Machine Learning‐Driven Design of Electrolytes and Interfaces for Solid‐State Lithium Batteries: Progress and Outlook

Abstract

Solid‐state lithium batteries are a leading candidate for next‐generation high‐energy‐density and high‐safety batteries but suffer from inherent trade‐offs among ionic conductivity, electrochemical stability, and mechanical strength in solid‐state electrolytes, as well as complex electrode–electrolyte interfacial coupling involving side reactions, space‐charge layers, and mechanical mismatch. Traditional trial‐and‐error experiments and isolated first‐principles calculations struggle to address such high‐dimensional, multiscale problems. Recently, machine learning has offered a new route to this challenge, enabled by breakthroughs in machine‐learning potentials (MLIPs) that approach density functional theory (DFT) accuracy while extending atomistic simulations to tens of thousands of atoms and nanosecond timescales, allowing direct observation of ion transport pathways and interfacial dynamic evolution. Concurrently, models such as graph neural networks have significantly improved the efficiency of high‐throughput screening for candidate materials, while the integration of active learning, Bayesian optimization, and automated experimental platforms is fostering a closed‐loop “prediction–validation–optimization” research paradigm. This review systematically summarizes recent advances in machine learning for solid‐state lithium batteries, covering methodological foundations, bulk material design, interface engineering, and closed‐loop R&D (research and development) pathways. It highlights that machine learning has evolved from merely improving screening efficiency to a pivotal hub iteratively integrating material discovery, interface analysis, and experimental optimization.