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RESEARCH PAPER · Chemical Engineering Journal · 2024

Machine learning in energy storage material discovery and performance prediction

Guochang Huang, Fuqiang Huang, Wujie Dong

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Machine learning in energy storage material discovery and performance predictionOriginalInsights on the rate performance of polyaniline supercapacitors by integrated mathematical modeling and machine learning1Review of progress in calculation and simulation of high-temperature oxidation181A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems176Density Functional Theory and Machine Learning of Transition Metals in Mo2C for Gas Sensors12Advances in multi-element doping of LiFePO4 cathode material for capacity enhancement in Li-ion batteries74Review of computational advances in tailoring magnesium-hydrogen interactions: Atomistic simulations meet machine learning13Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives92Recent trends of machine learning on energy storage devices7Adaptive subspace Bayesian optimization over molecular descriptor libraries for data-efficient chemical design5Challenges and advances in redox flow batteries utilizing sustainable and cost-effective non-vanadium active materials19Data-driven design and experimental validation of high-precision Ni–Co bimetallic compound-based pseudocapacitor models4DLS-based optimization of ZnS–CoS nanoparticles with enhanced energy and power density for supercapacitor applications and its validation by AI models20Predicting neutron experiments from first principles: a workflow powered by machine learning6PVDF-based solid polymer electrolytes for lithium-ion batteries: strategies in composites, blends, dielectric engineering, and machine learning approaches32Sequential construction of stable nitrogen–oxygen compounds using high-throughput quantum mechanical calculations and customized machine learning model81Zinc Sulfide Decorated N-doped Porous Carbon on reduced Graphene Oxide-Coated Separators for Suppressing Shuttle Effect in High-Performance Lithium-Sulfur Batteries0Unlocking new horizons, challenges of integrating machine learning to energy conversion and storage research9Application of machine learning in adsorption energy storage using metal organic frameworks: A review17Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials10Deep-learning approach for developing bilayered electromagnetic interference shielding composite aerogels based on multimodal data fusion neural networks15Role of artificial intelligence in the design and discovery of next-generation battery electrolytes29Enhancing energy materials with data-driven methods: A roadmap to long-term hydrogen energy sustainability using machine learning16Operational and Maintenance Challenges of Energy Storage Systems in Microgrid0Machine learning applications in energy systems: current trends, challenges, and research directions91Comprehensive machine learning approaches for modelling the state of charge of lithium-ion batteries10Customized large-scale model for human-AI collaborative operation and maintenance management of building energy systems17Development and optimization of lithium-ion sieves through machine learning in complex brine systems33High-temperature experiments and simulation methods for oxidation behavior research of thermostructural materials11Predicting unmeasured asymmetry time spectra in μ SR experiments using Random Forest0AI-Empowered Electrochemical Sensors for Biomedical Applications: Technological Advances and Future Challenges51Statistics in enabling 2D materials: Optimization, predictive modelling, and data-driven discovery9Accelerating the Discovery of Low-Dielectric Polyimides Based on Interpretable Machine Learning7Machine learning-assisted investigation of strain effects on clustered anti-perovskite solid-state electrolytes: A case of study about Li₃OBH₄ using DFT calculations1The role of computational materials science in achieving sustainable development goals: A review5Mechanisms and modelling of diffusion in solids: a multiscale framework with industrial case studies and AI enhancements18LiCondAI: A Machine Learning Model for Predicting Lithium-Ion Conductivity in Solid Electrolytes5Machine learning in X-ray diffraction for materials discovery and characterization19Artificial intelligence in materials science and chemistry: Past, present and future trajectories9Prediction of SF6 Replacement Gases Based on Machine Learning0Computational frontiers in advancing inorganic solid-state electrolytes for the next-generation lithium batteries3Modeling and state estimation of liquid metal batteries3Process optimization for synthesizing α-Al2O3 with tailored morphology and dispersibility by interpretable incremental machine learning3Ambient-condition hydrogen storage: Accelerating the development of high-capacity hydrides via a quantitative interpretable machine learning framework6Asymmetric multistage composite foams for high-efficient absorption-dominant electromagnetic interference shielding exploited with aid of machine learning6Machine learning-guided optimization of flame retardancy in rigid polyurethane foams0Machine learning-based prediction and optimization of performances of porous ZrO2/Al2O3 ceramics: a few-shot learning approach1Multi-staged machine learning-driven investigation of biomass-derived hard carbon anodes for sodium-ion batteries: Connecting precursor, structure, and performance19Improved clustering-multimodal feature decomposition for multi-time scale terminal voltage characterization, edge computing performance evaluation, and retirement prediction of large-capacity batteries in energy storage power stations0Recent innovations in energy storage system solutions for renewable integration0Review on Recent Development of Artificial Intelligence and Machine Learning Approaches in Energy Applications0Small-sample ensemble learning-driven prediction and mechanistic analysis of hydrogen release performance in modified LiBH 40Use of digital tools in energy management: A key to enhancing cooperation0Vacancy‐Engineered ZnS/N‐Doped Carbon on rGO as a Multifunctional Separator Coating for High Stable Lithium‐Sulfur Batteries0Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation15Solubility named entity recognition using ensemble learning with enhanced model-agnostic meta-learning and dual feature encoding0Predicting dielectric properties of polyetherimide-based composite via combined molecular dynamics simulation and machine learning0AI-Driven Modeling and Optimization of 3D Graphene–MXene Hybrid Nanocomposites0Machine Learning Assisted Development of COFs Materials as Solid Electrolytes for Lithium-Ion Batteries—A Mini Review3How AI-driven technological transformation advances the modernization integrated transition of energy1Accelerated discovery of stable spinels for energy applications using dense neural network approach0Solid-gas thermochemical energy storage systems: A critical review4Revolutionizing sustainable energy: A bibliometric analysis and review of AI applications in nano-enhanced thermal storage2ML empowered supercapacitors: A new era of predictive energy storage research4From Discovery to Deliverables: Bridging the Machine Learning, Synthesis, and Characterization Gap in Nanoscale Materials1First-principles investigation of electrochemical and lithium-ion diffusion properties of LiFePO₄ with Mn/F doping and oxygen vacancy2From Trial‐and‐Error to Artificial Intelligence‐Guided Design of Hard Carbon for Sodium‐Ion Batteries1Improved clustering-multimodal feature decomposition for multi-time scale terminal voltage characterization, edge computing performance evaluation, and retirement prediction of large-capacity batteries in energy storage power stations3Self-powered intelligent sensing networks for next-generation super airports0Dual‐Metal Co‐Doping Strategy Enhances the Hydrogen Dissociation and Hydrogen Release Performance of LiBH 4 Assisted by Ensemble Learning0A deep-learning-based investigation on CO2 capture performance of graphene oxide-based materials1Entropy-defect synergy for dual luminescence mechanism in spinel: Time-resolved anti-counterfeiting and fingerprint visualization0Titanium-based lithium-ion sieves for sustainable lithium recovery from complex liquid resources: Mechanisms, engineering applications, and intelligent design0Understanding the synergistic effects of dopants and Li-Fe antisite defects in LiFePO4: linking electronic structure evolution to Li-ion transport0Aluminum-ion batteries: materials, charge storage mechanisms, and future challenges0Miedema-based formation enthalpy criteria and strategies for optimizing hydrogen storage performance in rare-earth-magnesium hydrides: A review0Machine learning for predictive maintenance in energy storage systems: A systematic review of health prediction, fault diagnosis, and deployment challenges0Machine Learning‐Assisted Development in Insulating Materials for High Voltage Applications0

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  1. Insights on the rate performance of polyaniline supercapacitors by integrated mathematical modeling and machine learning

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  2. Review of progress in calculation and simulation of high-temperature oxidation

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  3. A Comprehensive Review of the Current Status of Smart Grid Technologies for Renewable Energies Integration and Future Trends: The Role of Machine Learning and Energy Storage Systems

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  4. Density Functional Theory and Machine Learning of Transition Metals in Mo2C for Gas Sensors

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  5. Advances in multi-element doping of LiFePO4 cathode material for capacity enhancement in Li-ion batteries

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  6. Review of computational advances in tailoring magnesium-hydrogen interactions: Atomistic simulations meet machine learning

    International Journal of Hydrogen Energy · 2024-10-04

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  7. Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives

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  9. Adaptive subspace Bayesian optimization over molecular descriptor libraries for data-efficient chemical design

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  10. Challenges and advances in redox flow batteries utilizing sustainable and cost-effective non-vanadium active materials

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  11. Data-driven design and experimental validation of high-precision Ni–Co bimetallic compound-based pseudocapacitor models

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  12. DLS-based optimization of ZnS–CoS nanoparticles with enhanced energy and power density for supercapacitor applications and its validation by AI models

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  13. Predicting neutron experiments from first principles: a workflow powered by machine learning

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  14. PVDF-based solid polymer electrolytes for lithium-ion batteries: strategies in composites, blends, dielectric engineering, and machine learning approaches

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  16. Zinc Sulfide Decorated N-doped Porous Carbon on reduced Graphene Oxide-Coated Separators for Suppressing Shuttle Effect in High-Performance Lithium-Sulfur Batteries

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  17. Unlocking new horizons, challenges of integrating machine learning to energy conversion and storage research

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  18. Application of machine learning in adsorption energy storage using metal organic frameworks: A review

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  23. Operational and Maintenance Challenges of Energy Storage Systems in Microgrid

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  27. Development and optimization of lithium-ion sieves through machine learning in complex brine systems

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  28. High-temperature experiments and simulation methods for oxidation behavior research of thermostructural materials

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  42. Process optimization for synthesizing α-Al2O3 with tailored morphology and dispersibility by interpretable incremental machine learning

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  46. Machine learning-based prediction and optimization of performances of porous ZrO2/Al2O3 ceramics: a few-shot learning approach

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  47. Multi-staged machine learning-driven investigation of biomass-derived hard carbon anodes for sodium-ion batteries: Connecting precursor, structure, and performance

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  48. Improved clustering-multimodal feature decomposition for multi-time scale terminal voltage characterization, edge computing performance evaluation, and retirement prediction of large-capacity batteries in energy storage power stations

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  49. Recent innovations in energy storage system solutions for renewable integration

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  50. Review on Recent Development of Artificial Intelligence and Machine Learning Approaches in Energy Applications

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