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RESEARCH PAPER · Interdisciplinary materials · 2022

Machine learning in energy storage materials

Zhong‐Hui Shen, Hanxing Liu, Yang Shen, Jia‐Mian Hu, Long‐Qing Chen, Ce‐Wen Nan

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Machine learning in energy storage materialsOriginalMachine-learning-assisted discovery of perovskite materials with high dielectric breakdown strength19Bridging Multiscale Characterization Technologies and Digital Modeling to Evaluate Lithium Battery Full Lifecycle93Prospects for managing end‐of‐life lithium‐ion batteries: Present and future137Recent advances in dispersion and alignment of fillers in PVDF-based composites for high-performance dielectric energy storage93Prediction on the relative permittivity of energy storage composite dielectrics using convolutional neural networks: A fast and accurate alternative to finite-element method6An ecofriendly and universal strategy to balance the active sites and electrical conductivity of biomass-derived carbon for superior lithium storage2Complex impedance spectroscopy for capacitive energy-storage ceramics: a review and prospects113Recent progress of theoretical research on inorganic solid state electrolytes for Li metal batteries33Magnetic Field Effects on the Structure, Dielectric and Energy Storage Properties of High-Entropy Spinel Ferrite (La0.14Ce0.14Mn0.14Zr0.14Cu0.14Ca0.14Ni0.14)Fe2O4/PVDF Nanocomposites11Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization87Single‐atom metal‐nitrogen‐carbon catalysts energize single molecule detection for biosensing29High-performance ionic thermoelectric materials and emerging applications of ionic thermoelectric devices494D printing: interdisciplinary integration of smart materials, structural design, and new functionality100Machine learning guided hydrothermal synthesis of thermochromic VO2 nanoparticles39Machine learning assisted chemical characterization to investigate the temperature-dependent supercapacitance using Co-rGO electrodes13Polymer dielectrics for capacitive energy storage: From theories, materials to industrial capacitors180Highly Pseudocapacitive Storage Design Principles of Heteroatom‐Doped Graphene Anode in Calcium‐Ion Batteries25Application of Machine Learning in Material Synthesis and Property Prediction166Multilevel Heterostructure of MoS 2 /GDYO for Lithium‐Ion Batteries60Hydrothermal ageing of carbon fiber reinforced polymer composites applied for construction: A review62Stable Operation Induced by Plastic Crystal Electrolyte Used in Ni-Rich NMC811 Cathodes for Li-Ion Batteries9Optimization of mechanical properties of multiscale hybrid polymer nanocomposites: A combination of experimental and machine learning techniques125Recent progress on defect-engineering in ferroelectric HfO 2 : The next step forward via multiscale structural optimization38Colossal permittivity and low dielectric loss in (Nb, Ga) co-doped SrTiO3 ceramics sintered in nitrogen atmosphere25Optimizing energy storage performance of 0.9(Na0.5Bi0.5)(Fe0.02Ti0.98)O3-0.1SrTiO3 flexible capacitor via relaxation strategy4Preparation of Ba0.65Bi0.07Sr0.245TiO3 relaxor ferroelectric ceramics with high energy storage capability by coating powders with ZnO9Achieving high energy storage performance in Na0.5Bi0.5TiO3‒based ceramics via Li+ substitution14Integrating crystal structure and numerical data for predictive models of lithium-ion battery materials: A modified crystal graph convolutional neural networks approach13Knowledge Graphs vs Property Predictors: Competing Infrastructures of Materials Intelligence0Single‐atom materials: The application in energy conversion87Superior energy storage performance of BiFeO 3 –BaTiO 3 –CaHfO 3 lead-free ceramics52Temperature-insensitive electrical performance of the potassium sodium niobate-based ceramics modified by barium zirconate6The energy storage application of core-/yolk–shell structures in sodium batteries17Low‐temperature sintering Sm‐doped PMN–PZ–PT multilayer ceramic actuator with high strain and temperature stability7Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future69Piezoelectric Properties and Thermal Stability of Pb(Yb1/2Nb1/2)O3-BiScO3-PbTiO3 Ternary Ceramics3Effects of structural defects on dielectric properties of Nb-doped SrTiO3-based ceramics5Enhancing core–rim structure control in (K,Na)NbO3-based lead-free piezoceramics via rapid sintering method5Accurate Finite Element Simulations of Dynamic Behaviour: Constitutive Models and Analysis with Deep Learning5Enhanced energy storage density of Bi3.25La0.75Ti3O12 thin films by preferred orientation and interface engineering12Significant impact of TiO 2 purity on the phase structure and electrical properties of BiFeO 3 –BaTiO 3 lead‐free piezoelectric ceramics10Spatial distribution, occurrence form, availability and ecological risk assessment of arsenic in soils of riparian zones on the Tibetan Plateau19Realizing excellent pyroelectric performance in BNT-BNN-xLa ceramics via an A-site engineering strategy15Machine learning in the era of smart automation for renewable energy materials9Bismuth Ferrite-Based Lead-Free High-Entropy Piezoelectric Ceramics44Excellent Energy Storage Performance Achieved in Sr(Sc0.5Nb0.5)O3-Doped Bi0.5Na0.5TiO3-Based Lead-Free Relaxor Ferroelectric Ceramics23Improving the Energy Storage Performance of Barium Titanate-Based Ceramics through the Addition of ZnO-Bi2O3-SiO2 Glass13Fault-tolerant structures against breakdown in heterogeneous ferroelectric PbZr0.52Ti0.48O3 thin films based on adaptive self-repairing effects1Interpretable machine learning model of effective mass in perovskite oxides with cross-scale features3Machine learning assisted prediction of the phonon cutoff frequency of ABO3 perovskite materials16Integrated micro thermoelectric devices with self-power supply and temperature monitoring: Design and application in power grid early warning16Ultrafast discharge properties and enhanced energy density in lead‐free NaNbO 3 ‐based relaxor ferroelectric ceramics11Advanced ceramics with integrated structures and functions: Machine learning prediction and experimental verification20Exploring the Core-shell Structure of BaTiO3-based Dielectric Ceramics Using Machine Learning Models and Interpretability Analysis3Generative learning facilitated discovery of high-entropy ceramic dielectrics for capacitive energy storage58AI for dielectric capacitors16Deep-learning enabled atomic insights into the phase transitions and nanodomain topology of lead-free (K,Na)NbO3 ferroelectrics17Applying Machine Learning to Design Delicate Amorphous Micro-Nano Materials for Rechargeable Batteries20Advanced ceramics in energy storage applications: Batteries to hydrogen energy58Predicting ballistic resistance based on the mechanical properties of armored ceramics10Predicting battery applications for complex materials based on chemical composition and machine learning14Database of ternary amorphous alloys based on machine learning1Materials selection of non-metallic glasses for planar solid-oxide fuel cell sealants5Property Prediction for Complex Compounds Using Structure-Free Mendeleev Encoding and Machine Learning9Recent trends of machine learning on energy storage devices7Enhanced design optimization of micro-thermoelectric cooler in optical module5Application of machine learning in adsorption energy storage using metal organic frameworks: A review17Lead-free KNN-based ceramics with enhanced pyroelectric properties and good temperature stability via constructing phase boundary8Machine Learning-Assisted Accelerated Research of Energy Storage Properties of BaTiO3–BiMeO3 Ceramics10High-entropy electromagnetic functional materials: From electromagnetic genes to materials design79Data-driven design of electrolyte additives supporting high-performance 5 V LiNi0.5Mn1.5O4 positive electrodes48Prediction of specific capacitance of activated carbon electrode for energy storage device by machine learning based approach19A guided review of machine learning in the design and application for pore nanoarchitectonics of carbon materials85Machine learning applications in energy systems: current trends, challenges, and research directions91Graded density impactor design via machine learning and numerical simulation: Achieve controllable stress and strain rate12AI ‐Driven Discovery of High Performance Polymer Electrodes for Next‐Generation Batteries10Applications of AI in Modern/Smart Power Grids: A Short Review0Machine learning for hydrogen storage applications: An exploration of the current literature11Enhanced breakdown strength and energy storage density of MgO modified (Bi0.5Na0.5)0.6(Ba0.3Sr0.7)0.4TiO3 ceramics fabricated by spark plasma sintering6Predicting unmeasured asymmetry time spectra in μ SR experiments using Random Forest0Multiscale Modulation for Enhanced Breakdown Strength and Relaxation Performance in Bi0.5Na0.5TiO3-Based Ferroelectric Films5Recent advances in elevated-temperature flexible composite dielectrics for energy storage applications14Key aspects of sustainable and high-performance K-ion batteries: A machine learning approach4Machine learning in X-ray diffraction for materials discovery and characterization19Machine Learning‐Driven Ultra‐High Energy Storage Performance in Ferro‐Superparaelectric Capacitors29Optimized energy storage performance in HZO3ZO12 thin films through modulation of deposition temperature and film thickness2Multi-matrix co-optimization and artificial neural network-assisted property prediction of carbon fiber reinforced friction materials14Recent advances in the high entropy materials for advanced energy storage with machine learning6Research on the Variation Law of Resistivity of Composite Insulating Materials under Multiple Physical Fields0Multi-temporal forecasting framework for net-zero energy management in cold climates via synergistic hydrogen–battery storage shifting5The prediction of energy storage parameters for (Ba0.85Ca0.15) (Ti0.9Zr0.1) O3 ceramic using artificial neural networks2Molecular property prediction: Input types and information processing in machine learning models5Superior Energy‐Storage Performance Enabled by Machine Learning Accelerated Composition Design for Lead‐Free Antiferroelectrics1Soluble‐Salt‐Template Synthesis of Nanosheets With Tunable Two‐/Three‐Dimensional Architectures for Electrochemical Energy Storage and Conversion3Soluble‐Salt‐Template Synthesis of Nanosheets With Tunable Two‐/Three‐Dimensional Architectures for Electrochemical Energy Storage and Conversion0Atomic‐Scale Intermediate Polarization States Enable Superb Energy Storage in NaNbO 3 Ceramics via Machine Learning5Data-driven State-of-Charge Estimation for Supercapacitor Balancing Systems0From Trial‐and‐Error to Artificial Intelligence‐Guided Design of Hard Carbon for Sodium‐Ion Batteries1Optimisation of Equipment Maintenance Strategies Based on Deep Learning0Prediction of doped silicon phases for enhanced lithium-ion battery anodes: Exploring the superior potential of C, N, and F doping0Advanced dielectric spectroscopy, electrochemical impedance spectroscopy and machine learning investigation of DMF–n-Octanol binary mixtures0Synergistic Defect Chemistry and Strain Engineering via Coating Layer Design for Enhanced Electrical Property of Nano-BaTiO3 Sintered in Reducing Atmosphere0Intrinsic Large‐Unit‐Cell Systems for Advanced Thermoelectrics and Thermal Management0Machine Learning‐Assisted Development in Insulating Materials for High Voltage Applications0Atomic‐Layer Materials for Energy Storage Systems0When universal machine learning models fail in capacitance prediction for carbon-based supercapacitors: Leakage-aware validation and electrolyte screening0

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