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RESEARCH PAPER · Nature · 2018

Machine learning for molecular and materials science

Keith T. Butler, Daniel W. Davies, Hugh Cartwright, Olexandr Isayev, Aron Walsh

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42 direct citing works

Machine learning for molecular and materials scienceOriginalElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition510Transferable Machine-Learning Model of the Electron Density269A quantitative uncertainty metric controls error in neural network-driven chemical discovery240Artificial Intelligence and Personalized Medicine488Deep learning for molecular design—a review of the state of the art677Efficient multi-objective molecular optimization in a continuous latent space260Machine learning enables long time scale molecular photodynamics simulations214Machine learning for renewable energy materials312From DFT to machine learning: recent approaches to materials science–a review849Concepts for improving hydrogen storage in nanoporous materials261Machine learning for composite materials311Structure prediction drives materials discovery717New frontiers for the materials genome initiative562Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals1525Parallel Multistream Training of High-Dimensional Neural Network Potentials224Exploiting machine learning for end-to-end drug discovery and development574Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics332Machine Learning for Computational Heterogeneous Catalysis356Artificial Intelligence in Society480A Structure-Based Drug Discovery Paradigm744Deep Learning in Chemistry561A map of the inorganic ternary metal nitrides430Unsupervised word embeddings capture latent knowledge from materials science literature1115Heterogeneity at multiple length scales in halide perovskite semiconductors274Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review1183Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network277Recent advances and applications of machine learning in solid-state materials science2607Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism1053Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design384Synthetic organic chemistry driven by artificial intelligence303All-optical neural network with nonlinear activation functions474Data‐Driven Materials Science: Status, Challenges, and Perspectives700Machine learning in materials science1058Machine Learning Interatomic Potentials as Emerging Tools for Materials Science1036Autonomous Discovery in the Chemical Sciences Part I: Progress317DScribe: Library of descriptors for machine learning in materials science845Text-mined dataset of inorganic materials synthesis recipes259Uncovering the eutectics design by machine learning in the Al–Co–Cr–Fe–Ni high entropy system275Machine Learning Approaches for Thermoelectric Materials Research231Unsupervised discovery of solid-state lithium ion conductors312Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models461Machine Learning in Nanoscience: Big Data at Small Scales229Machine learning and the physical sciences2417Machine Learning for Catalysis Informatics: Recent Applications and Prospects613Machine learning guided appraisal and exploration of phase design for high entropy alloys327Recent advances in fast-scan cyclic voltammetry235A review of composite solid-state electrolytes for lithium batteries: fundamentals, key materials and advanced structures1030Digital Twin: Values, Challenges and Enablers From a Modeling Perspective1912Dramatically Enhanced Combination of Ultimate Tensile Strength and Electric Conductivity of Alloys via Machine Learning Screening310Explainable Machine Learning for Scientific Insights and Discoveries1026Machine learning the ropes: principles, applications and directions in synthetic chemistry279Machine learning-driven new material discovery296Non-precious-metal catalysts for alkaline water electrolysis: operando characterizations, theoretical calculations, and recent advances884QSAR without borders878Recent advancements in polyethyleneimine-based materials and their biomedical, biotechnology, and biomaterial applications245Recent advances in developing organic electrode materials for multivalent rechargeable batteries256De novo generation of hit-like molecules from gene expression signatures using artificial intelligence419Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning335A Critical Review of Machine Learning of Energy Materials606Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems258High-entropy ceramics2048A perspective on conventional high-temperature superconductors at high pressure: Methods and materials525Closed-loop optimization of fast-charging protocols for batteries with machine learning1153Machine learning: Accelerating materials development for energy storage and conversion370Machine Learning for Molecular Simulation906Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys213Quantum Chemistry in the Age of Machine Learning417Single-particle spectroscopy for functional nanomaterials253Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles313A perspective on conventional high-temperature superconductors at high pressure: Methods and materials433Learning Molecular Representations for Medicinal Chemistry222Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices509Photonic quantum metrology414The promise of implementing machine learning in earthquake engineering: A state‐of‐the‐art review496Exploring the Mechanisms of Selectivity for Environmentally Significant Oxo-Anion Removal during Water Treatment: A Review of Common Competing Oxo-Anions and Tools for Quantifying Selective Adsorption270Big-Data Science in Porous Materials: Materials Genomics and Machine Learning599Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology484Emerging Multifunctional Single-Atom Catalysts/Nanozymes223Mechanical properties and deformation mechanisms of gradient nanostructured metals and alloys846A critical examination of compound stability predictions from machine-learned formation energies268Electronic Structure Modeling of Metal–Organic Frameworks329Machine learning approaches for the prediction of materials properties255Applications of machine learning in metal-organic frameworks225Challenges and opportunities toward fast-charging of lithium-ion batteries303Neural Network Potential Energy Surfaces for Small Molecules and Reactions329Towards data-driven next-generation transmission electron microscopy248Emerging materials intelligence ecosystems propelled by machine learning441Machine Learning for Electronically Excited States of Molecules398Artificial intelligence and machine learning in design of mechanical materials690Materials design by synthetic biology386Developing sustainable, high-performance perovskites in photocatalysis: design strategies and applications254Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review306Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems793Review and outlook on high-entropy alloys for hydrogen storage442Origins of structural and electronic transitions in disordered silicon334Learning properties of ordered and disordered materials from multi-fidelity data216The shape of porphyrins255Machine learning for perovskite materials design and discovery536Data-Driven Strategies for Accelerated Materials Design473Biomimetic and flexible piezoelectric mobile acoustic sensors with multiresonant ultrathin structures for machine learning biometrics219Machine learning in materials science: From explainable predictions to autonomous design281A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics1305Graph Learning: A Survey473Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery538Deep learning model to predict complex stress and strain fields in hierarchical composites389Phase transitions in 2D materials479Two-step machine learning enables optimized nanoparticle synthesis267Phase-field modeling of microstructure evolution: Recent applications, perspectives and challenges238Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation240Unsupervised Learning Methods for Molecular Simulation Data402Machine-learned potentials for next-generation matter simulations560Mesoscopic and multiscale modelling in materials397Copper‐Based Plasmonic Catalysis: Recent Advances and Future Perspectives319Best practices in machine learning for chemistry505Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems840Machine learning for alloys698Autonomous experimentation systems for materials development: A community perspective398Machine learning potentials for complex aqueous systems made simple224Application of Artificial Intelligence in Food Industry—a Guideline315Gaussian Process Regression for Materials and Molecules1221Research status of laser additive manufacturing for metal: a review318Artificial Intelligence Applied to Battery Research: Hype or Reality?503Reviewing machine learning of corrosion prediction in a data-oriented perspective250MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning**237From Fluid Flow to Coupled Processes in Fractured Rock: Recent Advances and New Frontiers336Tackling Climate Change with Machine Learning1015Pushing the Limits of Functionality‐Multiplexing Capability in Metasurface Design Based on Statistical Machine Learning238Analyses of internal structures and defects in materials using physics-informed neural networks393Engineered Living Hydrogels220Recent advances and applications of deep learning methods in materials science1161Perspectives on the integration between first-principles and data-driven modeling225Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Machine learning for high-entropy alloys: Progress, challenges and opportunities397Machine learning for a sustainable energy future51714 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon214Additively manufactured materials and structures: A state-of-the-art review on their mechanical characteristics and energy absorption440Machine Learning Interatomic Potentials and Long-Range Physics223Generative Models as an Emerging Paradigm in the Chemical Sciences313Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design372DeePMD-kit v2: A software package for deep potential models614Diffusion Models: A Comprehensive Survey of Methods and Applications1666Advancements in Microwave Absorption Motivated by Interdisciplinary Research310Scope of machine learning in materials research—A review230Computational chemistry for water-splitting electrocatalysis239Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics257CREST—A program for the exploration of low-energy molecular chemical space574Hydrogen Embrittlement as a Conspicuous Material Challenge─Comprehensive Review and Future Directions405Self-Driving Laboratories for Chemistry and Materials Science674A review of large language models and autonomous agents in chemistry269Integrating artificial intelligence in energy transition: A comprehensive review248

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