Machine Learning for Rational Electrolyte Design in Lithium Batteries: Bridging Macroscopic Performance and Physically Interpretable Descriptors

Abstract

Rational design of high‐performance electrolytes is central to the development of next‐generation lithium batteries, but it remains hindered by vast molecular design space, intricate multiscale structure–property relationships, and multiple performance requirements. Although machine learning (ML) accelerates electrolyte discovery through data‐driven performance prediction, most existing studies rely on sparse performance data and provide limited physical interpretability, thereby falling short of true rational design. This review traces the evolution of ML‐enabled electrolyte research from macroscopic performance prediction to mesoscale mechanistic understanding, with particular emphasis on the limited coverage of electrolyte chemical space in existing performance datasets and the insufficient evaluation of model extrapolation. We then review the physically interpretable descriptors and examine their intercorrelations and domains of applicability using a common set of 26 representative solvents. The current maturity and remaining limitations of ML‐based descriptor prediction are further assessed to support the high‐throughput virtual screening of electrolyte candidates. Finally, we review recent advances in performance‐guided autonomous experimentation and discuss the key challenges associated with establishing descriptor‐guided closed‐loop discovery platforms. Although descriptor‐driven rational electrolyte design has yet to be fully realized, this review outlines an actionable roadmap for integrating physically interpretable descriptors, generalizable ML models, and autonomous experimentation toward interpretable, data‐driven electrolyte discovery.