Enabling rational electrolyte design for lithium batteries through precise descriptors: progress and future perspectives

Abstract

The rational design of new electrolytes has become a hot topic for improving ion transport and chemical stability of lithium batteries under extreme conditions, particularly in cold environments. Traditional research on electrolyte innovations has relied on experimental trial-and-error methods, which are highly time-consuming and often imprecise, even with well-developed theories of electrochemistry. Thus, researchers are increasingly turning to computational methods. Ab initio calculations and advancements in computer science, such as machine learning (ML), offer a more efficient way to screen potential electrolyte candidates. To accurately evaluate these candidates, precise descriptors that accurately reflect specific properties and reliably predict electrochemical performance are highly needed. This review summarizes and compares the most-used descriptors (e.g., donor number and dielectric constant) alongside critical properties (Lewis basicity and polarity). Additionally, several potential descriptors (e.g., local ionization energy) are explored. A comprehensive comparison of these descriptors is provided, and principles for developing new, more effective descriptors are proposed. This review aims to guide efficient electrolyte design and inspire the discovery of better descriptors for high-performance lithium batteries.