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RESEARCH PAPER · Chemical Reviews · 2021

Artificial Intelligence Applied to Battery Research: Hype or Reality?

Teo Lombardo, Marc Duquesnoy, Hassna El-Bouysidy, Fabian Årén, A. Gallo‐Bueno, Peter Bjørn Jørgensen, Arghya Bhowmik, Arnaud Demortière, Elixabete Ayerbe, Francisco Alcaide, Marine Reynaud, Javier Carrasco, Alexis Grimaud, Chao Zhang, Tejs Vegge, Patrik Johansson, Alejandro A. Franco

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42 direct citing works

Artificial Intelligence Applied to Battery Research: Hype or Reality?OriginalQuantifying key factors for optimised manufacturing of Li-ion battery anode and cathode via artificial intelligence79Review—Reference Electrodes in Li-Ion and Next Generation Batteries: Correct Potential Assessment, Applications and Practices64Towards autonomous high-throughput multiscale modelling of battery interfaces33Nitrogen reduction reaction energy and pathways in metal-zeolites: deep learning and explainable machine learning with local acidity and hydrogen bonding features35Liquid electrolyte: The nexus of practical lithium metal batteries596Deconvoluting the benefits of porosity distribution in layered electrodes on the electrochemical performance of Li-ion batteries110Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images63Data-driven prediction of battery failure for electric vehicles91Machine learning in energy storage materials106Towards unified machine learning characterization of lithium-ion battery degradation across multiple levels: A critical review93Theory-guided experimental design in battery materials research156Applying Classical, Ab Initio , and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries562Data‐Driven Analysis of High‐Throughput Experiments on Liquid Battery Electrolyte Formulations: Unraveling the Impact of Composition on Conductivity**29Prospects for managing end‐of‐life lithium‐ion batteries: Present and future137Systematic analysis of the impact of slurry coating on manufacture of Li-ion battery electrodes via explainable machine learning88Unity of Opposites between Soluble and Insoluble Lithium Polysulfides in Lithium–Sulfur Batteries138Chemomechanics of Rechargeable Batteries: Status, Theories, and Perspectives228Data Specifications for Battery Manufacturing Digitalization: Current Status, Challenges, and Opportunities67Scaling up high-energy-density sulfidic solid-state batteries: A lab-to-pilot perspective251Artificial Intelligence‐Based Material Discovery for Clean Energy Future47Machine Learning Predicts the X-ray Photoelectron Spectroscopy of the Solid Electrolyte Interface of Lithium Metal Battery61Digital Technology Implementation in Battery-Management Systems for Sustainable Energy Storage: Review, Challenges, and Recommendations102Roadmap on Li-ion battery manufacturing research76Li-ion battery degradation modes diagnosis via Convolutional Neural Networks79Machine learning for investigating the relative importance of electrodes’ N:P areal capacity ratio in the manufacturing of lithium-ion battery cells35Electric Vehicle Batteries: Status and Perspectives of Data-Driven Diagnosis and Prognosis79Untangling Degradation Chemistries of Lithium‐Sulfur Batteries Through Interpretable Hybrid Machine Learning56Comprehensive recycling of lithium-ion batteries: Fundamentals, pretreatment, and perspectives248The Application of Artificial Intelligence in the Effective Battery Life Cycle in the Closed Circular Economy Model—A Perspective39An adaptive framework to accelerate optimization of high flame retardant composites using machine learning41Machine-Learning-Driven Advanced Characterization of Battery Electrodes76Lithium ion battery electrode manufacturing model accounting for 3D realistic shapes of active material particles58Artificial intelligence driven hydrogen and battery technologies – A review201Modified cathode-electrolyte interphase toward high-performance batteries84A review on methods for state of health forecasting of lithium-ion batteries applicable in real-world operational conditions87Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations119Porous Flow Field for Next-Generation Proton Exchange Membrane Fuel Cells: Materials, Characterization, Design, and Challenges274Automated electrolyte formulation and coin cell assembly for high-throughput lithium-ion battery research35Machine learning-inspired battery material innovation49Onboard Energy Storage Systems for Railway: Present and Trends65Accelerated design of electrodes for liquid metal battery by machine learning39Novel Method of Characterization of Heart Disease Prediction Using Sequential Feature Selection-Based Ensemble Technique39A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning100High-efficient prediction of state of health for lithium-ion battery based on AC impedance feature tuned with Gaussian process regression89Advances in thermal‐related analysis techniques for solid‐state lithium batteries85Overview on Theoretical Simulations of Lithium‐Ion Batteries and Their Application to Battery Separators160Electrostatic Potential as Solvent Descriptor to Enable Rational Electrolyte Design for Lithium Batteries270On the Hyping of Scholarly Research (with a Shout-Out to ChatGPT)39Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization87Machine Learning in Lithium‐Ion Battery Cell Production: A Comprehensive Mapping Study40Deep learning to estimate lithium-ion battery state of health without additional degradation experiments403Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use37Machine learning promotes the development of all-solid-state batteries35Molecular dynamics for electrocatalysis: Mechanism explanation and performance prediction58Battery State of Health Estimate Strategies: From Data Analysis to End-Cloud Collaborative Framework58Optimization techniques for electrochemical devices for hydrogen production and energy storage applications46Electric vehicle battery capacity degradation and health estimation using machine-learning techniques: a review69Lithium–Ion Battery Data: From Production to Prediction42Oxide Ion-Conducting Materials Containing Tetrahedral Moieties: Structures and Conduction Mechanisms94MOF-derived porous NiCo2O4 nanofile arrays as an efficient anode material for rechargeable Li-ion batteries36Battery prognostics and health management from a machine learning perspective136Accurate Model Parameter Identification to Boost Precise Aging Prediction of Lithium‐Ion Batteries: A Review64A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation57Safety Performance and Failure Criteria of Lithium-Ion Batteries under Mechanical Abuse32State of charge estimation combining physics-based and artificial intelligence models for Lithium-ion batteries36A dynamic database of solid-state electrolyte (DDSE) picturing all-solid-state batteries61Data-Driven Insight into the Reductive Stability of Ion–Solvent Complexes in Lithium Battery Electrolytes224Thermal state monitoring of lithium-ion batteries: Progress, challenges, and opportunities273Minireview on Porous Media and Microstructure Reconstruction Using Machine Learning Techniques: Recent Advances and Outlook432023 Roadmap on molecular modelling of electrochemical energy materials40Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method135Identification of Lithium Compounds on Surfaces of Lithium Metal Anode with Machine-Learning-Assisted Analysis of ToF-SIMS Spectra38A Review of Transition Metal Boride, Carbide, Pnictide, and Chalcogenide Water Oxidation Electrocatalysts295Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion battery32Machine learning in metal-ion battery research: Advancing material prediction, characterization, and status evaluation49Mxenes for Zn-based energy storage devices: Nano-engineering and machine learning31Impact of Formulation and Slurry Properties on Lithium‐ion Electrode Manufacturing40Advancing next-generation proton-exchange membrane fuel cell development in multi-physics transfer204Machine learning for expediting next-generation of fire-retardant polymer composites43Aspects of artificial intelligence in future electric vehicle technology for sustainable environmental impact63Electrochemical coupling in subnanometer pores/channels for rechargeable batteries48Microstructures of layered Ni-rich cathodes for lithium-ion batteries189Rechargeable alkali metal–chlorine batteries: advances, challenges, and future perspectives57Sixty years of electrochemical optical spectroscopy: a retrospective40Building Stable Anodes for High‐Rate Na‐Metal Batteries135Electrolyte and interface engineering for solid-state sodium batteries54Battery safety: Machine learning-based prognostics211Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects128Aging datasets of commercial lithium-ion batteries: A review38Smart batteries for powering the future141Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte143A CNN-SAM-LSTM hybrid neural network for multi-state estimation of lithium-ion batteries under dynamical operating conditions151Materials descriptors of machine learning to boost development of lithium-ion batteries32Unveiling Confinement Engineering for Achieving High‐Performance Rechargeable Batteries35Decoding Electrochemical Processes of Lithium‐Ion Batteries by Classical Molecular Dynamics Simulations66Graphic, Quantitation, Visualization, Standardization, Digitization, and Intelligence of Electrolyte and Electrolyte‐Electrode Interface102Interpretable machine learning prediction for li-ion battery's state of health based on electrochemical impedance spectroscopy and temporal features53Lithium-ion battery digitalization: Combining physics-based models and machine learning101Optimizing lithium-ion battery electrode manufacturing: Advances and prospects in process simulation46Flow field structure design for redox flow battery: Developments and Prospects30Human inventions and its environmental challenges, especially artificial intelligence: New challenges require new thinking44The Rise of Multivalent Metal–Sulfur Batteries: Advances, Challenges, and Opportunities46Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends562024 roadmap for sustainable batteries34FullProfAPP : a graphical user interface for the streamlined automation of powder diffraction data analysis58Machine learning-accelerated discovery and design of electrode materials and electrolytes for lithium ion batteries93Self-Driving Laboratories for Chemistry and Materials Science674Optimal Electric Vehicle Battery Management Using Q-learning for Sustainability32Anion-derived contact ion pairing as a unifying principle for electrolyte design67Artificial intelligence-driven real-world battery diagnostics100Empowering lithium-ion battery manufacturing with big data: Current status, challenges, and future37Synergizing physics and machine learning for advanced battery management66A Knowledge–Data Dual‐Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes54Artificial intelligence in rechargeable battery: Advancements and prospects37Interpretable Learning of Accelerated Aging in Lithium Metal Batteries47Electro‐Chemo‐Mechanical Degradation in Solid‐State Batteries: A Review of Microscale and Multiphysics Modeling59Layered double hydroxide-based nanomaterials for supercapacitors and batteries: Strategies and mechanisms144Recent Advancements in Artificial Intelligence in Battery Recycling53Research progress in sodium-iron-phosphate-based cathode materials for cost-effective sodium-ion batteries: Crystal structure, preparation, challenges, strategies, and developments47A comprehensive understanding on the anionic redox chemistry of high-voltage cathode materials for high-energy-density lithium-ion batteries115A roadmap for ammonia synthesis via electrocatalytic reduction of nitric oxide37Accelerating battery innovation: AI-powered molecular discovery58Amorphous material based heterostructures with disordered heterointerfaces for advanced rechargeable batteries31Integrating Machine Learning and Characterization in Battery Research: Toward Cognitive Digital Twins with Physics and Knowledge30Advanced electrode processing for lithium-ion battery manufacturing135Molecular Engineering of Organic Electrode Materials for Beyond Lithium‐Ion Batteries36High-Volume Battery Recycling: Technical Review of Challenges and Future Directions48Utilizing Machine Learning to Advance Battery Materials Design: Challenges and Prospects51A review of functional group selection and design strategies for gel polymer electrolytes for metal batteries61Leveraging machine learning for accelerated materials innovation in lithium-ion battery: A review54RETRACTED: Comparative analysis of hydrogen production methods: Environmental impact and efficiency of electrochemical and thermochemical processes37A review of silicon-carbon anode materials: The role of precursor and its effect on lithium-ion battery performance37Molecular Regulation and Intermolecular Chemistry in Gel Polymer Electrolytes for High‐Voltage Lithium Batteries46Chemical perspectives on synthesis, functionalization, artificial intelligence, and energy storage applications of layered double hydroxides-based nanomaterials: A comprehensive review31Uni‐Electrolyte: An Artificial Intelligence Platform for Designing Electrolyte Molecules for Rechargeable Batteries33From Mining to Manufacturing: Scientific Challenges and Opportunities behind Battery Production100FIB-SEM: Emerging Multimodal/Multiscale Characterization Techniques for Advanced Battery Development51Sensors Innovations for Smart Lithium-Based Batteries: Advancements, Opportunities, and Potential Challenges62Electrochemical Energy Storage toward Extreme Conditions: Driving Human Exploration beyond Current Boundaries75Next-generation battery safety management: Machine learning assisted life-time prediction and performance enhancement43Three‐Dimensional Graphene Aerogel Materials for Supercapacitors: Strategies and Mechanisms36Battery technologies for grid-scale energy storage250Understanding solid-state battery electrolytes using atomistic modelling and machine learning107Artificial Intelligence‐Driven Development in Rechargeable Battery Materials: Progress, Challenges, and Future Perspectives53Cutting‐Edge Developments at the Interface of Inorganic Solid‐State Electrolytes38Prior-knowledge-driven machine learning modeling for electro-chemo-mechanical failure of solid-state electrolyte35Materials informatics: A review of AI and machine learning tools, platforms, data repositories, and applications to architectured porous materials41A comprehensive review of remaining useful life prediction methods for lithium-ion batteries: Models, trends, and engineering applications44Low-Temperature Electrolytes for Lithium-Ion Batteries: Current Challenges, Development, and Perspectives44Extremely Fast-Charging Batteries: Principle, Strategies, Detection, and Prediction64

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