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RESEARCH PAPER · Angewandte Chemie International Edition · 2021

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

Xiang Chen, Xinyan Liu, Xin Pu Shen, Qiang Zhang

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Applying Machine Learning to Rechargeable Batteries: From the Microscale to the MacroscaleOriginalComputational design of double-layer cathode coatings in all-solid-state batteries11Machine learning 3D-resolved prediction of electrolyte infiltration in battery porous electrodes53Combined density functional theory/kinetic Monte Carlo investigation of surface morphology during cycling of Li-Cu electrodes6Computational Design of Double-Layer Cathode Coatings in All-Solid-State Batteries0Artificial intelligence driven in-silico discovery of novel organic lithium-ion battery cathodes73Machine Learning for Battery Research4Using machine learning to screen non-graphite carbon materials based on Na-ion storage properties60Solvent Degradation and Polymerization in the Li-Metal Battery: Organic-Phase Formation in Solid-Electrolyte Interphases42Liquid electrolyte: The nexus of practical lithium metal batteries596The chemical origin of temperature-dependent lithium-ion concerted diffusion in sulfide solid electrolyte Li10GeP2S1265Issues and Opportunities Facing Aqueous Mn 2+ /MnO 2 ‐based Batteries178Machine learning in energy storage materials106Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review93Data‐driven battery degradation prediction: Forecasting voltage‐capacity curves using one‐cycle data44Materials Data toward Machine Learning: Advances and Challenges62Applying Classical, Ab Initio , and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries562The Origin of Fast Lithium‐Ion Transport in the Inorganic Solid Electrolyte Interphase on Lithium Metal Anodes104Battery state-of-charge estimation amid dynamic usage with physics-informed deep learning278Systematic analysis of the impact of slurry coating on manufacture of Li-ion battery electrodes via explainable machine learning88Challenges and advances of organic electrode materials for sustainable secondary batteries110Artificial Intelligence‐Based Material Discovery for Clean Energy Future47Tailoring layered transition metal compounds for high-performance aqueous zinc-ion batteries84Review on lithium metal anodes towards high energy density batteries134Performance analysis of Na-ion batteries by machine learning59Machine learning for battery research110The dead lithium formation under mechano-electrochemical coupling in lithium metal batteries39Artificial intelligence in accelerating the development of air electrode materials for proton conducting solid oxide cells0Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials36Basic guidelines of first-principles calculations for suitable selection of electrochemical Li storage materials: a review38Constant-potential molecular dynamics simulation and its application in rechargeable batteries28Machine learning-inspired battery material innovation49Machine learning approach to understanding the ‘synergistic’ pseudocapacitive effects of heteroatom doped graphene27Accelerated design of electrodes for liquid metal battery by machine learning39Battery impedance spectrum prediction from partial charging voltage curve by machine learning71Advances in thermal‐related analysis techniques for solid‐state lithium batteries85Deep learning-based battery state of charge estimation: Enhancing estimation performance with unlabelled training samples83A review on battery technology for space application103Reshaping the material research paradigm of electrochemical energy storage and conversion by machine learning34Computational insights into the rational design of organic electrode materials for metal ion batteries18Overview on Theoretical Simulations of Lithium‐Ion Batteries and Their Application to Battery Separators160A data-driven method for extracting aging features to accurately predict the battery health186Small Molecules, Great Powers: Chemistry of Small Organo‐Chalcogenide Molecules in Rechargeable Li‐Sulfur Batteries23Flame retardant properties of metal hydroxide-based polymer composites: A machine learning approach45Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization87Machine Learning in Lithium‐Ion Battery Cell Production: A Comprehensive Mapping Study40Molecular dynamics for electrocatalysis: Mechanism explanation and performance prediction58Data-driven approaches enabling the screening and design of promising materials for energy storage batteries0Rethinking the Electrode Multiscale Microstructures: A Review on Structuring Strategies toward Battery Manufacturing Genome54Research Progress of Liquid Electrolytes for Lithium Metal Batteries at High Temperatures48Data-driven discovery and intelligent design of artificial hybrid interphase layer for stabilizing lithium-metal anode24Advances in data‐assisted high‐throughput computations for material design112Building Smarter Aqueous Batteries23Data-Driven Insight into the Reductive Stability of Ion–Solvent Complexes in Lithium Battery Electrolytes224Digital modeling-assisted mesoscale visualization lights up materials science from liquid- to solid-state batteries9Machine learning for beyond Li-ion batteries: Powering the research39A comparative study of data-driven battery capacity estimation based on partial charging curves44Physics-based battery SOC estimation methods: Recent advances and future perspectives202Opportunities for ionic liquid-based electrolytes in rechargeable lithium batteries22Engineering the spin configuration of electrocatalysts for electrochemical renewable conversions28New insights on designing the next-generation materials for electrochemical synthesis of reactive oxidative species towards efficient and scalable water treatment: A review and perspectives16Depth analysis of battery performance based on a data-driven approach10Navigating materials chemical space to discover new battery electrodes using machine learning26Impact of Formulation and Slurry Properties on Lithium‐ion Electrode Manufacturing40A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data32An Adaptive State of Health Estimation Method for Lithium-Ion Batteries Using Impedance-Based Health Indicators and Multilayer Perceptron0Data-driven search for promising intercalating ions and layered materials for metal-ion batteries7High-Throughput Screening of Ni-Rich Layered Cathode Materials for Sodium-Ion Batteries Based on Machine Learning0Na[Mn 0.36 Ni 0.44 Ti 0.15 Fe 0.05 ]O 2 predicted via machine learning for high energy Na-ion batteries15Toward Next-Generation Heterogeneous Catalysts: Empowering Surface Reactivity Prediction with Machine Learning24A self‐adaptive, data‐driven method to predict the cycling life of lithium‐ion batteries42Data‐Driven Online Prognosis of Rechargeable Batteries: Prospect and Perspective7A mini review: How to select electrolyte additives for better Zn anode electrochemistry?69Battery pack capacity estimation for electric vehicles based on enhanced machine learning and field data78Interpretable Machine Learning To Accelerate the Analysis of Doping Effect on Li/Ni Exchange in Ni-Rich Layered Oxide Cathodes19Cationic perturbation strategy to solve the information drought in material explainable machine learning9Electrolyte Solvation Chemistry for Stabilizing the Zn Anode via Functionalized Organic Agents22Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte143Decoding Electrochemical Processes of Lithium‐Ion Batteries by Classical Molecular Dynamics Simulations66State-of-charge estimation of lithium-ion battery: Joint long short-term memory network and adaptive extended Kalman filter online estimation algorithm80Exploring solvation structure and transport behavior for rational design of advanced electrolytes for next generation of lithium batteries15Critical Effects of Insoluble Additives in Liquid Electrolytes for Metal Batteries7Machine learning in energy storage material discovery and performance prediction77Bridging multimodal data and battery science with machine learning35Lithium-ion battery digitalization: Combining physics-based models and machine learning101The importance of precise and suitable descriptors in data‐driven approach to boost development of lithium batteries: A perspective12Current update and prospects in the development of conductive metal-organic framework electrodes for lithium-based batteries13Rational design of MXene-based vacancy-confined single-atom catalyst for efficient oxygen evolution reaction32A high-flash-point quasi-solid polymer electrolyte for stable nickel-rich lithium metal batteries4A novel transformer‐embedded lithium‐ion battery model for joint estimation of state‐of‐charge and state‐of‐health30Unveiling spin magnetic effect of TM doped SnS2 nanosheet with enhanced hydrogen evolution reaction17Machine Learning‐Assisted Design of Nitrogen‐Rich Covalent Triazine Frameworks Photocatalysts16Accelerating the design of catalysts for CO2 electroreduction to HCOOH: A data-driven DFT-ML screening of dual atom catalysts32Design principle of single-atom catalysts for sulfur reduction reaction–interplay between coordination patterns and transition metals5Data-driven analysis of battery formation reveals the role of electrode utilization in extending cycle life60Recognition of Molecular Structure of Phosphonium Salts from the Visual Appearance of Material with Deep Learning Can Reveal Subtle Homologs7Conversion-type cathode materials for high energy density solid-state lithium batteries21A Knowledge–Data Dual‐Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes18A Knowledge–Data Dual‐Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes54Enhanced battery life prediction with reduced data demand via semi-supervised representation learning40Interpretable Learning of Accelerated Aging in Lithium Metal Batteries47Machine learning descriptors for crystal materials: applications in Ni-rich layered cathode and lithium anode materials for high-energy-density lithium batteries20Interpretable Surrogate Learning for Electronic Material Generation11A perspective on the fundamental theory of nonaqueous electrolytes for rechargeable batteries11Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook31Cost-Aware Bayesian Optimization for Self-Driven Condition Screening of Flow Electrosynthesis15Understanding the activity origin of Pd-anchored single-atom alloy catalysts for NO-to-NH3 conversion by DFT studies and machine learning4Lithium Batteries Operating at Wide Temperatures: Opportunities and Challenges22Accelerating battery innovation: AI-powered molecular discovery58Advances in high-coulombic-efficiency lithium metal anodes under practical conditions in liquid electrolytes48Autonomous organic synthesis for redox flow batteries via flexible batch Bayesian optimization22From mold to Ah level pouch cell design: bipolar all-solid-state Li battery as an emerging configuration with very high energy density9Machine learning-assisted design of oxygen-containing inorganic coating materials on a separator for lithium metal anodes1Principles and trends in extreme fast charging lithium-ion batteries92RedCat, an automated discovery workflow for aqueous organic electrolytes4Screening of potential candidates for solid electrolyte interphase materials for lithium-ion batteries through a data-driven approach5Computational understanding and multiscale simulation of secondary batteries14A novel multivariable prognostic approach for PEMFC degradation and remaining useful life prediction using random forest and temporal convolutional network42TELL-Me: T time-series-decomposition-based ensembled lightweight learning model for diverse battery prognosis and diagnosis16Rapid Screening Strategy of 2D Materials for Li-Ion Battery (LIB) Electrode Based on Deep Neural Networks (DNN)12A Universal Solution for Direct Regeneration of Spent Lithium Iron Phosphate67A review of lithium–sulfur batteries at different working conditions: The role of ambient temperature, external force, and electromagnetic field34Utilizing Machine Learning to Advance Battery Materials Design: Challenges and Prospects51Correlating Self‐Discharge and Cycling Performance of Batteries to Fasten Electrolytes Development2Insights into the doping rules of heteroatom on Ni-rich ternary cathode stability by integrating high throughput calculation and machine learning13Leveraging machine learning for accelerated materials innovation in lithium-ion battery: A review54High-throughput screening of Ni-rich layered cathode materials for sodium-ion batteries based on machine learning9AI-driven accelerated discovery of intercalation-type cathode materials for magnesium batteries28Deep learning-driven detection of lithium-plating-type defects for battery manufacturing via formation and capacity grading data16Dual-objective optimization of lithium metal battery electrolytes via machine learning9High quality large‐scale nickel‐rich layered oxides precursor co‐precipitation via domain adaptation‐based machine learning11Predicting ash accumulation in industrial systems using machine learning: Enhancing maintenance and operational efficiency4Machine Learning-based Battery Life Detection and Photoelectrode Materials Selection for Lithium Batteries4Data‐Driven Lithium Salt Design for Long‐Cycle Lithium Metal Battery3Localized feature selection augmented dual-stream fusion network for state of health estimation of lithium-ion batteries10The evolution of low-temperature lithium metal batteries: Materials, mechanisms, and applications4Cognition-enhanced instruction framework: Accelerating structured battery knowledge extraction with low-parameter models1Large language models for batteries31SOH estimation for lithium-ion batteries using the distribution of relaxation time and feature optimized multilayer perceptron2Low-Temperature Electrolytes for Lithium-Ion Batteries: Current Challenges, Development, and Perspectives44Nanogold Empowered Energy: Oxygen Reduction and Oxygen Evolution Reactions Boost via Co Nanoclusters and NiFe‐LDH Modulation2AI-augmented electrochemical model for lithium-ion battery: Recent advances and perspectives24Atomic-scale tracking of sodium metal-electrolyte reactions via adaptive machine learning force fields7Effects of Key Factors on Lithium Dendrite Dissolution and Dead Lithium Formation: A Phase-Field Simulation Study3Predictive modelling of slurry viscosity using transfer learning to mitigate uncertainties in pilot-scale lithium-ion battery manufacturing process2Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches2Pre-sodiation strategies for high reversibility of hard carbon anodes: A review6Surface Modification of Lithium Metal as an Anode in Lithium Metal‐Based Batteries14Machine learning-guided deep eutectic electrolytes for high energy density and safe lithium–air batteries3Coupling Data‐Driven and Reinforcement Learning for Material Development and Device Management in Batteries4Machine-learning interatomic potentials for interfaces in all-solid-state batteries: Perspectives on training data, model selection, and validation4

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