Machine Learning in Liquid Electrolyte Design for Rechargeable Batteries: Addressing Fundamental Challenges

Abstract

Designing high‐performance liquid electrolytes for rechargeable batteries requires simultaneously achieving fast ionic transport, a wide electrochemical window, and interphase stability, yet these objectives are intrinsically coupled and often involve competing trade‐offs. Electrolyte optimization remains a formidable challenge for conventional trial‐and‐error approaches. Recent advances in machine learning (ML) are transforming electrolyte research into a data‐driven and increasingly autonomous paradigm for accelerated materials discovery. This review provides a comprehensive perspective on how ML methodologies are tailored to address the fundamental challenges of liquid electrolyte design. We first introduce the physicochemical foundations of liquid electrolytes alongside key ML workflows, emphasizing dataset construction and feature representation tailored to electrolyte chemistry. We then examine ML strategies for predicting and optimizing ionic transport, electrochemical window, interphase regulation, and multi‐objective optimization, highlighting representative advances and practical design principles for rational electrolyte development. Finally, we discuss current challenges, including data quality, model generalizability, and interpretability, as well as future opportunities for developing mechanism‐informed and transferable ML frameworks. By bridging machine learning with electrolyte science, this review aims to facilitate the rational design of next‐generation liquid electrolytes and accelerate the development of high‐performance rechargeable batteries.