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RESEARCH PAPER · Advanced Energy Materials · 2025

Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency

Kwang S. Kim

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Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational EfficiencyOriginalAlgorithmic Path Dependence in AI-Driven Materials Design0Discovery of Effective Halide Solid Electrolytes for Solid-State Rechargeable Batteries via Machine Learning and DFT Calculations6Accelerating Materials Discovery Through Sparse Gaussian Process Machine Learning Potentials6Artificial intelligence-driven nanomaterial design and applications: advances, challenges, and future directions0Decoding non-fullerene acceptors for organic solar cells: a theoretical structure–property–performance perspective1Artificial intelligence for energy materials research: From classical machine learning to large models4Artificial Intelligence in Accelerating Materials Discovery: Opportunities and Challenges9Density Functional Theory–Machine Learning-Assisted Insightful Identification of Next-Generation High-Voltage Organic Dual-Ion Batteries3Bridging Topological Over-Squashing and Physical Long-Range Interactions in ML Interatomic Potentials0Facile Critical Evaluation of Extensive Lithium–Oxygen Battery Literature Using In-House Data and the Structured Query Learning–Retrieval-Augmented Generation Method12D Materials Beyond Graphene for Energy Storage and Conversion: Recent Advances and Future Perspectives3Hydroxylation Mechanism of Hydroxyapatite Nucleation with Aspartate Assistance under Confinement Revealed by Machine Learning Molecular Dynamics2Accelerating single-atom ORR catalyst discovery through theory and machine learning: a critical review13Recent advances, challenges and future perspectives of polycyclic aromatic hydrocarbons as materials for metal-ion batteries – A comprehensive review1Machine Learning-Aided High-Throughput Virtual Screening of Novel Fused Nitrogen-Rich Heterocyclic Energetic Compounds0Sensitivity and Performance Analysis of an AI Model for Virtual Prototyping of Brazed Ceramic–Metal Composite Materials0Continuous transition from palladium clusters to nanocrystals simulated by machine learning potential0Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates1Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications14Integrated Machine Learning and Molecular Dynamics for Functional Nanoparticle Design: Synthesis, Characterization, Force-Field Development, and Property Prediction2Required properties of separators for lithium-ion batteries: A systematic review0Prediction of the Tensor-Form Refractive Index in a Cyclic Olefin Copolymer: A Multiscale Computer Simulation Approach0Synergistic hybrid systems of 2D-MOFs and TMDs for advanced energy harvesting2Machine Learning-Guided Prediction of Low-Barrier N 2 and CO 2 Activation for Catalyst Design0Machine learning-guided electrolyte–interface co-design for Li-based anode-free batteries: Toward a predictive framework for stable metal deposition3Accelerating the Discovery of Superhalogens via Physics-Informed Graph Neural Networks1Machine Learning for Zinc‐Based Energy Storage Devices: From Data‐Driven Design to Mechanistic Insights0AI-Driven Two-Dimensional Molecular Electronics Spectroscopy for Chiral Discrimination of Natural Products0A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations1AutoAIM Studio: A desktop application for automated machine learning in materials science0Coupling Molecular Simulation and Machine Learning for CO2 Adsorption Prediction on Defective and N/O/S-Doped Graphene Models: Insights into Feature Importance and Synergistic Effects0Advanced energy materials for a sustainable future: Phase change materials, thermal energy storage, solar thermal integration, numerical modelling, techno-economic assessment, environmental impact, and AI-assisted materials design0Reliable Machine Learning Screening of Adsorption Energies Is Better Assessed with Formula-Grouped Cross-Validation0Hydrogen-bond engineering and crystallographic regulation via lactitol additive toward highly reversible Zn anodes0A review of modern design methods and multi-scale coupling simulation strategies for high-entropy alloys0Quantum-mechanical descriptors and machine learning for energy materials: a proof-of-concept computational framework0Mesoporous single-atom and nano-confined catalysts: Enabling low-energy integrated CO2 capture and direct conversion0Multifaceted In Silico and DFT-Based Analysis of 2-Aminosuccinic Acid on Ferroptosis-Related Targets0Computational design of quantum and functional materials for next generation electronic and energy technologies0Covalent Functionalization of 2D Semiconductors: A Roadmap to Advanced Electronic Devices0Machine learning-based optimization of hydrothermal carbonization for CQDs production0From Passive Targets to Chemical Fuels: Mechanistic Insights and Engineering Implementation of Pollutant–Catalyst Synergy in Oxidant‐Free Fenton‐Like Catalysis0Multiscale computational modeling and machine learning for energy aware safety monitoring in open pit coal mines0First-Principles Investigation of the Structural, Physical, and Radiation Shielding Properties of FrZnX3 (X = F, I) Perovskites0

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  18. Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

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  19. Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

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  20. Integrated Machine Learning and Molecular Dynamics for Functional Nanoparticle Design: Synthesis, Characterization, Force-Field Development, and Property Prediction

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