Machine Learning and Theoretical Computation Synergy Advancing Halide Electrolytes Toward All‐Solid‐State Lithium Batteries: Recent Advances, Challenges, and Perspectives

Abstract

Machine learning (ML) demonstrates profound potential to accelerate the development of halide solid‐state electrolytes (HSSEs) for all‐solid‐state lithium batteries (ASSLBs). Further synergistic integration of ML with theoretical computational methods enables researchers to effectively decipher complex structure–property relationships, predict essential performance metrics, and guide rational design of high‐performance HSSEs. This review begins with a comprehensive overview of HSSEs, emphasizing their structural characteristics, ion transport mechanisms, and prevailing challenges. The theoretical basis and advanced computational techniques are then systematically elaborated along with their specific roles in simulating ion migration behavior, thermodynamic stability, and interfacial phenomena. The core of the review focuses on the integration of diverse ML approaches—encompassing supervised, semi‐supervised, unsupervised, and reinforcement learning—combined with theoretical methods to facilitate high‐throughput screening, feature engineering, property prediction, and mechanistic interpretation. Representative applications are elaborated, including the prediction of ionic conductivity, migration energy barriers, activation energy, and electrochemical stability, as well as the development of machine learning potentials for realistic multiscale simulations. Finally, the review provides forward‐looking perspectives on emerging research paradigms and further expansion beyond lithium‐based systems. This work aims to establish a foundational roadmap for the data‐driven and rational design of advanced HSSEs, thereby advancing the realization of next‐generation ASSLBs.