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RESEARCH PAPER · InfoMat · 2020

Machine learning: Accelerating materials development for energy storage and conversion

An Chen, Xu Zhang, Zhen Ping Zhou

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Machine learning: Accelerating materials development for energy storage and conversionOriginalAn aromatic carbonyl compound-linked conjugated microporous polymer as an advanced cathode material for lithium-organic batteries53Effect of pore structure and doping species on charge storage mechanisms in porous carbon-based supercapacitors218Electrocatalyst design for aprotic Li–CO 2 batteries115In silico investigation of Cu(In,Ga)Se 2 -based solar cells3CHAIN: Cyber Hierarchy and Interactional Network Enabling Digital Solution for Battery Full-Lifespan Management156Lithium lanthanum titanate perovskite as an anode for lithium ion batteries235Gallium-based anodes for alkali metal ion batteries44Layered Oxide Cathode for Potassium‐Ion Battery: Recent Progress and Prospective99Machine Learning Lattice Constants for Cubic Perovskite Compounds53A Machine Learning Model on Simple Features for CO2 Reduction Electrocatalysts207Vanadate-based electrodes for rechargeable batteries28Preparation of Ni3Fe2@NC/CC Integrated Electrode and Its Application in Zinc-Air Battery9“More is Different:” Synergistic Effect and Structural Engineering in Double‐Atom Catalysts349Data-driven machine learning model for the prediction of oxygen vacancy formation energy of metal oxide materials31First-principles materials simulation and design for alkali and alkaline metal ion batteries accelerated by machine learning7Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles40Prediction of band gap for 2D hybrid organic–inorganic perovskites by using machine learning through molecular graphics descriptors24Recent advances in ferromagnetic metal sulfides and selenides as anodes for sodium- and potassium-ion batteries126Understanding the role of axial O in CO 2 electroreduction on NiN 4 single-atom catalysts via simulations in realistic electrochemical environment88Unravelling the origin of bifunctional OER/ORR activity for single-atom catalysts supported on C 2 N by DFT and machine learning191Waste Heat Harvesting: Descriptor of Thermogalvanic Cell0Machine learning for advanced energy materials208Ultralong-Life Supercapacitors Using Pyridine-Derived Porous Carbon Materials37Predicting metal-organic frameworks as catalysts to fix carbon dioxide to cyclic carbonate by machine learning512D Materials Bridging Experiments and Computations for Electro/Photocatalysis216Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks201Micronanostructured Design of Dendrite‐Free Zinc Anodes and Their Applications in Aqueous Zinc‐Based Rechargeable Batteries115Computational insights into modulating the performance of MXene based electrode materials for rechargeable batteries49Molybdenum‐based materials for sodium‐ion batteries105Visual Analysis on Machine Learning Assisted Prediction of Ionic Conductivity for Solid-State Electrolytes3Layered P3-Type K0.4Fe0.1Mn0.8Ti0.1O2 as a Low-Cost and Zero-Strain Electrode Material for both Potassium and Sodium Storage51Computational Discovery of New 2D Materials Using Deep Learning Generative Models84Automatic Migration Path Exploration for Multivalent Battery Cathodes using Geometrical Descriptors21JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics85Machine Learning Screening of Metal-Ion Battery Electrode Materials116Polymer‐/Ceramic‐based Dielectric Composites for Energy Storage and Conversion201Machine Learning Investigation of Supplementary Adsorbate Influence on Copper for Enhanced Electrochemical CO2 Reduction Performance36High temperature oxidation of corrosion resistant alloys from machine learning99The role of artificial intelligence in the mass adoption of electric vehicles172Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers61Deep Reinforcement Learning for Digital Materials Design106Modulating electronic structure of CoSe 2 by Ni doping for efficient electrocatalyst for hydrogen evolution reaction45Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries412An emerging machine learning strategy for the assisted‐design of high-performance supercapacitor materials by mining the relationship between capacitance and structural features of porous carbon74Synthesis and Electronic Modulation of Nanostructured Layered Double Hydroxides for Efficient Electrochemical Oxygen Evolution37First-principles computational insights into lithium battery cathode materials63matExplorer: Visual Exploration on Predicting Ionic Conductivity for Solid-state Electrolytes8Machine Learning in Screening High Performance Electrocatalysts for CO2 Reduction110Research on Optimal Scheduling Strategy for Multi-energy Interconnection1Cross-Platform Classifier of Chemical Stability for Charged Redoxmers3High‐Entropy Energy Materials in the Age of Big Data: A Critical Guide to Next‐Generation Synthesis and Applications107Review of machine learning‐driven design of polymer‐based dielectrics50Computational modeling of green hydrogen generation from photocatalytic H2S splitting: Overview and perspectives40Multiscale Graph Attention Neural Networks for Mapping Materials and Molecules beyond Short-Range Interatomic Correlations0Screening of potential double perovskite materials for photovoltaic applications using agglomerative hierarchical clustering1Targeted design of advanced electrocatalysts by machine learning110Δ-Machine learning-driven discovery of double hybrid organic–inorganic perovskites31Digital Microscale Electrochemical Energy Storage Devices for a Fully Connected and Intelligent World46Accelerating colloidal quantum dot innovation with algorithms and automation30Building up the “Genome” of bi-atom catalysts toward efficient HER/OER/ORR80Functional Nanomaterials Design in the Workflow of Building Machine-Learning Models1High-throughput assessment of two-dimensional electrode materials for energy storage devices21Influence of Molecular Layered Structure on Thermoelectric Properties of Materials0Machine learning enabled high-throughput screening of inorganic solid electrolytes for regulating dendritic growth in lithium metal anodes19Mutual modulation via charge transfer and unpaired electrons of catalytic sites for the superior intrinsic activity of N 2 reduction: from high-throughput computation assisted with a machine learning perspective47Optimization of Chemical Synthesis with Heuristic Algorithms4Using machine learning to screen non-graphite carbon materials based on Na-ion storage properties60Toward Excellence of Electrocatalyst Design by Emerging Descriptor‐Oriented Machine Learning127Machine Learning Approach for Maximizing Thermoelectric Properties of BiCuSeO and Discovering New Doping Element15Performance Predictors for Organic Cathodes of Lithium-Ion Battery23Reviewing machine learning of corrosion prediction in a data-oriented perspective250Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State Electrolyte33Global instability index as a crystallographic stability descriptor of halide and chalcogenide perovskites43Perspective on Theoretical Models for CO2 Electrochemical Reduction67Learning Matter: Materials Design with Machine Learning and Atomistic Simulations112Evaluation of performance of machine learning methods in mining structure–property data of halide perovskite materials21Inverse Design of Nanoclusters for Light-Controlled CO2–HCOOH Interconversion8Accelerating the discovery of high-performance donor/acceptor pairs in photovoltaic materials via machine learning and density functional theory23Emerging Trends in Sustainable CO 2 ‐Management Materials151Fast Remaining Capacity Estimation for Lithium‐ion Batteries Based on Short‐time Pulse Test and Gaussian Process Regression33Intelligent disassembly of electric-vehicle batteries: a forward-looking overview172Machine learning in energy storage materials106Multi-dimensional characteristic construction methods of computational materials under big data environment1Catalytic Hydrogenation of CO2 to Methanol: A Review171Vision for energy material design: A roadmap for integrated data-driven modeling27MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art70N/P/O doped porous carbon materials for supercapacitor with high performance36Data‐driven battery degradation prediction: Forecasting voltage‐capacity curves using one‐cycle data44Data‐Driven Materials Innovation and Applications187Theory-guided experimental design in battery materials research156A facile and environmentally friendly method for preparing supercapacitor electrode carbon-based materials with ultra-long cycling stability6B/N/O/Zn doped porous carbon materials for supercapacitor with high performance6Layered Perovskite Lithium Yttrium Titanate as a Low‐Potential and Ultrahigh‐Rate Anode for Lithium‐Ion Batteries46Bridging Multiscale Characterization Technologies and Digital Modeling to Evaluate Lithium Battery Full Lifecycle93Metal-organic frameworks marry carbon: Booster for electrochemical energy storage36Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis18Atomic‐Level Design of Active Site on Two‐Dimensional MoS 2 toward Efficient Hydrogen Evolution: Experiment, Theory, and Artificial Intelligence Modelling99Advanced Cathode Materials for Protonic Ceramic Fuel Cells: Recent Progress and Future Perspectives183High-Throughput Computational Screening and Machine Learning Modeling of Janus 2D III–VI van der Waals Heterostructures for Solar Energy Applications72Machine Learning: A New Paradigm in Computational Electrocatalysis113Accelerating the discovery of battery electrode materials through data mining and deep learning models30Battery Materials Discovery and Smart Grid Management using Machine Learning8Tuned layered double hydroxide-based catalysts inducing singlet oxygen evolution: Reactive oxygen species evolution mechanism exploration, norfloxacin degradation and catalysts screen based on machine learning51Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects24A Universal Machine Learning Framework for Electrocatalyst Innovation: A Case Study of Discovering Alloys for Hydrogen Evolution Reaction112MetaNOR: A meta-learnt nonlocal operator regression approach for metamaterial modeling6Machine‐Learning‐Accelerated Development of Efficient Mixed Protonic–Electronic Conducting Oxides as the Air Electrodes for Protonic Ceramic Cells70Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities50Transition metal Dual–Atom Ni2/TiO2 catalysts for photoelectrocatalytic hydrogen Evolution: A density functional theory study22Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives113Electrochemical hydroxidation of sulfide for preparing sulfur-doped NiFe (oxy) hydroxide towards efficient oxygen evolution reaction34In Situ/Operando Characterization Techniques: The Guiding Tool for the Development of Li–CO2 Battery40Electrochemoinformatics as an Emerging Scientific Field for Designing Materials and Electrochemical Energy Storage and Conversion Devices—An Application in Battery Science and Technology24Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics141Crystal twins: self-supervised learning for crystalline material property prediction72Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review119In situ/operando regulation of the reaction activities on hetero-structured electrodes for solid oxide cells75Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores37Data-driven design of carbon-based materials for high-performance flexible energy storage devices25Spin-State Regulation of Nickel Cobalt Spinel toward Enhancing the Electron Transfer Process of Oxygen Redox Reactions in Lithium–Oxygen Batteries13A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite A 2 B + B 3+ X 615A Novel Real Time Electric Vehicles Smart Charging Approach Based on Artificial Intelligence3Analysis of the oxygen evolution activity of layered double hydroxides (LDHs) using machine learning guidance16Electrochemical reduction of carbon dioxide to multicarbon (C 2+ ) products: challenges and perspectives458Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models4Machine learning in energy chemistry: introduction, challenges and perspectives31Machine learning-inspired battery material innovation49Microkinetic studies for mechanism interpretation in electrocatalytic CO and CO 2 reduction: current and perspective19Recent Advance of Machine Learning in Selecting New Materials7Recent advances in perovskite oxide electrocatalysts for Li–O 2 batteries26The Application of Novel Functional Materials to Machine Learning0Towards interactional management for power batteries of electric vehicles12Machine learning approach to understanding the ‘synergistic’ pseudocapacitive effects of heteroatom doped graphene27A Machine Learning Method for Predicting Corrosion Weight Gain of Uranium and Uranium Alloys9Perspective on machine learning in energy material discovery21High-efficient prediction of state of health for lithium-ion battery based on AC impedance feature tuned with Gaussian process regression89Advanced ocean wave energy harvesting: current progress and future trends54NaNO3-KNO3-KCl/K2CO3 with the elevated working temperature for CSP application: Phase diagram calculation and machine learning31Reshaping the material research paradigm of electrochemical energy storage and conversion by machine learning34Overview on Theoretical Simulations of Lithium‐Ion Batteries and Their Application to Battery Separators160Machine learning assisted screening of MXenes pseudocapacitive materials36A Survey on Energy Storage: Techniques and Challenges52The Creativity of Teachers Using Online Learning Media in Pandemic Covid-194Current Status and Enhancement Strategies for All-Solid-State Lithium Batteries103Data‐driven structural descriptor for predicting platinum‐based alloys as oxygen reduction electrocatalysts39A disquisition on CO2 electroreduction to C2H4: An engineering and design perspective looking beyond novel choosy catalyst materials54Simple physical model with empirical formulas for solid-state sintering of CaCO3 for estimation of porosity5Learning from mistakes: Sampling strategies to efficiently train machine learning models for material property prediction21Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization87An XGBoost Algorithm Based on Molecular Structure and Molecular Specificity Parameters for Predicting Gas Adsorption31

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