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RESEARCH PAPER · Advanced Energy Materials · 2020

A Critical Review of Machine Learning of Energy Materials

Chi Chen, Yunxing Zuo, Weike Ye, Xiangguo Li, Zhi Deng, Shyue Ping Ong

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A Critical Review of Machine Learning of Energy MaterialsOriginalElectrocatalyst design for aprotic Li–CO 2 batteries115Graph convolutional neural networks with global attention for improved materials property prediction257Benchmark Synthetic Training Data for Artificial Intelligence-based Li-ion Diagnosis and Prognosis155Quantum Chemistry-Informed Active Learning to Accelerate the Design and Discovery of Sustainable Energy Storage Materials88Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions120Machine learning assisted materials design and discovery for rechargeable batteries466Modelling and understanding battery materials with machine-learning-driven atomistic simulations114Machine learning in experimental materials chemistry66Effect of Increasing the Descriptor Set on Machine Learning Prediction of Small Molecule-Based Organic Solar Cells102Machine learning for halide perovskite materials130Design of Polymer Blend Electrolytes through a Machine Learning Approach67Machine Learning and Digital Twin Driven Diagnostics and Prognostics of Light‐Emitting Diodes79Critical review of machine learning applications in perovskite solar research139Machine Learning for Atomic Simulation and Activity Prediction in Heterogeneous Catalysis: Current Status and Future185Data-driven assessment of electrode calendering process by combining experimental results, in silico mesostructures generation and machine learning129Ultra-fast and accurate binding energy prediction of shuttle effect-suppressive sulfur hosts for lithium-sulfur batteries using machine learning91Accelerating Discovery of Metal–Organic Frameworks for Methane Adsorption with Hierarchical Screening and Deep Learning74Machine learning for high performance organic solar cells: current scenario and future prospects375Accelerated discovery of stable spinels in energy systems via machine learning56Machine learning toward advanced energy storage devices and systems199The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety303A data-driven XRD analysis protocol for phase identification and phase-fraction prediction of multiphase inorganic compounds58Bandgap tuning strategy by cations and halide ions of lead halide perovskites learned from machine learning107Designing positive electrodes with high energy density for lithium-ion batteries84High-throughput virtual screening for organic electronics: a comparative study of alternative strategies68Machine learned features from density of states for accurate adsorption energy prediction257Digital Transformation in Materials Science: A Paradigm Change in Material's Development72Opportunities for Next-Generation Luminescent Materials through Artificial Intelligence85Machine learning for advanced energy materials208Machine learning for perovskite materials design and discovery536Predicting adsorption ability of adsorbents at arbitrary sites for pollutants using deep transfer learning50Machine learning for design of thin-film nanocomposite membranes88Materials discovery through machine learning formation energy78Machine learning aided design of perovskite oxide materials for photocatalytic water splitting156Machine learning approach for the prediction and optimization of thermal transport properties71Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration65Machine Learning for Advanced Design of Nanocomposite Ultrafiltration Membranes72Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning126Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery538Deep learning for ultra-fast and high precision screening of energy materials55Guiding the Design of Heterogeneous Electrode Microstructures for Li‐Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning129Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation240Machine learning of materials design and state prediction for lithium ion batteries61Open Catalyst 2020 (OC20) Dataset and Community Challenges765Machine Learning in Magnetic Materials48Benchmarking graph neural networks for materials chemistry332Prediction of future capacity and internal resistance of Li-ion cells from one cycle of input data84Computational discovery of energy materials in the era of big data and machine learning: A critical review60The data-intensive scientific revolution occurring where two-dimensional materials meet machine learning62Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale166Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale56Catalyze Materials Science with Machine Learning67Designing polymer nanocomposites with high energy density using machine learning81A Machine Learning Shortcut for Screening the Spinel Structures of Mg/Zn Ion Battery Cathodes with a High Conductivity and Rapid Ion Kinetics66Kinetic Monte Carlo simulations applied to Li-ion and post Li-ion batteries: a key link in the multi-scale chain48The role of artificial intelligence in the mass adoption of electric vehicles172Machine Learning Roadmap for Perovskite Photovoltaics85Energy Consumption and Spatial Assessment of Renewable Energy Penetration and Building Energy Efficiency in Malaysia: A Review64Machine Learning‐Based and Experimentally Validated Optimal Adhesive Fibril Designs54Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers61Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries412Relationships between Distortions of Inorganic Framework and Band Gap of Layered Hybrid Halide Perovskites68Artificial Intelligence Applied to Battery Research: Hype or Reality?503Perspectives for next generation lithium-ion battery cathode materials102Ab initio molecular dynamics with enhanced sampling in heterogeneous catalysis97AtomSets as a hierarchical transfer learning framework for small and large materials datasets76Inverse design of two-dimensional materials with invertible neural networks69Data-Driven Methods for Accelerating Polymer Design123High‐Efficiency Non‐Fullerene Acceptors Developed by Machine Learning and Quantum Chemistry78Design high-entropy carbide ceramics from machine learning149Modeling the Solid Electrolyte Interphase: Machine Learning as a Game Changer?87Nanosphere Lithography: A Versatile Approach to Develop Transparent Conductive Films for Optoelectronic Applications128A review of the recent progress in battery informatics178How Machine Learning Predicts and Explains the Performance of Perovskite Solar Cells86CHAIN: unlocking informatics‐aided design of Li metal anode from materials to applications65Machine learning accelerated calculation and design of electrocatalysts for CO2 reduction91Machine learning analysis of alloying element effects on hydrogen storage properties of AB2 metal hydrides118Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm707Emerging Trends in Sustainable CO 2 ‐Management Materials151Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation118Intelligent disassembly of electric-vehicle batteries: a forward-looking overview172Machine learning in energy storage materials106Recent advances and applications of deep learning methods in materials science1161MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art70Data‐Driven Materials Innovation and Applications187Machine learning predicts and optimizes hydrothermal liquefaction of biomass204Scalable deeper graph neural networks for high-performance materials property prediction111MatSciBERT: A materials domain language model for text mining and information extraction342Artificial Intelligence for Electricity Supply Chain automation84Review on 2D Molybdenum Diselenide (MoSe 2 ) and Its Hybrids for Green Hydrogen (H 2 ) Generation Applications97Recent progress in bismuth-based high Curie temperature piezo-/ferroelectric perovskites for electromechanical transduction applications88Material machine learning for alloys: Applications, challenges and perspectives150Porous Electrode Modeling and its Applications to Li‐Ion Batteries190Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery552Feature selection method reducing correlations among features by embedding domain knowledge101A Generative Approach to Materials Discovery, Design, and Optimization121Data-driven machine learning approach for predicting the capacitance of graphene-based supercapacitor electrodes98Machine learning-guided property prediction of energetic materials: Recent advances, challenges, and perspectives92A flexible and scalable scheme for mixing computed formation energies from different levels of theory49Adsorption Energy in Oxygen Electrocatalysis299Machine Learning Assisted Graphdiyne-Based Nanozyme Discovery55Towards green smart cities using Internet of Things and optimization algorithms: A systematic and bibliometric review75Challenges and Opportunities of Chemiresistors Based on Microelectromechanical Systems for Chemical Olfaction52Using Machine Learning to make nanomaterials sustainable57Sustainable energies and machine learning: An organized review of recent applications and challenges79Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges66Artificial Intelligence and Machine Learning for Energy Consumption and Production in Emerging Markets: A Review151Designing All-Solid-State Batteries by Theoretical Computation: A Review122Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects170Overview on Theoretical Simulations of Lithium‐Ion Batteries and Their Application to Battery Separators160The role of machine learning in carbon neutrality: Catalyst property prediction, design, and synthesis for carbon dioxide reduction62Advances in machine learning- and artificial intelligence-assisted material design of steels109Mechanical energy metamaterials in interstellar travel74Energy Usage Forecasting Model Based on Long Short-Term Memory (LSTM) and eXplainable Artificial Intelligence (XAI)57A Surrogate Machine Learning Model for the Design of Single-Atom Catalyst on Carbon and Porphyrin Supports towards Electrochemistry56Auto-MatRegressor: liberating machine learning alchemists63AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)122Machine Learning-Driven Discovery of Key Descriptors for CO 2 Activation over Two-Dimensional Transition Metal Carbides and Nitrides54Thermodynamics and kinetic analysis of membrane: Challenges and perspectives52First‐Row Transition Metals for Catalyzing Oxygen Redox51An artificial neural network model for capacitance prediction of porous carbon-based supercapacitor electrodes78Batteries temperature prediction and thermal management using machine learning: An overview132Active Site Engineering in Reticular Covalent Organic Frameworks for Photocatalytic CO 2 Reduction94A comprehensive review of artificial intelligence and machine learning applications in energy consumption and production52Advancements in Microwave Absorption Motivated by Interdisciplinary Research310Efficient screening framework for organic solar cells with deep learning and ensemble learning48Advances of machine learning in materials science: Ideas and techniques105Microwave Encounters Ionic Liquid: Synergistic Mechanism, Synthesis and Emerging Applications71Lead-Free Double Perovskites: A Review of the Structural, Optoelectronic, Mechanical, and Thermoelectric Properties Derived from First-Principles Calculations, and Materials Design Applicable for Pedagogical Purposes94Advancements and Challenges in Solid-State Battery Technology: An In-Depth Review of Solid Electrolytes and Anode Innovations193Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry93From biomass to biocrude: Innovations in hydrothermal liquefaction and upgrading132Machine Learning Design of Perovskite Catalytic Properties83Machine learning for membrane design in energy production, gas separation, and water treatment: a review111Machine learning-assisted screening of metal-organic frameworks (MOFs) for the removal of heavy metals in aqueous solution55Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte143Application of machine learning in perovskite materials and devices: A review75MLMD: a programming-free AI platform to predict and design materials63Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations147Fungal systems for lignocellulose deconstruction: From enzymatic mechanisms to hydrolysis optimization60Recent advances in artificial intelligence boosting materials design for electrochemical energy storage124Machine learning in energy storage material discovery and performance prediction77Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends56Machine learning-accelerated discovery and design of electrode materials and electrolytes for lithium ion batteries93Machine learning in materials research: Developments over the last decade and challenges for the future105Quantifying the use and potential benefits of artificial intelligence in scientific research78Utilizing Machine Learning to Advance Battery Materials Design: Challenges and Prospects51Exploiting chemical bonding principles to design high-performance thermoelectric materials58AI‐Driven TENGs for Self‐Powered Smart Sensors and Intelligent Devices80Understanding solid-state battery electrolytes using atomistic modelling and machine learning107

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