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RESEARCH PAPER · npj Computational Materials · 2022

A review of the recent progress in battery informatics

Chen Ling

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A review of the recent progress in battery informaticsOriginalCapacity prediction of K-ion batteries: a machine learning based approach for high throughput screening of electrode materials30Data-driven selection of electrolyte additives for aqueous magnesium batteries21Na superionic conductor-type LiZr 2 (PO 4 ) 3 as a promising solid electrolyte for use in all-solid-state Li metal batteries19Process optimisation for NASICON-type solid electrolyte synthesis using a combination of experiments and bayesian optimisation31Informing geometric deep learning with electronic interactions to accelerate quantum chemistry100Descriptor-Free Design of Multicomponent Catalysts27Prognosis and Health Management (PHM) of Solid-State Batteries: Perspectives, Challenges, and Opportunities36Advances and challenges in multiscale characterizations and analyses for battery materials10An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range110Design of Biocompatible Monolayers and Polymeric Materials using Informatics0Electrochemoinformatics as an Emerging Scientific Field for Designing Materials and Electrochemical Energy Storage and Conversion Devices—An Application in Battery Science and Technology24Predicting the state parameters of lithium ion batteries: the race between filter-based and data driven approaches22MatFlow: A System for Knowledge-based Novel Materials Design using Machine Learning5Computational and Data-Driven Materials Engineering: High-Throughput Computational Screening Platforms, Workflows, and Discovery Outcomes0Computational and data-driven modelling of solid polymer electrolytes21Li 2 ZnTi 3 O 8 anode: design from material to electrode and devices8Machine learning-inspired battery material innovation49Realizing the cooking recipe of materials synthesis through large language models14Solid State Ionics 2011–2021: Trends and Prospects1Solid State Ionics 2011–2021: Trends and Prospects0Synergistic theoretical and experimental study on the ion dynamics of bis(trifluoromethanesulfonyl)imide-based alkali metal salts for solid polymer electrolytes33A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning100LIBAC: An Annotated Corpus for Automated “Reading” of the Lithium-Ion Battery Research Literature10The value of negative results in data-driven catalysis research72RETRACTED ARTICLE: Fog robotics-based intelligence transportation system using line-of-sight intelligent transportation28Unconstrained Machine Learning Screening for New Li‐Ion Cathode Materials Enhanced by Class Balancing9Machine Learning in Lithium‐Ion Battery Cell Production: A Comprehensive Mapping Study40Data-Driven Prognosis of Multiscale and Multiphysics Complex System Anomalies: Its Application to Lithium-ion Batteries Failure Detection31Structural Transformation of Hydrated WO3 into SnWO4 via Sn Incorporation Enables a Superior Pseudocapacitor and Aqueous Zinc-Ion Battery21Ageing-aware battery discharge prediction with deep learning38NIMS-OS: an automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science23Growth of TiS2 nanoflakes using CVD approach for sodium-ion battery application8Lithium–Ion Battery Data: From Production to Prediction42How to Develop Useful Models for Solid‐State Batteries – A Plea for Simplicity and Interdisciplinary Cooperation7Metal-solvent interaction contribution on voltage for metal ion battery: An interpretable machine learning approach10Building Smarter Aqueous Batteries23Detecting electrochemical changes in a nickel–zinc battery by operando x‐ray computed‐tomography analysis during charge–discharge tests1A dynamic database of solid-state electrolyte (DDSE) picturing all-solid-state batteries61Artificial intelligence-driven rechargeable batteries in multiple fields of development and application towards energy storage34Minireview on Porous Media and Microstructure Reconstruction Using Machine Learning Techniques: Recent Advances and Outlook43MatGPT: A Vane of Materials Informatics from Past, Present, to Future74Materials Informatics for Thermistor Properties of Mn–Co–Ni Oxides14New insights on designing the next-generation materials for electrochemical synthesis of reactive oxidative species towards efficient and scalable water treatment: A review and perspectives16Influence of amorphous intermediate domains on the performance of Na2O–Bi2O3–GeO2 glass–ceramic anode material system2A Review of Transition Metal Boride, Carbide, Pnictide, and Chalcogenide Water Oxidation Electrocatalysts295Machine learning in metal-ion battery research: Advancing material prediction, characterization, and status evaluation49Human–Machine Collaboration for Accelerated Discovery of Promising Oxygen Evolution Electrocatalysts with On-Demand Elements30Comprehending garnet solid electrolytes and interfaces in all-solid lithium-ion batteries12Toward Knowledge Engineering Using MatFlow for Inverse Quantum Dye Design2OPTIMIZATION OF WEDM PARAMETERS USING MACHINE LEARNING: A COMPARATIVE ANALYSIS OF SELECTED REGRESSION MODELS0Crystallography of Active Particles Defining Battery Electrochemistry8Autonomous millimeter scale high throughput battery research system12Computer vision enabled high-quality electrochemical experimentation3Design of Biomaterials Using Informatics2Expediting Ionic Conductivity Prediction of Solid-State Battery Electrodes Using Machine Learning5Integrated electro- and chemical characterization of sulfide-based solid-state electrolytes22Nanoscale Modelling of Substitutional Disorder in Battery Materials3Rational Design of Battery Materials Through Spectroscopic Characterization and Computational Modeling of Redox Orbitals0Rational electrolyte design for Li-metal batteries operated under extreme conditions: a combined DFT, COSMO-RS, and machine learning study22Unravelling air/moisture stability of cathode materials in sodium ion batteries: characterization, rational design, and perspectives159XRDMatch: a semi-supervised learning framework to efficiently discover room temperature lithium superionic conductors12Size dependent lithium-ion conductivity of solid electrolytes in machine learning molecular dynamics simulations21Bi-LSTM, GRU and 1D-CNN models for short-term photovoltaic panel efficiency forecasting case amorphous silicon grid-connected PV system76Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning22Expanding the Applicability Domain of Machine Learning Model for Advancements in Electrochemical Material Discovery7Designing the next generation of symmetrical organic redox flow batteries using helical carbocations6Recent advances in functional materials and devices for Zn-Ion hybrid supercapacitors97A document-level information extraction pipeline for layered cathode materials for sodium-ion batteries16State-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 batteries15ESM Cloud Toolkit: A Copilot for Energy Storage Material Research2End-to-End High-Throughput Approach for Data-Driven Internal Donor Development in Heterogeneous Ziegler–Natta Propylene Polymerization18Deep machine learning approaches for battery health monitoring41Attention towards chemistry agnostic and explainable battery lifetime prediction24Machine learning in energy storage material discovery and performance prediction77A guide to discovering next-generation semiconductor materials using atomistic simulations and machine learning14State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges184Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review38Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery29Investigating the influence of oxygen-functionalized graphene nanosheets as an efficient multifunctional material for supercapacitor and electrocatalytic water splitting applications10Machine learning for full lifecycle management of lithium-ion batteries43Addressing the Accuracy-Cost Trade-off in Material Property Prediction Using a Teacher-Student Strategy5Tuning Excitonic Properties of Monochalcogenides via Design of Janus Structures8Versatile Deep Learning Pipeline for Transferable Chemical Data Extraction7Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends56Experimental Exploration of Cellulose Material for Battery Separators and Artificial Neural Network-Driven Predictive Modeling for Enhanced Thermal Safety in Electric Vehicles5Unsupervised learning of charge-discharge cycles from various lithium-ion battery cells to visualize dataset characteristics and to interpret model performance11Self-Driving Laboratories for Chemistry and Materials Science674Fostering Li-ion conduction in Zr-Sn-Al-based mid-entropy NASICON electrolyte7The ARTISTIC Battery Manufacturing Digitalization Initiative: From Fundamental Research to Industrialization24Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development10Toward high performance all-solid-state lithium or sodium metal batteries: Potential application on Li/Na-rich antiperovskites (LiRAPs/NaRAPs) electrolyte for energy storage13A universal strategy of multi-objective active learning to accelerate the discovery of organic electrode molecules6Data refinement for enhanced ionic conductivity prediction in garnet-type solid-state electrolytes12A comprehensive review, perspectives and future directions of battery characterization and parameter estimation16Machine learning to support prospective life cycle assessment of emerging chemical technologies19A synthetic data generation method and evolutionary transformer model for degradation trajectory prediction in lithium-ion batteries37Machine-Learning-Assisted Materials Discovery from Electronic Band Structure7Machine Learning-Assisted Bayesian Optimization for the Discovery of Effective Additives for Dendrite Suppression in Lithium Metal Batteries17A comparative analysis of the influence of data-processing on battery health prediction by two machine learning algorithms10Machine learning in advancing anode materials for Lithium-Ion batteries – A review17Perturbation-Based Battery Impedance Characterization Methods: From the Laboratory to Practical Implementation18Property Prediction for Complex Compounds Using Structure-Free Mendeleev Encoding and Machine Learning9A two-stage method with twin autoencoders for the degradation trajectories prediction of lithium-ion batteries7Research progress in sodium-iron-phosphate-based cathode materials for cost-effective sodium-ion batteries: Crystal structure, preparation, challenges, strategies, and developments47Disentangling multifactorial impacts on cathode thermochemical properties with explainable machine learning9Screening of potential candidates for solid electrolyte interphase materials for lithium-ion batteries through a data-driven approach5Deep learning and polarization equilibrium based state of health estimation for lithium-ion battery using partial charging data25A comprehensive and quantitative SEM–EDS analytical process applied to lithium-ion battery electrodes18The Evolution of Smartphone Battery Technology: From the First Mobile Phone to Modern-Day Devices and Future Advancements0Utilizing Machine Learning to Advance Battery Materials Design: Challenges and Prospects51Role of artificial intelligence in the design and discovery of next-generation battery electrolytes29Accurate estimation of state of health for lithium-ion batteries based on Pyraformer and TimeGAN data augmentation18Accelerated Discovery of Solid-State Electrolytes Using Bayesian Optimization5Deep Learning-Driven Molecular Generation and Electrochemical Property Prediction for Optimal Electrolyte Additive Design8Features of the formation of a potential barrier in a dispersed medium of a two-phase dispersed system based on ZrO2 and Ca6H2O19Si61Application-oriented design of machine learning paradigms for battery science69From Mining to Manufacturing: Scientific Challenges and Opportunities behind Battery Production100Understanding the Electrochemical Window of Solid-State Electrolyte in Full Battery Application81Benchmarking machine learning models for predicting lithium ion migration27Sensors Innovations for Smart Lithium-Based Batteries: Advancements, Opportunities, and Potential Challenges62A brief review: transition metal oxides with carbon composite materials for high-performance supercapacitor, applications, fabrication methods, and future perspective31Accelerated development of battery materials leveraging artificial intelligence and automation3State‐of‐the‐Art Machine Learning Technology for Sustainable Lithium Battery Cathode Design: A Perspective13Discovering novel halide perovskites using generative adversarial networks and random forest regression4Advancing our understanding of optoionic effects for the design of solar batteries: a theoretical perspective1Cutting‐Edge Developments at the Interface of Inorganic Solid‐State Electrolytes38Large language models for batteries31Evolution and Degradation Patterns of Electrochemical Cells Based on the Analysis of Interfacial Phenomena at Li Metal Anode/Electrolyte Interfaces2DegradAI: A scalable framework for early battery health diagnosis from limited data2Explainable Artificial Intelligence of Things for Health Prognosis of Lithium-Ion Batteries5Average Voltage Prediction of Battery Electrodes Using Transformer Models with SHAP-Based Interpretability2Chemical foundation model-guided design of high ionic conductivity electrolyte formulations13AI‐Driven Advances in Sustainable Materials for Green Energy: From Innovation to Lifecycle Management31Generative Deep Learning for Advanced Battery Materials3Comparative Advances in Sulfide and Halide Electrolytes for Commercialization of All‐Solid‐State Lithium Batteries50Transformative applications of artificial intelligence in lithium battery materials science: advancements and future prospects0Nonequilibrium molecular dynamics of ion conduction with equivariant neural network models1Data-Efficient Characterization of Battery Slurry Viscosity using Uncertainty-Based Active Learning with Neural Networks0Interface engineering and safety in solid-state batteries: Advancing from human-centered insights to AI-driven innovations22Machine learning–driven prediction and analysis of lifetime and electrochemical parameters in graphite/LFP batteries0Machine-Learning-Accelerated Development of High-Nickel NCM Cathodes via Multivariable Co-optimization3Two‐Dimensional Antiferromagnetic Fe 2 As 2 as an Anode Material for Rechargeable Li‐Ion Batteries: A DFT Study1Solid-state battery: An emerging transformation of battery technology in electric vehicle applications6Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency44Machine learning-based approach for PV energy forecasting for mono-Si, poly-Si and a-Si Grid-connected PV systems1Integrating Density Functional Theory with Deep Neural Networks for Accurate Voltage Prediction in Alkali‐Metal‐Ion Battery Materials4Machine Learning-assisted Design of High-temperature BSPT-based Piezoelectric Ceramics with Enhanced Dual Properties1Oxidation-aware hybrid tabular-graph neural network for intercalation voltage prediction across multiple working-ion chemistries0Scientific Claims Under Distribution Shift: A Conceptual Theory for Robust Materials AI Inference0

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  48. Comprehending garnet solid electrolytes and interfaces in all-solid lithium-ion batteries

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  49. Toward Knowledge Engineering Using MatFlow for Inverse Quantum Dye Design

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  50. OPTIMIZATION OF WEDM PARAMETERS USING MACHINE LEARNING: A COMPARATIVE ANALYSIS OF SELECTED REGRESSION MODELS

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