Artificial Intelligence for Functional Battery Materials: From Multimodal Mechanism Mining to Closed‐Loop Autonomous Optimization

Abstract

Artificial intelligence (AI) is reshaping battery‐materials research, but its value extends beyond prediction accuracy on static datasets. Functional battery materials involve strongly coupled chemical, structural, electrochemical, interfacial, and processing variables, requiring contextualized data, mechanistic credibility, uncertainty assessment, and experimental validation. This review presents a unified framework connecting multimodal data infrastructure, task‐matched AI models, mechanism mining, and closed‐loop optimization. We examine battery data sources, representations, FAIR principles, ontologies, reusable platforms, and benchmark limitations, highlighting metadata completeness, domain shift, dataset bias, negative results, and computational‐label uncertainty. We compare major model families according to the battery questions they address, their risks, and validation requirements, and discuss applications to cathodes, anodes, electrolytes, solid‐state interfaces, and beyond‐Li systems. Particular emphasis is placed on whether AI‐derived descriptors and latent features can be linked to experimentally testable mechanisms. We further assess active learning, Bayesian optimization, robotic experimentation, real‐time control, smart batteries, and digital twins through evidence‐hierarchy and closed‐loop‐maturity perspectives. We argue that battery AI should progress from isolated prediction toward multimodal, mechanism‐aware, uncertainty‐quantified, and experimentally validated closed‐loop discovery.