Machine Learning for Zinc‐Based Energy Storage Devices: From Data‐Driven Design to Mechanistic Insights

Machine learning (ML) is rapidly emerging as a powerful paradigm for decoding the complex physicochemical relationships that govern aqueous zinc‐based battery systems. By uncovering hidden correlations from high‐dimensional experimental and computational datasets, ML provides unprecedented opportunities to resolve ion transport, interfacial chemistry, and degradation behaviors that remain difficult to capture using conventional approaches alone. Herein, this review presents a comprehensive and mechanism‐oriented perspective on ML in zinc‐based energy storage, with feature engineering positioned as the central conceptual framework. Particular emphasis is placed on how physically meaningful descriptors are constructed and utilized across cathodes, zinc anodes, electrolytes, and electrode‐electrolyte interfaces, as well as on their distinct roles in dictating electrochemical performance. Recent advances are discussed in terms of how ML enables the identification of structure‐property relationships, accelerates materials optimization, and deepens mechanistic understanding of critical processes, including Zn 2+ transport, desolvation, interfacial evolution, and performance degradation. By organizing this evolving field through the dual lenses of device components and fundamental mechanisms, the intrinsic connection between data‐driven modeling with electrochemical theory is clarified. Finally, future directions toward data‐centric, physics‐informed, dynamic, and autonomous ML frameworks are highlighted, which are expected to drive zinc‐based energy storage from empirical trial‐and‐error development toward predictive and rational design.