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]
• [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/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/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/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/2404.06628[Quantum graph model][Transport][Filamentary switching]
• 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/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/2404.04023[Phase binarization][Mutually synchronized][Bias field-free spin Hall nano-oscillator][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/2306.03099[CrystalGPT][Enhancing system-to-system transferability][Crystallization prediction][Control][Time-series-transformer]
• http://arXiv.org/abs/1509.02515[Dimensionless mapping][Combinatorial algorithm][Design invisible dopants]
• 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]
• 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/2311.10205[Case study][Multi-modal][Multi-institutional data management][Combinatorial materials science community]
• 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/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/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/2306.06283[14 Examples][How LLMs can transform materials science and chemistry][Reflection][Large language model hackathon]
• 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/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/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/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/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/2203.08205[Learning deep implicit Fourier neural operators][IFNOs][Application][Heterogeneous material modeling]
• https://arxiv.org/abs/2207.03239[Learning][Stress-strain field][Digital composite][Fourier neural operator]
• https://arxiv.org/abs/2305.00478[Domain agnostic][Fourier neural operator]
• https://arxiv.org/abs/2403.18597[Heterogeneous peridynamic neural operator][Discover biotissue constitutive law][Microstructure][Digital Image Correlation Measurement]
• 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/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/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/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/2304.10382[Conditional generative model][Learning stochastic processes]
• 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/1907.00907[Self-learning projective quantum Monte Carlo simulation][Restricted Boltzmann machine]
• https://arxiv.org/abs/2312.14936[PerCNet][Periodic complete representation][Crystal graph]
• 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/2005.01547[Atomic Boltzmann machine][Capable][On-chip learning]
• https://arxiv.org/abs/2009.14777[Helping restricted Boltzmann machine][Quantum-state representation][Restoring symmetry]
• https://arxiv.org/abs/2102.05137[Hardware-aware][In-situ Boltzmann machine 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/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/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/2402.02794[Convolutional restricted Boltzmann machine (CRBM)][Correlated variational wave function][Hubbard model][Square lattice][Mott metal-insulator transition]
• 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/2005.00707[Benchmarking materials property prediction method][Matbench test set][Automatminer reference algorithm]
• https://arxiv.org/abs/2309.02646[Benchmarking inverse optimization algorithm][Molecular materials discovery]
• https://arxiv.org/abs/1710.11611[Online search tool][Graphical patterns][Electronic band structure]
• https://arxiv.org/abs/1808.03383[Pattern learning][Electronic density of states]
• https://arxiv.org/abs/2006.11803[Learning the electronic density of states][Condensed matter]
• http://arXiv.org/abs/1506.02841[Computational 2D materials database]
• 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/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/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/1602.07784[MPInterfaces][Materials project][Python tool][High-throughput Computational Screening][Interfacial system]
• http://arXiv.org/abs/1603.05967[High-throughput search][New ternary superalloys]
• 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/1806.03173[Computational 2D materials database][High-throughput modeling][Discovery][Atomically thin crystal]
• https://arxiv.org/abs/1702.02734[First-principles high-throughput screening][Shape-memory alloy][Energetic][Dynamical][Structural]
• 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/1806.04285[High-throughput computational screening][Two-dimensional semiconductor]
• https://arxiv.org/abs/1806.09733[High-throughput screening algorithm][Relativistic Density Functional Theory][Find chelating agent][Separation][Radioactive waste]
• https://arxiv.org/abs/1807.01273[High-throughput investigation][Tunable][Superconductivity][FeSe film]
• 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/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/1810.10640[High-throughput discovery][Topological material][Spin-orbit spillage]
• 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/1812.06222[Inorganic electride][High-throughput screening]
• 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/2309.01160[Oxygen vacancy formation energy][Metal oxide][High throughput computational studies][Machine learning prediction]
• https://arxiv.org/abs/1907.03811[High-throughput extraction][Anisotropic interdiffusion coefficient][hcp Mg-Al alloy]
• https://arxiv.org/abs/1909.00623[High-throughput computational screening][Solid-state Li-ion conductor]
• https://arxiv.org/abs/1909.00433[Automated][High-throughput][Wannierisation]
• https://arxiv.org/abs/1909.03511[Beyond ternary OPV][High-throughput experimentation][Self-driving laboratory][Optimize multi-component system]
• https://arxiv.org/abs/1909.04195[High dielectric ternary Oxide][Crystal structure prediction][High-throughput screening]
• 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/1910.02984[Accurate high-throughput screening][I-II-V 8-electron Half-Heusler compound][Renewable-energy application]
• 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/2004.07991[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/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]
• https://arxiv.org/abs/2006.01075[High-throughput search][Magnetic and topological order][Transition metal oxide]
• https://arxiv.org/abs/2006.00183[High-throughput screening][Band gap engineering][Sublattice mixing][Cs₂AgBiCl₆]
• 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/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/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/2009.08529[Complex solid solution][Electrocatalyst][Discovery][Prediction][High-throughput experimentation]
• 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/2011.05225[High-throughput screening][Thermoelastic][Ultra-high temperature ceramics]
• 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/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/2104.10235[Accelerated discovery][Molten salt corrosion-resistant alloy][High-throughput experimental and modeling method][Coupled to data analytics]
• https://arxiv.org/abs/1901.09323[Prediction][Silicate glasses' stiffness][High-throughput MD][Machine learning]
• https://arxiv.org/abs/1910.01183[High-throughput DFPT][Machine learning prediction][Infrared][Piezoelectric][Dielectric Response]
• 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/2102.00237[High-throughput search][Magnetic topological material][Spin-orbit spillage][Machine-learning][Experiment]
• 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/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/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/2104.00864[Constrained non-negative matrix factorization][Enabling real-time insight][In situ][High-throughput experiment]
• 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/2105.02296[High-throughput discovery][High-temperature conventional superconductor]
• https://arxiv.org/abs/2307.10728[Sampling][Whole materials space][Conventional superconducting material]
• https://arxiv.org/abs/2105.05160[Research data infrastructure][High-throughput experimental materials science]
• https://arxiv.org/abs/2106.03312[High-throughput study][Antisolvent][Stability][Multicomponent metal halide perovskite][Robotics-based synthesis][Machine learning]
• https://arxiv.org/abs/2106.11985[High-throughput investigation][Topological][Nodal][Superconductor][Phys. Rev. Lett. 129, 027001 (2022)]
• 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/2112.05421[High-throughput computational screening][Bipolar magnetic semiconductor]
• 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/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/2202.05962[High-throughput discovery][Chemical structure-polarity relationship][Combining][Automation][Machine learning]
• https://arxiv.org/abs/2202.09886[High-throughput computational screening][Nanoporous material][Targeted application]
• 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/2204.11223[High-throughput characterization][Transition metal dichalcogenide alloy][Thermodynamic stability][Electronic band alignment]
• https://arxiv.org/abs/2204.14065[Holistic determination][Optoelectronic properties][High-throughput spectroscopy][Surface-guided CsPbBr₃ nanowire]
• https://arxiv.org/abs/2205.11085[High-throughput screening][Piezo-photocatalytic material][Hydrogen production]
• 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/2207.03365[High-throughput screening][Half-antiperovskite][Stacked kagome lattice]
• https://arxiv.org/abs/2207.05353[Absorption][Adsorption][High-throughput computation][Impurities][2D material]
• https://arxiv.org/abs/2207.05283[High-throughput screening][Transition metal single-atom catalyst][Nitrogen reduction reaction]
• https://arxiv.org/abs/2207.09569[Indirect band gap semiconductor][Thin-film photovoltaics][High-throughput calculation][Phonon-assisted absorption]
• https://arxiv.org/abs/2207.10134[High-throughput screening][Strong electron-phonon coupling][Ternary metal diboride]
• 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/2212.04804[Computationally accelerated experimental materials characterization][Drawing inspiration][High-throughput simulation workflow]
• https://arxiv.org/abs/2212.06289[High-throughput screening assisted discovery][Stable layered anti-ferromagnetic semiconductor][CdFeP₂Se₆]
• 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/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/2306.03095[High-throughput search][Triplet point defect][Narrow emission line][2D material]
• 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/2306.17277[Speeding up high-throughput characterization][Materials libraries][Active learning][Autonomous electrical resistance measurement]
• https://arxiv.org/abs/2307.14250[High-throughput density functional theory screening][Double transition metal MXene precursor]
• 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/2308.16174[High-throughput assessment][Microstructural stability][Segregation-engineered nanocrystalline][Al-Ni-Y alloy]
• https://arxiv.org/abs/2309.04822[High-throughput first-principles calculation][Screening][Coherent topologically close-packed precipitate][Hexagonal close-packed metallic system]
• https://arxiv.org/abs/2309.08032[Substitutional quantum defect][WS₂][Discovered][High-throughput computational screening][Fabricated][Site-selective STM manipulation]
• https://arxiv.org/abs/2309.10274[Prediction][Superconductivity][Metallic boron-carbon compound][From 0 to 100 GPa][High-throughput screening]
• https://arxiv.org/abs/2310.00118[Transforming materials discovery][Artificial photosynthesis][High-throughput screening][Earth-abundant semiconductor]
• 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/2311.14497[Advancing high-throughput combinatorial aging studies][Hybrid perovskite thin-film][Precise automated characterization method][Machine learning assisted analysis]
• 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/2401.13211[High-throughput screening][Boride superconductor]
• https://arxiv.org/abs/2402.04726[Phase stability][Mechanical property trend][MAB phase][High-throughput ab initio calculation]
• https://arxiv.org/abs/2403.05952[New direction][Thermoelectrics][Roadmap][High-throughput materials discovery][Advanced device manufacturing]
• https://arxiv.org/abs/2404.02437[High-throughput calculation][Antiferromagnets hosting anomalous transport phenomena][Phys. Rev. B 109, 094435 (2024)]
• 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]
• 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/2403.18987[Reconfigurable multiplex setup][High throughput electrical characterisation][Cryogenic temperature]
• 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]
• http://arxiv.org/abs/1609.04972[Designing nanostructure][Interfacial phonon transport][Bayesian optimization]
• 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/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/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/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/1811.00421[Bayesim][Tool for adaptive grid model fitting][Bayesian inference]
• https://arxiv.org/abs/1812.01205[Bayesian Hamiltonian selection][X-ray photoelectron spectroscopy]
• https://arxiv.org/abs/1903.09385[Bayesian optimization][Chemical composition][Comprehensive framework][RFe₁₂-type magnet compound]
• 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/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/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/2001.09312[Prediction of perovskite-related structure][ACuO_3−x (A = Ca, Sr, Ba, Sc, Y, La)][DFT][Bayesian optimization]
• https://arxiv.org/abs/2002.04977[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/2002.05598[Detecting stable adsorbate][(1S)-camphor on Cu(111)][Bayesian optimization]
• 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/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/2003.09036[Computational design][Stable and highly ion-conductive material][Multi-objective Bayesian optimization][Diffusion of oxygen and lithium]
• 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/2004.12512[Guided search][Desired functional response][Bayesian optimization][Generative model][Hysteresis loop shape engineering][Ferroelectrics]
• 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/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/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/2208.13771[Fast Bayesian optimization][Needle-in-a-Haystack problem][Zooming memory-based initialization]
• 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/2011.10968[Crystal structure prediction][Combining graph network][Bayesian optimization]
• 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/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/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/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/2112.10851[Bayesian][Frequentist][Information geometry approache][Parametric uncertainty quantification][Classical empirical interatomic potential]
• 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/2202.12218[Robust spin relaxometry][Fast adaptive Bayesian estimation]
• https://arxiv.org/abs/2203.07892[Autonomous atomic Hamiltonian construction][Active sampling][x-ray absorption spectroscopy][Adversarial Bayesian optimization]
• 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/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/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/2206.12435[Bayesian optimization][Continuous space][Virtual process embedding]
• 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/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/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]
• https://arxiv.org/abs/2211.03103[Highest melting point material][Searched][Bayesian global optimization][Deep potential molecular dynamics]
• 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/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/2305.05170[Efficient NMR measurement][Data analysis][Bayesian inference][Heavy fermion compound YbCo₂Zn₂₀]
• 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/2309.01271[Bayesian inference][Composition-dependent phase diagram]
• https://arxiv.org/abs/2309.04168[Bayesian optimization][Active learning][Ta-Nb-Hf-Zr-Ti system][Spin transport properties]
• https://arxiv.org/abs/2309.10812[Bond breaking kinetics][Mechanically controlled break junction experiment][Bayesian approach]
• https://arxiv.org/abs/2309.12464[Bayesian optimisation approach][Quantify][Input parameter uncertainty][Prediction][Numerical physics simulation]
• 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/2309.14785[Bayesian inference][Identify crystalline structure][XRD]
• https://arxiv.org/abs/2310.17765[Autonomous convergence][STM control parameter][Bayesian optimization]
• 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/2311.09591[Accelerating material discovery][Threshold-driven hybrid acquisition][Policy-based Bayesian optimization]
• 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/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/2402.02198[Multimodal Co-orchestration][Exploring structure-property relationship][Combinatorial libraries][Multi-task Bayesian optimization]
• https://arxiv.org/abs/2402.04873[Seebeck coefficient][Ionic conductor][Bayesian regression analysis]
• 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/2402.11101[Physics-based material parameters extraction][Perovskite experiment][Bayesian optimization]
• 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/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/1611.03277[Machine-learning based interatomic potential][Amorphous carbon]
• https://arxiv.org/abs/1611.08645[Representation of crystalline compounds][Machine-learning prediction]
• 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/1706.01840[Designing magnetism][Fe-based Heusler alloy][Machine learning approach]
• 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/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/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/1908.00746[Materials informatics][Evolutionary algorithms][Application to search for superconducting hydrogen compound]
• 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/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/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/2310.13136[Approaches for uncertainty quantification][AI-predicted material properties][Comparison]
• 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/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/1709.02727[Machine learning modeling][Superconducting critical temperature]
• 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/1801.08217[Electronic branched Flow][Random potential][Machine learning prediction]
• 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/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/2206.02040[MetaNOR][Meta-learnt nonlocal operator regression approach][Metamaterial modeling]
• https://arxiv.org/abs/2206.06422[Symbolic regression][Materials Science][Discovering interatomic potential][Data]
• 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/1803.03073[Compositional optimization][Hard-magnetic phase][Machine-learning model]
• 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/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/1806.01038[Predicting][Thermodynamic stability][Perovskite oxide][Machine learning model]
• 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/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/1809.01753[Automatic convergence][Machine learning prediction][Monkhorst-Pack k-points][Plane-wave cut-off][DFT]
• https://arxiv.org/abs/1809.03960[Atomic positions independent descriptor][Machine learning][Material properties]
• 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/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/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/1810.12700[Machine learning][Density functional theory][Hubbard model]
• 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/1811.12425[Classifying snapshots][Doped Hubbard model][Machine learning]
• 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/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/1812.11642[Machine learning prediction][DNA charge transport]
• https://arxiv.org/abs/1901.00722[Machine learning approach][Thermal conductivity modeling][Irradiated uranium-molybdenum nuclear fuel]
• 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/2306.08017[Transferable machine learning approach][Predicting electronic structure][Charged defect]
• 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/1901.01972[Machine learning approach][Automated fine-tuning][Semiconductor spin qubit]
• 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/1901.03346[Machine learning assisted measurement][Local topological invariant]
• https://arxiv.org/abs/1901.05801[Machine learning][Novel thermal-materials Discovery][Early Successes][Opportunities][Challenges]
• https://arxiv.org/abs/1901.07900[Characterization][Photoexcited state][Half-filled one-dimensional extended Hubbard model][Machine learning]
• 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/1902.00594[Machine learning approach][Determine the space group of a structure][Atomic pair distribution function][PDF]
• 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/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/1903.02757[Prediction of atomization energy][Au13+ Cluster][Machine learning approach]
• 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/1903.10216[Road to accuracy][Machine-learning-accelerated silicon][Ab initio simulation]
• 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/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/1904.06032[Machine learning][Phase diagram][Half-filled one-dimensional extended Hubbard model]
• 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/1905.02142[Optimizing many-body atomic descriptor][Enhanced computational performance][Machine-learning-based interatomic potential]
• 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/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/1906.02889[Machine learning modeling][High entropy alloy][Short-range order]
• https://arxiv.org/abs/1906.03233[Machine learning prediction][Accurate atomization energy][Organic molecule][Low-fidelity quantum chemical calculation]
• 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/1906.06378[Machine learning model][Lattice thermal conductivity prediction][Inorganic Material]
• 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/1906.11275[Machine learning assisted insight][Spin Ice][Dy₂Ti₂O₇]
• 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/1908.00926[Accelerating approach][Designing ferromagnetic material][Machine leaning modeling][Magnetic ground state][Curie temperature]
• https://arxiv.org/abs/1908.02398[Assessing and improving machine learning model][Prediction][Polymer glass transition Temperature]
• https://arxiv.org/abs/1908.04604[Valley notch filter][Graphene strain superlattice][Green's function][Machine learning approach]
• 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/1908.09102[Accelerating small-angle scattering experiment][Simulation-based machine learning]
• 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/1911.03307[Pushing the limits of atomistic simulations][Ultra-high temperature][Machine-learning force field][ZrB₂]
• 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/1912.10761[Complexity][Transferability][Machine-learning force field][Gold-iron interaction]
• https://arxiv.org/abs/2001.00030[Quantum adversarial][Machine learning]
• 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/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/2205.15151[Berezinskii-Kosterlitz-Thouless transition][Unsupervised machine learning approach]
• 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/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/2004.00080[Machine learning analysis][Perovskite oxides grown][Molecular beam epitaxy]
• https://arxiv.org/abs/2004.00232[Machine learning][Multi-fidelity scale bridging][Dynamical simulation]
• https://arxiv.org/abs/2004.00008[Efficient machine learning approach][Optimizing][Timing resolution][High purity germanium detector]
• 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/2004.04882[Machine-learning based extraction][Short-range part][Interaction][Non-contact atomic force microscopy]
• 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]
• http://arxiv.org/abs/1609.05737[Machine-learned approximation][Density functional theory Hamiltonian]
• 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/2110.00321[Exploring librational pathway][On-the-fly machine-learning force field][Methylammonium molecule][MAPbX₃ (X=I, Br, Cl) perovskite]
• 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/2005.05618[Machine learning guided discovery][Gigantic magnetocaloric effect][HoB₂][Near hydrogen liquefaction temperature]
• 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/2006.06252[Machine learning model][Cluster][Map tribocorrosion regime][Feature space]
• 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/2311.17920[Autoencoder-based analytic continuation method][Strongly correlated quantum system]
• https://arxiv.org/abs/2310.07927[Enhanced sampling][Crystal nucleation][Graph representation learnt variables]
• https://arxiv.org/abs/2309.06449[Quantized non-volatile nanomagnetic synapse][Autoencoder][Efficient unsupervised network anomaly detection]
• https://arxiv.org/abs/2006.11221[Leveraging machine learning][Alleviate Hubbard model][Sign problem]
• 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/2007.03839[Machine-learning model][Raman spectra analysis][Twisted bilayer graphene]
• 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/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/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/2402.03219[Experiment-driven atomistic materials modeling][Case study][Combining][XPS][ML potential][Oxygen-rich amorphous carbon]
• 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/2008.07120[Machine-learning-based sampling method][Exploring local energy minima][Interstitial species in a crystal]
• https://arxiv.org/abs/2008.08341[Phase formation][Mechanical property][Complex concentrated alloy][Machine learning approach]
• https://arxiv.org/abs/2302.08101[GAASP][Genetic algorithm based atomistic sampling protocol][High-entropy material]
• 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/2010.04742[Machine learning approach][Muon spectroscopy analysis]
• 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/2010.12467[Machine-learning-based prediction][Lattice thermal conductivity][Half-Heusler compound][Atomic information]
• https://arxiv.org/abs/2010.13306[Database][Machine learning Model][Identify thermally driven][Metal-insulator transition compound]
• 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/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/2012.08657[Iterative machine learning approach][Discovering unexpected thermal conductivity enhancement][Aperiodic superlattice]
• 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/2012.15739[Uncertainty bound][Multivariate machine learning prediction][High-strain brittle fracture]
• 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/2101.06099[Anharmonic lattice dynamics][Large thermodynamic ensemble][Machine-learning force field][Breakdown][Phonon quasiparticle picture][CsPbBr₃]
• 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/2102.00191[Importance][Feature engineering][Database selection][Machine learning model][Case study][Carbon crystal structure]
• 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/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/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/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/2103.12543[Machine learning prediction][Magnetic][Fe-based metallic glass][Local structure]
• 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/2104.10443[Interpretability][Machine-learning model][Physical science]
• 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/2105.08940[Thermo-mechanical][Nitrogenated holey graphene (C2N)][Comparison][Machine-learning-based][Classical interatomic potential]
• 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/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/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/2107.06044[Parametrization][ Non-bonded force field term][Metal-organic framework][Machine learning approach]
• 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/2107.07028[Machine learning][Materials discovery][Two-dimensional topological insulator]
• https://arxiv.org/abs/2107.07997[Uncertainty prediction][Machine learning model]
• 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/2107.13149[Machine learning approach][Predict L-edge x-ray absorption spectra][Light transition metal ion compound]
• 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/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/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/1802.07605[Accelerating crystal structure prediction][Machine-learning][Interatomic potential][Active learning]
• https://arxiv.org/abs/2108.08766[Molecular simulation-derived feature][Machine learning prediction][Metal glass forming ability]
• 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/2108.13171[Functional nanomaterials Design][Workflow][Building machine-learning model]
• 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/2109.03751[Scale-invariant machine-learning model][Accelerate][Discovery][Quaternary chalcogenide][Ultralow lattice thermal conductivity]
• https://arxiv.org/abs/2109.08005[Elucidating proximity magnetism][Polarized neutron reflectometry][Machine learning]
• https://arxiv.org/abs/2109.08098[MOFSimplify][Machine learning model][Extracted stability data][Three thousand metal-organic frameworks]
• https://arxiv.org/abs/2109.09394[Prediction][Metal alloy][Machine learning]
• https://arxiv.org/abs/2109.10890[Machine-learning enabled search][Next-generation catalyst][Hydrogen evolution reaction]
• https://arxiv.org/abs/2109.13762[Machine learning prediction][Superalloy microstructure]
• 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/2110.02827[Colmena][Scalable machine-learning-based steering][Ensemble simulation][High performance computing]
• 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/2110.12887[Descriptor][Intrinsic hydrodynamic thermal transport][Screening][Phonon database][Machine learning approach]
• https://arxiv.org/abs/2110.13308[Calibrating DFT formation enthalpy calculation][Multi-fidelity machine learning]
• 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/2111.11262[Synergistic coupling][Ab initio-machine learning simulation][Dislocation]
• 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/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/2112.02169[UV-visible absorption spectra][Solvated molecule][Quantum chemical machine learning]
• 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/2112.08676[Machine learning-accelerated computational solid mechanics][Application][Linear elasticity]
• https://arxiv.org/abs/2112.09612[Inorganic synthesis][Reaction condition prediction][Generative machine learning]
• https://arxiv.org/abs/2112.09601[Joint machine learning analysis][Muon spectroscopy data][Different materials]
• 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/2112.11537[Accelerating][Theoretical study][Li-polysulphide adsorption][Single-atom catalyst][Machine learning approache]
• 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/2403.04217[Performance assessment][Universal machine learning interatomic potential][Challenges and Directions][Materials' surfaces]
• 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/2112.14308[Reconstructing][Exit wave][High-resolution transmission electron microscopy][Machine learning]
• https://arxiv.org/abs/2201.00798[Descriptor][Machine learning model][Generalized force field][Condensed matter system]
• 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/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/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]
• https://arxiv.org/abs/2202.00564[Machine learning approach][Quantum phase][Frustrated one dimensional spin-1/2 system]
• https://arxiv.org/abs/2202.00930[Machine learning based prediction][Electronic structure][Quasi-one-dimensional material][Under strain]
• 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/2202.01051[Element selection][Functional materials discovery][Integrated machine learning][Atomic contributions]
• https://arxiv.org/abs/2202.01449[Predicting tensorial molecular properties][Equivariant machine-learning model]
• 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/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/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/2203.01276[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/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/2204.04198[Modern application][Machine learning][Quantum science]
• https://arxiv.org/abs/2204.05967[Machine learning prediction][Local electronic properties][Disordered correlated electron system]
• 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/2204.10430[Simple denoising approach][Exploit multi-fidelity data][Machine learning materials properties]
• 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/2205.02967[Putting density functional theory][Test][Machine-learning-accelerated materials discovery]
• https://arxiv.org/abs/2205.03708[Description][Collective magnetization process][Machine learning model]
• 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/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/2305.02158[Shotgun crystal structure prediction][Machine-learned formation energies]
• 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/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/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 lLearning].
• 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/2207.05546[Enhancing thermoelectric properties][Isotope graphene nanoribbon][Machine learning guided manipulation][Disordered antidot][Interface]
• 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/2211.16576[Physics-based machine learning approach][Modeling][Temperature-dependent yield strength][Superalloy]
• 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/2207.09444[Machine learning approach][Genome][Two-dimensional material][Flat electronic band]
• 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/2208.04172[Impact of physicochemical features][Carbon electrode][Capacitive performance][Supercapacitor][Machine learning approach]
• 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/2208.06139[Beyond potential][Integrated machine-learning model]
• https://arxiv.org/abs/2208.05546[Data-driven machine learning][Predict mechanical properties][Monolayer TMD]
• https://arxiv.org/abs/2307.05211[Data-driven machine learning approach][Predicting yield strength][Additively manufactured multi-principal element alloy]
• https://arxiv.org/abs/2208.05672[Searching][Chromate replacement][Natural language processing][Machine learning algorithm]
• https://arxiv.org/abs/2208.04976[Machine learning][1- and 2-electron reduced density matrices][Polymeric molecule]
• 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/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/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/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]
• https://arxiv.org/abs/2403.00377[Emergence][Accurate atomic energies][Machine learned noble gas potential]
• https://arxiv.org/abs/2208.11448[nanoNET][Machine learning platform][Predicting nanoparticles distribution][Polymer matrix]
• https://arxiv.org/abs/2208.12736[Machine-learning based screening][Lead-free halide double perovskite][Photovoltaic application]
• https://arxiv.org/abs/2208.12705[Machine learning approach][Predict][Structural and magnetic properties][Heusler alloy families]
• 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/2208.13742[Machine learning guided high-throughput search][Non-oxide garnet]
• https://arxiv.org/abs/2209.00628[Monotonic Gaussian process][Physics-constrained machine learning][Materials science application]
• 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/2209.03173[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/2209.07145[Machine learning based modeling][Disordered elemental semiconductor][Understanding][Atomic structure][a-Si][a-C]
• https://arxiv.org/abs/2209.07578[Pixel-wise classification][Graphene-detection][Tree-based machine learning algorithm]
• https://arxiv.org/abs/2209.08595[Low-cost machine learning approach][Prediction][Transition metal phosphor][Excited state properties]
• 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/2210.08878[Hundreds][New][Stable][One-dimensional material][Generative machine learning model]
• 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/2210.13587[Quantifying][Performance][Machine learning model][Materials discovery]
• https://arxiv.org/abs/2210.13597[Critical examination][Robustness][Generalizability][Machine learning prediction][Materials properties]
• https://arxiv.org/abs/2210.14191[Database][Ultrastable MOFs][Reassembled][Stable fragment][Machine learning model]
• 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/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/2211.04373[Machine learning approach][Drawing phase diagram][Topological lasing mode]
• 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/2211.07999[Machine-learning based prediction][Small molecule][Surface interaction potential]
• 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/2211.09584[Mitigating][Hubbard sign problem][Novel application][Machine learning]
• 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/2212.09796[Machine learning assisted derivation][Effective low energy model][Metallic magnet]
• https://arxiv.org/abs/2212.10478[Machine learning][Polymer self-consistent field theory][Two spatial dimension]
• https://arxiv.org/abs/2212.10283[Interpretable model][Extrapolation][Scientific machine learning]
• 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/2303.14434[Heat flux][Semi-local machine-learning potential]
• https://arxiv.org/abs/2303.16597[Machine-learning surrogate model][Accelerating][Search][Stable ternary alloy]
• https://arxiv.org/abs/2303.16538[Efficient generation][Stable linear machine-learning force field][Uncertainty-aware active learning]
• 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/2304.13932[Yield strength-plasticity][Trade-off][Uncertainty quantification][Machine-learning-based Design][Refractory high-entropy alloy]
• 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/2305.10255[First-principles machine-learning force field][Heterogeneous ice nucleation][Microcline feldspar]
• https://arxiv.org/abs/2305.12060[Mechanical property design][Bio-compatible Mg alloy][Machine-learning algorithm]
• 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/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/2309.00996[Machine-learning potential][Nanoscale simulation][Deformation][Fracture][Example of TiB₂ ceramic]
• https://arxiv.org/abs/2310.09062[Mechanisms of temperature-dependent thermal transport][Amorphous silica][Machine-learning molecular dynamics]
• https://arxiv.org/abs/2310.15591[Machine-learning-based non-local kinetic energy density functional][Simple metals and alloys]
• 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/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/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/2301.07790[Machine learning based approach][Predict ductile damage model parameter][Polycrystalline metal]
• 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/2302.02303[Inorganic synthesis recommendation][Machine learning materials similarity][Scientific literature]
• 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/2302.09507[Predicting structure-dependent Hubbard U parameter][Assessing hybrid functional-level exchange][Machine learning]
• https://arxiv.org/abs/2302.09542[Dynamics of growing carbon nanotube interface][Machine learning-enabled molecular simulation]
• 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/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/2303.09342[Machine learning guided discovery][Superconducting calcium borocarbide]
• 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/2304.01146[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/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/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/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/2303.12638[Scientific machine learning framework][Understand flash graphene synthesis]
• 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/2305.12010[Chemellia][Ecosystem][Atomistic scientific machine learning]
• 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]
• 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/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/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/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/2306.11853[Generalization across experimental parameter][Machine learning analysis][High resolution transmission electron microscopy dataset]
• 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/2307.06556[Metal oxide-based gas sensor array][VOCs analysis][Complex Mixtures][Machine learning]
• https://arxiv.org/abs/2307.06879[Machine learning prediction][High-Curie-Temperature Material]
• https://arxiv.org/abs/2307.11068[Machine learning][Majorana nanowire][Disorder landscape]
• 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/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/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/2312.02475[Accurate machine learning prediction][Coercivity][High-performance permanent magnet]
• https://arxiv.org/abs/2312.17596[Accurate machine learning interatomic potential][FCC and HCP nickel]
• https://arxiv.org/abs/2401.10998[Leveraging domain adaptation][Accurate machine learning prediction][New halide Perovskites]
• https://arxiv.org/abs/1610.02098[Machine learning force fields][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/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/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/2309.07424[Composition][Structure][GGA bandgap prediction][Machine learning approach]
• 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/2310.05018[Human-in-the-loop][Machine learning][Automated electron microscopy]
• https://arxiv.org/abs/2310.04667[Machine learning prediction][Self-assembly][Analysis][Molecular structure dependence][Critical packing parameter]
• 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/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/2311.06010[Machine learning-driven structure prediction][Iron hydride]
• 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/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/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/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/2401.12042[Machine learning based prediction][Polaron-vacancy pattern][TiO₂(110) surface]
• 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/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/2402.09128[Machine learning assisted prediction][Organic salt structure properties]
• 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/2403.02553[Enhancing magnetocaloric material discovery][Machine learning approach][Autogenerated Database][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/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/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/2312.03687[MatterGen][Generative model][Inorganic materials design]
• 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/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/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/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/1802.03008[Deep learning approach][Identify local structure][Atomic-resolution transmission electron microscopy image]
• 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/1901.00915[Deep learning approach][Structural analysis of proteins]
• 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/1904.05433[Phase segmentation][Atom-probe tomography][Deep learning-based edge detection]
• https://arxiv.org/abs/1905.03928[Deep learning model][Atomic structures prediction][X-ray absorption spectroscopic data]
• https://arxiv.org/abs/1905.10730[Deep learning-enhanced variational Monte Carlo method][Quantum many-body physics]
• 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/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/2302.00261[Deep learning model][Inverse design][Semiconductor heterostructure][Desired electronic band structure]
• 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/1812.01995[Deep learning model][Finding new superconductors]
• https://arxiv.org/abs/1912.03296[Rapid exploration][Topological band structure][Deep learning]
• https://arxiv.org/abs/1912.06077[Understanding important features][Deep learning model][Transmission electron microscopy image segmentation]
• 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/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/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/2212.04457[Spatio-temporal super-resolution][Dynamical system][Physics-informed deep-learning]
• 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/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/2310.07836[Revolutionising inverse design][Magnesium alloy][Generative adversarial network]
• 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/2003.11505[Deep learning approach][Determining the chiral indices][Carbon nanotube][High-resolution transmission electron microscopy image]
• 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/2105.07485[AtomAI][Deep learning framework][Analysis][Image][Spectroscopy data][(Scanning) transmission electron microscopy and Beyond]
• 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/2105.12638[Predicting aqueous solubility][Organic molecule][Deep learning model][Varied molecular representation]
• 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/2110.13720[Deep DIC][Deep learning-based digital image correlation][End-to-end displacement][Strain measurement]
• https://arxiv.org/abs/2111.05271[Stress field prediction][Fiber-reinforced composite material][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/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/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/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/2201.07342[Deep learning approach][Semantic segmentation][Unbalanced data][Electron tomography][Catalytic material]
• 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/2203.06820[Edge detection][Image filter algorithm][Spectroscopic analysis][Deep learning application]
• https://arxiv.org/abs/2203.14326[DeepXRD][Deep learning model][Predicting][XRD spectrum][Materials composition]
• 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/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/2206.11457[Exploring physics][Ferroelectric domain wall][Real time][Deep learning][Scanning probe microscopy]
• 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/2207.05013[Boosting heterogeneous catalyst discovery][Structurally constrained deep learning model]
• https://arxiv.org/abs/2207.08681[Deep learning potential][Mean force][Polymer grafted nanoparticle]
• https://arxiv.org/abs/2207.10173[Benchmark test][Atom segmentation deep learning model][Consistent dataset]
• 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/2211.01490[Deep learning Hamiltonian][Disordered image data][Quantum material]
• 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/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/2211.11543[Leveraging orbital information][Atomic feature][Deep learning model]
• 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/2002.11315[Assessing graph-based deep learning Model][Predicting flash point]
• https://arxiv.org/abs/2007.04898[Difference-based deep learning framework][Stress prediction][Heterogeneous media]
• 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/2403.08596[Patching-based deep learning model][Inpainting][Bragg coherent diffraction pattern][Detectors' gaps]
• https://arxiv.org/abs/2212.13809[Atomic force microscopy simulation][CO-functionalized tip][Deep learning]
• https://arxiv.org/abs/2212.14601[Accelerated multiscale mechanics modeling][Deep learning framework]
• 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/2303.16412[Comprehensive][Versatile][Multimodal deep learning approach][Predicting][Diverse properties][Advanced material]
• https://arxiv.org/abs/2304.00616[Robust deep learning framework][Constitutive-relation modeling]
• 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/2304.09606[Screening spin lattice interaction][Deep learning approach]
• 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/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/2307.04638[DeePTB][Deep learning-based tight-binding approach][Ab initio accuracy]
• https://arxiv.org/abs/2307.04283[Deep learning][CALPHAD modeling][Universal parameter learning][Solely][Chemical formula]
• https://arxiv.org/abs/2307.04121[Deep learning framework][Solving hyperbolic partial differential equation][Part I]
• 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/2309.09355[Structure to property][Chemical element embedding][Deep learning approach][Accurate prediction][Chemical properties]
• 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/2310.01117[Predicting emergence][Crystal][Amorphous matter][Deep learning]
• https://arxiv.org/abs/2310.06852[DeepCrysTet][Deep learning approach][Tetrahedral mesh][Predicting properties][Crystalline material]
• 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/2310.15188[Deep learning approaches][Dynamic mechanical analysis][Viscoelastic fiber composite]
• https://arxiv.org/abs/2310.18907[Topological, or Non-topological?][Deep learning based prediction]
• 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/2312.02871[Attention-enhanced neural differential equation][Physics-informed deep learning][Ion transport]
• 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/2401.03004[SAPNet][Deep learning model][Identification][Single-molecule peptide post-translational modification][Surface enhanced Raman spectroscopy]
• 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/2402.10931[Enabling discovery][Enhanced generalisability][Deep learning model]
• 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/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/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/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/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/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/1806.05772[Network analysis][Synthesizable materials discovery]
• https://arxiv.org/abs/1811.06060[Hybrid generative-discriminative model][Inverse materials design]
• https://arxiv.org/abs/2401.06779[VAE][Modified 1-hot generative materials modeling][Inverse material design]
• 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/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/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/2304.08446[Inverse design][Next-generation superconductor][Data-driven deep generative model]
• 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/2311.13328[MagGen][Graph aided deep generative model][Inverse design][Stable][Permanent magnet]
• https://arxiv.org/abs/2311.13812[Mechanical characterization][Inverse design][Stochastic architected metamaterial][Neural operator]
• https://arxiv.org/abs/2312.03690[Inverse design][Vitrimeric polymer][Molecular dynamics][Generative modeling]
• 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/2202.06699[Inverse-designed metaphotonics][Hypersensitive detection]
• https://arxiv.org/abs/2403.17724[Magnonic inverse-design processor]
• https://arxiv.org/abs/2203.04491[Towards large-scale][Spatio-temporally resolved diagnosis][Electronic density of states][DNN]
• 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/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/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/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/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/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/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/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/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/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/1702.05771[Predicting electronic structure properties][Transition metal complexes][Neural Network]
• https://arxiv.org/abs/1803.03039[Design][Nickel-base superalloy][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/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/1807.03404[Hierarchical visualization][Materials space][Graph convolutional][Neural network]
• 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/1905.10407[De novo exploration][Self-guided 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/1810.03787[Quantum convolutional][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/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]
• 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/2402.11179[Uncertainty quantification][Graph convolution neural network model][Evolving Processes]
• 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/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/2211.11786[Model-independent learning][Quantum phases of matter][Quantum convolutional neural network]
• 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/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/1907.03055[PANNA][Artificial neural network architecture]
• 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/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/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/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/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/2006.05708[Image reconstruction][Multimode fiber][Simple neural network architecture]
• https://arxiv.org/abs/2104.02529[Active learning][Element embedding approach][Neural network][Infinite-layer][Perovskite oxide]
• https://arxiv.org/abs/2007.00335[Predicting][Oxidation][Spin state][High-dimensional neural network][Lithium manganese oxide spinel]
• 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/2007.00327[Closing the Gap][Theory and experiment][Lithium manganese oxide spinel][High-dimensional neural network potential]
• 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/2009.06484[Fourth-generation high-dimensional neural network potential][Accurate electrostatics][Non-local charge transfer]
• 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/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/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/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/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/2311.13434[Recurrent neural network][Transfer learning][Elasto-plasticity][Woven composites]
• https://arxiv.org/abs/2309.04482[Addressing][Accuracy-cost tradeoff][Material property prediction][Teacher-student strategy]
• https://arxiv.org/abs/2312.14485[Self-supervised generative model][Crystal structure]
• 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/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/2101.11099[Neural network][Quantum many-body physics][Hands-on tutorial]
• https://arxiv.org/abs/2102.01880[High-throughput discovery][Novel cubic crystal material][Deep generative neural network]
• 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/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]
• 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/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/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/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/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/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/2112.04881[Application][Neural network][Exchange-correlation functional interpolation]
• 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/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/2301.02191[Physics informed neural network][Charged particle][Surrounded][Conductive boundaries]
• 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/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/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/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/2204.11904[Neural network representation][Minimally entangled typical thermal 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/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/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/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/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/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/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]
• 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/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/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/2404.03863[Establishing][Relationship][Generalized crystallographic texture][Macroscopic yield surface][Partial input convex neural network]
• 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/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/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/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/2211.10342[Machine-learning enabled optimization][Atomic structure][Fractional existence]
• 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/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/2403.18298[Deciphering chemical ordering][High entropy material][Machine learning-accelerated high-throughput cluster expansion approach]
• 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/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/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/1908.03665[Robust data-driven approach][Predicting][Configurational energy][High entropy alloy]
• 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/1910.11499[Deep generative model][Inorganic chemical composition]
• https://arxiv.org/abs/2403.11686[Crystalformer][Infinitely connected attention][Periodic structure encoding]
• https://arxiv.org/abs/1909.00949[Data-driven approach][Encoding][Decoding][3-D crystal structure]
• 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/1902.10282[Prediction of activation energy barrier][Island diffusion process][Data-driven approach]
• 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/2102.01131[Data-driven approach][Parameterize SCAN+U][Accurate description][3d transition metal oxide thermochemistry]
• 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/2108.13206[Data-driven approach][Identification][Novel organic ferroelectrics]
• 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/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/2202.01373[Data-driven discovery][High performance][Layered van der Waals piezoelectric NbOI₂]
• 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/2206.12159[Data-driven discovery][Novel 2D material][Deep generative model]
• 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/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/2302.03843[Data-driven prediction][Complex crystal structure][Dense lithium]
• 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/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/2311.14196[Data-driven discovery][Dynamics][Time-resolved coherent scattering]
• https://arxiv.org/abs/2401.06070[Peridynamic neural operator][Data-driven nonlocal constitutive model][Complex material response]
• 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/2402.12520[Data-driven study][Composition-dependent phase compatibility][NiTi shape memory alloy]
• 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/2209.13700[Data driven approach][Cross-slip modelling][Continuum dislocation dynamics]
• 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/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/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/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/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/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/2307.06901[Better sensing][Variable-range interaction]
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• Yosuke Harashima, Tomohiro Mano, Keith Slevin, and Tomi Ohtsuki, J. Phys. Soc. Jpn. 90, 094001 (2021)[Analysis of Kohn-Sham eigenfunctions][Convolutional neural network][Metalinsulator transition][Doped semiconductor]
• Ayu Irie, Shogo Fukushima, Akihide Koura, Kohei Shimamura, and Fuyuki Shimojo, J. Phys. Soc. Jpn. 90, 094603 (2021)[Reproduction][Melting][Crystallization][Sodium][Machine-learning interatomic potential][Artificial neural network]
• S. Kiyohara and T. Mizoguchi, J. Phys. Soc. Jpn. 89, 103001 (2020)[Radial distribution function][X-ray absorption][Near edge structure][Artificial neural network]
• Yue Li, Xuyang Zhou, Timoteo Colnaghi, Ye Wei, Andreas Marek, Hongxiang Li, Stefan Bauer, Markus Rampp, Leigh Stephenson, NPJ Computational Materials 7, 8 (2021) [Convolutional neural network-assisted recognition][Nanoscale L12 ordered structure][Face-centred cubic alloy][arXiv:2102.11024]
• Jutta Rogal, Elia Schneider, and Mark E. Tuckerman, Phys. Rev. Lett. 123, 245701 (2019)[Neural-network-based path collective variable][Enhanced sampling][Phase Transformation]
• Giacomo Torlai, Brian Timar, Evert P. L. van Nieuwenburg, Harry Levine, Ahmed Omran, Alexander Keesling, Hannes Bernien, Markus Greiner, Vladan Vuletic, Mikhail, D. Lukin, Roger G. Melko, and Manuel Endres, Phys. Rev. Lett. 123, 230504 (2019)[Integrating neural network][Quantum simulator][State reconstruction]
• Yu Li, Youjun Xu, Meiling Jiang, Bowen Li, Tianyang Han, Cheng Chi, Feng Lin, Bo Shen, Xing Zhu, Luhua Lai, and Zheyu Fang, Phys. Rev. Lett. 123, 213902 (2019)[Self-learning perfect optical chirality][Deep neural network].
• L. Storm, H. Linander, J. Bec, K. Gustavsson, and B. Mehlig, Phys. Rev. Lett. 132, 057301 (2024) [Finite-time Lyapunov exponent][Deep neural network].
• 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]
• 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]
• 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]
• 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]
• Rei Nishimura, Shun Katakami, Kenji Nagata, Masaichiro Mizumaki, and Masato Okada, J. Phys. Soc. Jpn. 93, 034003 (2024)[Bayesian integration][Hamiltonian parameter][Crystal field]
• 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]
• 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]
• 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]
• 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]
• 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]
• 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]
• Andrew H. Proppe, Kin Long Kelvin Lee, Alexander E.K. Kaplan, Matthias Ginterseder, Chantalle J. Krajewska, and Moungi G. Bawendi, Phys. Rev. Lett. 131, 053603 (2023)[Time-resolved line shape][Single quantum emitter][Machine learned photon correlation]
• M.F. Kasim and S.M. Vinko, Phys. Rev. Lett. 127, 126403 (2021)[Learning][Exchange-correlation functional][Nature][Fully differentiable DFT]
• Isao Ohkubo, Zhufeng Hou, Jiyeon N. Lee, Takashi Aizawa, Mikk Lippmaa, Toyohiro Chikyow, Koji Tsuda, and Takao Mori, Materials Today Physics 16 (2020) 100296 [Realization][Closed-loop optimization][Epitaxial titanium nitride thin-film growth][Machine learning]
• Yuqing Jin, Takahiro Kozawa and Takao Tamura, Jpn. J. Appl. Phys. 60, 076509 (2021)[Analysis][Mitigating factor][Line edge roughness][Electron beam lithography][Machine learning]
• Sena Suzuki and Jun Kondoh, Jpn. J. Appl. Phys. 60, SDDC09 (2021)[Cantilever damage evaluation][Impedance-loaded SAW sensor][Continuous wavelet analysis][Machine learning]
• N. Tanaka, et al., Jpn. J. Appl. Phys. 60 066503 (2021)[Analysis][Dissolution kinetics][Narrow polydispersity][Alkaline aqueous solution][Machine learning]
• Hongpeng Wang, Shangce Gao, Michiya Mozumi, Masaaki Omura, Ryo Nagaoka and Hideyuki Hasegawa, Jpn. J. Appl. Phys. 60, SDDE21 (2021)[Preliminary investigation][Clutter filtering][Deep learning]
• K. Ando, et al., Jpn. J. Appl. Phys. 59 SKKE06 (2020)[Speckle reduction][Medical ultrasound image][Deep learning][Fully convolutional network]
• N. Takahashi, Y. Liu and C. Kaneta, Jpn. J. Appl. Phys. 59 051005 (2020)[Materials informatics approach][Design][Si/Ge layered][Nanostructure][Low thermal conductivity]
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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
• （プレスリリース情報２８）「光でプラスチックの劣化が診断可能に！？ - 近赤外光と機械学習による材料診断 - 」←産総研のプレスリリース記事
• （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
• （プレスリリース情報４５）「リチウムイオン電池の高容量化に向けたデータ科学的手法による物質探索」←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
• （プレスリリース情報４７）「結合前の情報だけで、結合後の性質を高精度に予測 - 化学反応や触媒の予測への応用に期待 - 」←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