 材料の選定から加工・評価までを高速・高精度に再現、材料開発を加速 -」←NEDOのプレスリリース記事
• （プレスリリース情報５５）「触媒ビッグデータから「触媒世界地図」を描写 〜ブラックボックス化していた触媒設計をひもとく〜」←科学技術振興機構(JST)のプレスリリース記事
• （プレスリリース情報５６）「機械学習によるヨウ素の結合エネルギーの予測 従来法の1.3億倍のスピードでの予測に成功」←千葉大学のプレスリリース記事
• （プレスリリース情報５７）「スペクトルから思いもかけない物性をAIが予測」←東大生産研のプレスリリース記事
• （プレスリリース情報５８）「人工知能により材料の構造画像を生成し、物性を予測する技術を開発 - AI技術で扱える材料を広げ、材料開発加速へ - 」←産総研のプレスリリース記事
• （ニュース情報５９）「データ駆動型材料設計技術利用推進コンソーシアムの設立に向けて - 高度なデータ解析技術が拓く新たな材料開発の世界へ - 」←産総研のプレスリリース記事、参考サイト：データ駆動型材料設計技術利用推進コンソーシアム
• （プレスリリース情報６０）「ベイズ推定：電子状態解析の新手法を開発 - ベイズ理論を用いてトポロジカル絶縁体におけるディラック電子質量の解析に成功 - 」←東北大学（材料科学高等研究所）のプレスリリース記事
• （プレスリリース情報６１）「機械学習を用いてタンパク質立体構造を評価する構造生物学AI技術を構築」←横浜市立大学のプレスリリース記事
• （プレスリリース情報６２）「ロボット実験×ＡＩによる燃料電池のものづくり研究開発法の革新 〜粉体成膜プロセスインフォマティクスにより３万候補から４０回で新しい最適解の発見に成功〜」←科学技術振興機構(JST)のプレスリリース記事
• （プレスリリース情報６３）「スーパーコンピュータ「富岳」による大規模物性データの自動創出 - 不規則系磁性材料におけるビッグデータの実現へ - 」←東大物性研のプレスリリース記事
• （プレスリリース情報６４）「有機固体で実現する量子スピン液体の特異な性質を解明 人工ニューラルネットワーク第一原理計算による量子物質設計へ」←東大物性研のプレスリリース記事
• （プレスリリース情報６５）「大量の実画像データの収集が不要なAIを開発 - 数式からAIが自動学習、人の判断を経た学習と同程度以上の認識精度を実現 - 」←産総研のプレスリリース記事
• （プレスリリース情報６６）「データサイエンスを活用し、物質表面原子構造を高精度で自動解析 〜専門知識や熟練した技能は不要、材料開発の加速に期待〜」←東京理科大学のプレスリリース記事
• （プレスリリース情報６７）「複数のAIを活用し、複雑な材料データからさまざまな機能を予測する技術を開発 - 配合条件の選定から成形加工・評価までの材料開発を大幅に加速 -」←NEDOのプレスリリース記事
• （プレスリリース情報６８）「計算×情報×実験により 人間の経験則を超えた磁性材料の創製に成功 〜未踏物質の発見をアシスト〜」←東京理科大学のプレスリリース記事
• （プレスリリース情報６９）「厳密な電子状態計算 × 機械学習ポテンシャル：高圧水素における液体-液体相転移の研究」←北陸先端科学技術大学院大学のプレスリリース記事
• （プレスリリース情報７０）「高分子固体電解質をAIによる機械学習で自動設計する新たな手法を開発」←早稲田大学のプレスリリース記事
• （プレスリリース情報７１）「壊れにくい窒化ケイ素セラミックスをAIが予測 - セラミックス材料開発の加速に貢献 - 」←産総研のプレスリリース記事
• （プレスリリース情報７２）「電子状態インフォマティクス・合成・精密測定の三位一体研究によりCO₂ 物理吸収液の機能最適化法を確立」←中央大のプレスリリース記事
• （プレスリリース情報７３）「深層学習でガラスに眠る未来を掘り起こす 原子同士の動き方の関係まで理解するグラフニューラルネットワーク」←東京大学のプレスリリース記事
• （プレスリリース情報７４）「たった一部の情報から、すべての電子構造を決定 -- 原子一つ一つの全電子構造を計測する新手法の開発に、大きな前進 -- 」←東大生産研のプレスリリース記事
• （プレスリリース情報７５）「電子のスピンを用いた人工ニューラルネットワークの 新しい動作原理を発見 - AIハードウェア実現に向けたノイズに強い超大規模並列計算が可能に -」←東大工学部のプレスリリース記事
• （プレスリリース情報７６）「深層学習でナノ粒子評価の長年の課題を解決 - ブラウン運動の軌跡からナノ粒子の形状を識別 -」←東大工学部のプレスリリース記事
• （プレスリリース情報７７）「理論計算と機械学習により無機材料表面の性質を高精度かつ網羅的に予測 - 光触媒材料などの探索や電子・光電子デバイスの設計を支援 - 」←東北大学（金属材料研究所）のプレスリリース記事