A Quantitative Electrostatic Potential Descriptor Enables Deep Learning‐Accelerated Discovery of High‐Performance Lithium‐Ion Battery Electrolytes

Abstract

ABSTRACT Rational electrolyte design for high‐energy‐density lithium‐ion batteries (LIBs) urgently demands precise and quantitative molecular descriptors of solvation power to enable deep learning (DL)‐accelerated screening, yet such descriptors remain lacking. Here, we introduce the electrostatic potential ratio |ESP min |/ESP max (ESP ratio ) as a quantitative descriptor capturing the balance between electron‐donating and electron‐accepting capacities, and identify a solvation modulation zone (0.9 < ESP ratio  < 2.4) through unsupervised clustering of 344 molecules encompassing 196 experimentally reported LIB electrolyte molecules. By combining this descriptor with self‐supervised pre‐trained DL models fine‐tuned on small experimental datasets, we enable hierarchical screening of ∼10 6 PubChem molecules and prioritize electrolyte candidates from previously unexplored chemical space. Experimental evaluation of representative candidates, including TBDN and PIV as co‐solvents and additional nitrile‐containing molecules as electrolyte additives, confirms that the ESP ratio ‐guided workflow can enrich chemically meaningful electrolyte candidates for high‐voltage Li||LiCoO 2 .