Coupling Data‐Driven and Reinforcement Learning for Material Development and Device Management in Batteries

Abstract

ABSTRACT The growing demand for high‐energy, safe, and durable batteries demands innovative strategies in both material discovery and device management. Conventional approaches, rooted in empirical trial‐and‐error and physics‐based modeling, often struggle to address the complexity and dynamic nature of next‐generation battery systems. Here, we highlight the emerging synergy between data‐driven machine learning and reinforcement learning (RL), establishing a coupled paradigm that unifies predictive modeling with adaptive optimization. Data‐driven methods enable rapid screening of cathodes, anodes, and liquid/solid electrolytes through multi‐source data mining, while RL agents iteratively optimize synthesis conditions, interfacial properties, and charging protocols. Together, these approaches create closed‐loop frameworks for materials development (prediction, exploration, and validation) and device management (data insight and strategy optimization), which accelerate discovery, enhance safety, and improve performance across the battery lifecycle. Finally, we further outline critical opportunities in data processing, feature engineering, and model building that can elevate this coupled paradigm from conceptual promise to industrial deployment. This integration of data‐driven learning with reinforcement intelligence paves a pathway toward autonomous, high‐throughput battery innovation, providing new foundations for next‐generation energy technologies.