How Machine Learning Has Driven the Development of Rechargeable Ion Batteries

Abstract Machine learning (ML) has entered rechargeable ion battery (RIB) research. The goal is to speed progress toward carbon‐neutral energy storage. In the last decade, data‐driven workflows have joined experiments and simulations. They deliver rapid screening and structured insight for electrodes and electrolytes. Yet, despite substantial scientific advances, a coherent and mechanism‐aware framework for deploying ML throughout the RIB innovation cycle remains elusive. This review, therefore, concentrates on three interconnected dimensions of ML integration: i) overall machine‐learning process, ii) algorithm and technology selection with interpretability, and iii) application scenarios covering intercalation electrodes, conversion electrodes, and electrolyte design. To this end, design rules and workflow are distilled that can be readily adopted by both experimentalists and theorists. Moreover, the review highlights how task‐oriented ML architectures expedite material discovery, mechanistic elucidation, and process optimization in next‐generation RIB devices. Finally, persisting challenges, especially data scarcity, mechanism distortion, are outlined alongside opportunities offered by diffusion models, transformer frameworks, large language models, and autonomous laboratories. It is expected that this review will effectively guide readers in the design of next‐generation RIBs with emerging AI technologies.