AI‐Organized Multiscale Battery Modeling: Linking Structure and Property from Quantum to Device Scales

Rechargeable batteries exhibit complex behavior arising from coupled processes across electronic, atomistic, mesoscale, and device levels. These cross‐scale interactions govern redox energetics, ion transport, interphase evolution, morphological stability, and ultimately cell performance and safety. While computational approaches such as density functional theory (DFT), molecular simulations (MD), phase‐field modeling (PF), and electrochemical–thermal analyses have advanced mechanistic understanding, their predictive capability remains limited by difficulties in consistently transferring information across scales. Recent advances in artificial intelligence (AI) provide new opportunities to address these challenges. Machine‐learned interatomic potentials enable atomistic simulations with near‐DFT accuracy within defined training domains, while data‐driven sampling strategies facilitate exploration of interfacial reaction pathways. At larger scales, operator‐learning frameworks accelerate mesoscale field predictions, and hybrid digital‐twin approaches integrate simulations with real‐time data for device‐level state estimation and fault detection. This review examines how AI can support the generation, transformation, and propagation of structural descriptors across modeling scales, framing multiscale battery modeling as a coherent structure–property mapping problem. Remaining challenges include limited data coverage for reactive environments, inconsistent physical assumptions across models, and uncertain transferability across chemistries and operating conditions. Addressing these issues will be essential for developing predictive, experimentally grounded models for next‐generation energy‐storage systems.