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RESEARCH PAPER · Wiley Interdisciplinary Reviews Computational Molecular Science · 2019

Simulation and design of energy materials accelerated by machine learning

Hongshuai Wang, Youyong Li, Youyong Li

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Simulation and design of energy materials accelerated by machine learningOriginalMaking Machine Learning Work in Chemistry: Methodological Innovation, Software Development, and Application Studies3Computational Studies of Photocatalysis with Metal–Organic Frameworks96Unraveling Correlations between Molecular Properties and Device Parameters of Organic Solar Cells Using Machine Learning107ChemML : A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data92Rapid preparation and characterization of oxygen-deficient SnO2 nanobelts with enhanced Li diffusion kinetics14Machine learning assisted materials design and discovery for rechargeable batteries466Critical review of machine learning applications in perovskite solar research139The Hubbard-U correction and optical properties of d metal oxide photocatalysts18Computational Screening of MOFs for CO2 Capture3Recent Advances in Solar Energy Full Spectrum Conversion and Utilization26A Universal Deep Learning Framework based on Graph Neural Network for Virtual Co-Crystal Screening2A Universal Deep Learning Framework based on Graph Neural Network for Virtual Co-Crystal Screening0Machine learning for advanced energy materials208Machine learning for perovskite materials design and discovery536What Can Text Mining Tell Us About Lithium‐Ion Battery Researchers’ Habits?34Free energy of ( CoxMn1−x)3O4 mixed phases from machine-learning-enhanced ab initio calculations6Molecular design and performance improvement in organic solar cells guided by high‐throughput screening and machine learning22Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation240Probing impact of molecular structure on bulk modulus and impact sensitivity of energetic materials by machine learning methods25Machine learning of materials design and state prediction for lithium ion batteries61Structure and Dynamics of Energy Materials from Machine Learning Simulations: A Topical Review†12Scalable Estimator of the Diversity for De Novo Molecular Generation Resulting in a More Robust QM Dataset (OD9) and a More Efficient Molecular Optimization.0Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage260Artificial Intelligence Applied to Battery Research: Hype or Reality?503Scalable estimator of the diversity for de novo molecular generation resulting in a more robust QM dataset (OD9) and a more efficient molecular optimization12Machine-Learning Prediction of Vegard's Law Factor and Volume Size Factor for Binary Substitutional Metallic Solid Solutions31Significance of density functional theory (DFT) calculations for electrocatalysis of N 2 and CO 2 reduction reactions56Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?87Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors122Many-Scale Investigations of the Deformation Behavior of Polycrystalline Composites: I—Machine Learning Applied for Image Segmentation1Text Mining for Energy Materials3A Novel Optimization-Based Convolution Neural Network to Estimate the Contribution of Sensory Inputs to Postural Stability During Quiet Standing9Predicting the Properties of High-Performance Epoxy Resin by Machine Learning Using Molecular Dynamics Simulations22Molecular represention in AI-assisted synthesis chemistry: from sequence to graph1Data-Driven Analysis of Hole-Transporting Materials for Perovskite Solar Cells Performance38Machine learning-guided property prediction of energetic materials: Recent advances, challenges, and perspectives92Understanding the effectiveness of enzyme pre-reaction state by a quantum-based machine learning model31Exploring the fluorination effect mechanism on charge transport in organic solar cells24Recent Advance of Machine Learning in Selecting New Materials7Recent Progress and Perspectives of Solid State Na-CO2 Batteries16Computational insights into the rational design of organic electrode materials for metal ion batteries18AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)122Prediction of Specific Surface Area of Metal-Organic Frameworks by Graph Kernels0Potential electrolytes for solid state batteries and its electrochemical analysis—A review15Efficient screening framework for organic solar cells with deep learning and ensemble learning48Exploring the Co2 Conversion Activated by Plasma Photocatalysis Via Machine Learning0Prediction of specific surface area of metal–organic frameworks by graph kernels2Enhanced Charge Separation and Short-Circuit Current by Doping with Chlorophyll in Cascaded Ternary Solar Cells25Machine Learning Approach for Predicting the Hole Mobility of the Perovskite Solar Cells8Machine learning in energy storage material discovery and performance prediction77Machine Learning Strategies for Forecasting Discharge Capacity in Lithium-Ion Batteries with NCM Layered Cathode Material1Deep-learning-based acceleration of critical point calculations3Accelerated Discovery of Halide Perovskite Materials via Computational Methods: A Review37An application of graph neural network in pollution abatement: Acceleration heterogeneous catalyst design1Computational applications for the discovery of novel antiperovskites and chalcogenide perovskites: a review14Exploring the CO2 conversion activated by the dielectric barrier discharge plasma assisted with photocatalyst via machine learning14Theoretical Calculation–Driven Metal–Organic Framework Nanozymes: Catalytic Mechanisms and Applications9Electro‐Chemo‐Mechanical Degradation in Solid‐State Batteries: A Review of Microscale and Multiphysics Modeling59Machine learning-assisted design of high-performance perovskite photodetectors: a review22Progress in Theoretical Calculation and Simulation of Sulfide Solid Electrolytes and Their Application in All-Solid-State Batteries0Unlocking new horizons, challenges of integrating machine learning to energy conversion and storage research9Simple approach to more efficient density functional theory simulations4A many-objective surrogate optimization model driven by hybrid pilot-test data, molecular reconstruction, and crude oil direct cracking reaction mechanism4The Value of Device Characterization for the Optimization of Organic Solar Cells2Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions2Enhancing energy materials with data-driven methods: A roadmap to long-term hydrogen energy sustainability using machine learning16Design and Application of Electrocatalyst Based on Machine Learning33Prediction of material properties of energetic materials using machine learning methods1Integrating Theory and Experiment with Graph Neural Networks to Classify Molecular Self-Assembly on Metal Surfaces0From Trial‐and‐Error to Artificial Intelligence‐Guided Design of Hard Carbon for Sodium‐Ion Batteries1Fast and Flexible 3D Molecular Design Framework for Novel Organic Optoelectronic Materials0AI‐Driven Detection of Lithium Plating on Graphite Anode0Uncertainty-Aware Automated Machine-Learning Screening of BaTiO3‐Bi(Mg1/2Ti1/2)O3 Candidate Ceramics for X9R Temperature Stability0

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  7. Critical review of machine learning applications in perovskite solar research

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  20. Machine learning of materials design and state prediction for lithium ion batteries

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  21. Structure and Dynamics of Energy Materials from Machine Learning Simulations: A Topical Review†

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