ABSTRACT Rising energy demand from electronics, electric transportation, and the grid is driving the development of safer, cheaper, scalable rechargeable batteries. Among all battery components, electrolytes play a pivotal role in governing energy density and cycle life, motivating intensive research into advanced methodologies for the rational design and selection of next‐generation electrolyte systems. Conventional trial‐and‐error electrolyte design is often time‐consuming and resource‐intensive, and struggles to efficiently navigate the vast chemical and materials spaces associated with organic, aqueous, and solid‐state electrolytes (SSEs). Machine learning (ML) strategies have emerged as powerful tools for guiding and accelerating the design of electrolytes. They can extract structure–property relationships, enable high‐throughput screening, and inform experimental decision‐making. Many ML methods are being applied to identify improved electrolyte chemistries that enhance battery performance. However, an in‐depth and critical understanding of how specific ML paradigms align with electrolyte classes, target properties, data quality, and validation practices has not yet been comprehensively addressed in the literature. In this review, we discuss established and emerging ML methods for electrolyte design by comparing them and task‐focused evaluations of ML strategies across different electrolyte systems. We highlight their effectiveness, electrolyte design, machine learning, model validation, rechargeable batteries, workflow limitations, and opportunities for integration into experimentally validated design workflows.
Jagadeesan et al. (Tue,) studied this question.