AI‐Driven Big Data Frameworks for Electrode–Electrolyte Interphases in Batteries

Abstract

ABSTRACT This review presents a comprehensive perspective on how AI and big data strategies can transform the understanding and design of the electrode–electrolyte interphases (EEI) in rechargeable batteries, highlighting their pivotal role in battery performance and longevity. Through uniting high‐throughput experimentation and high‐throughput computation (HTC), which includes automated cell fabrication, advanced characterization, large‐scale HTC screening, and reaction network modeling, diverse datasets can be generated to reveal the mechanistic foundations of interfacial processes. The integration of these datasets with artificial intelligence‐orchestrated workflows and machine learning models, such as closed‐loop optimization and large language model‐assisted hypothesis generation, enables the prediction of interphase behavior, linking molecular‐level EEI understanding and macroscale device performance, and data‐driven discovery of optimal material combinations. Critically, the review identifies persistent challenges, including limited data standardization, a shortage of high‐quality interoperable datasets, the gap between optimization and generalizable understanding, the limits of currently available self‐driving labs, and outlines mitigation strategies for building intelligent, data‐centric frameworks for rational engineering of next‐generation battery systems.