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RESEARCH PAPER · Chemical Reviews · 2021

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

John A. Keith, Valentín Vassilev-Galindo, Bingqing Cheng, Stefan Chmiela, Michael Gastegger, Klaus‐Robert Müller, Alexandre Tkatchenko

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Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical SystemsOriginalGenerative models for inverse design of inorganic solid materials38Benchmarking graph neural networks for materials chemistry332Computational discovery of energy materials in the era of big data and machine learning: A critical review60Topological Characterization and Graph Entropies of Tessellations of Kekulene Structures: Existence of Isentropic Structures and Applications to Thermochemistry, Nuclear Magnetic Resonance, and Electron Spin Resonance84Higher-Order Explanations of Graph Neural Networks via Relevant Walks259Three decades of unveiling the complex chemistry of C -nitroso species with computational chemistry30Application of density functional theory and machine learning in heterogenous-based catalytic reactions for hydrogen production72SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects344Machine learning for design principles for single atom catalysts towards electrochemical reactions116Natural language processing models that automate programming will transform chemistry research and teaching59Revealing the thermal decomposition mechanism of RDX crystals by a neural network potential56Understanding the interactions between lithium polysulfides and anchoring materials in advanced lithium–sulfur batteries using density functional theory43Simulation of deep eutectic solvents: Progress to promises86Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning127A Concise Review on Recent Developments of Machine Learning for the Prediction of Vibrational Spectra72Inverse design of 3d molecular structures with conditional generative neural networks188Theoretical Modeling of Electrochemical Proton-Coupled Electron Transfer322Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation118New Insights and Predictions into Complex Homogeneous Reactions Enabled by Computational Chemistry in Synergy with Experiments: Isotopes and Mechanisms37Atomistic modeling of Li- and post-Li-ion batteries49Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics51Exploring the robust extrapolation of high-dimensional machine learning potentials34Thermal Stability of Metal–Organic Frameworks (MOFs): Concept, Determination, and Model Prediction Using Computational Chemistry and Machine Learning85Exploring Complex Reaction Networks Using Neural Network-Based Molecular Dynamics Simulation52Machine learning of material properties: Predictive and interpretable multilinear models114Mechanistic Inference from Statistical Models at Different Data-Size Regimes37Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization160Toward Explainable Artificial Intelligence for Regression Models: A methodological perspective122BIGDML—Towards accurate quantum machine learning force fields for materials74Design, Synthesis, Docking, DFT, MD Simulation Studies of a New Nicotinamide-Based Derivative: In Vitro Anticancer and VEGFR-2 Inhibitory Effects121Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Tackling Disorder in γ‐Ga 2 O 359Accurate large-scale simulations of siliceous zeolites by neural network potentials58Quantitatively Determining Surface–Adsorbate Properties from Vibrational Spectroscopy with Interpretable Machine Learning106Extending machine learning beyond interatomic potentials for predicting molecular properties167Machine Learning Accelerated Discovery of Promising Thermal Energy Storage Materials with High Heat Capacity33Selective functionalization of hindered meta-C–H bond of o-alkylaryl ketones promoted by automation and deep learning31Machine Learning Assisted Graphdiyne-Based Nanozyme Discovery55SELFIES and the future of molecular string representations261How robust are modern graph neural network 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toxic (PMT) and very persistent and very mobile (vPvM) substances based on molecular structure33Machine learning-assisted selection of adsorption-based carbon dioxide capture materials39A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data46Empowering Electrochemical Biosensors with AI: Overcoming Interference for Precise Dopamine Detection in Complex Samples64Explainable Supervised Machine Learning Model To Predict Solvation Gibbs Energy40Improving predictions and understanding of primary and ultimate biodegradation rates with machine learning models32Recent advances and applications of machine learning in electrocatalysis36Elucidation of collagen amino acid interactions with metals (B, Ni) encapsulated graphene/PEDOT material: Insight from DFT calculations and MD simulation32Equivariant Graph Neural Networks for Toxicity Prediction60Hyperactive learning for data-driven interatomic potentials110Accelerated discovery of 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Theoretical Understanding and Computational Simulations45Determining the mechanical and decomposition properties of high energetic materials (α-RDX, β-HMX, and ε-CL-20) using a neural network potential38Fundamentals of the recycling of spent lithium-ion batteries198The thermal decomposition mechanism of RDX/AP composites: ab initio neural network MD simulations32The emergence of machine learning force fields in drug design30Accelerated exploration of heterogeneous CO2 hydrogenation catalysts by Bayesian-optimized high-throughput and automated experimentation56Developing Cheap but Useful Machine Learning-Based Models for Investigating High-Entropy Alloy Catalysts31Some Recent Advances in Density-Based Reactivity Theory40Accelerating materials language processing with large language models78Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey98Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and 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reactions33Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry51Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm86The design space of E(3)-equivariant atom-centred interatomic potentials196The Influence of Ionizing Radiation on Quantification for In Situ and Operando Liquid‐Phase Electron Microscopy50A Review on Revolutionizing Healthcare Technologies with AI and ML Applications in Pharmaceutical Sciences43Enantioselective C–H annulations enabled by either nickel- or cobalt-electrocatalysed C–H activation for catalyst-controlled chemodivergence65The QCML dataset, Quantum chemistry reference data from 33.5M DFT and 14.7B semi-empirical calculations34Latent Ewald summation for machine learning of long-range interactions92A comprehensive review and trends in lubrication modelling30Studying Noncovalent Interactions in Molecular Systems with Machine Learning56Accelerating CO2 direct air capture screening for metal-organic frameworks with a transferable machine learning force field35Autonomous ‘self-driving’ laboratories: a review of technology and policy implications70Physics-Based Solubility Prediction for Organic Molecules31Exploring the role of large language models in the scientific method: from hypothesis to discovery89Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction44Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields10233 Unresolved Questions in Nanoscience and Nanotechnology58Machine learning of charges and long-range interactions from energies and forces41Physics-informed machine learning meets renewable energy systems: A review of advances, challenges, guidelines, and future outlooks53A Universal Augmentation Framework for Long-Range Electrostatics in Machine Learning Interatomic Potentials30

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