Physics‐Guided Descriptors Enable Data‐Efficient Prediction of Battery Coulombic Efficiency

ABSTRACT Lithium metal batteries promise energy densities beyond 500 Wh kg −1 ; but their practical deployment remains limited by low Coulombic efficiency and uncontrolled electrolyte‐interface reactions. Here, we show that physics‐guided machine learning can identify the molecular origin of Coulombic efficiency (CE) from small experimental datasets by embedding 3D electrolyte structures into data‐driven descriptors. Among the descriptors examined, the physics‐derived solvent‐surrounding‐Li + descriptor (Li SSL ) enables accurate CE prediction, achieving a test‐set R 2 of 91.15%. Explainable machine learning further reveals Li SSL as the dominant factor governing model performance, indicating that high‐efficiency lithium deposition requires suppression of direct Li +– solvent interactions. This insight establishes a molecular design principle for electrolytes: weakening solvent participation in the primary Li + solvation environment promotes higher CE. Our work provides a physics‐informed, data‐driven framework for accelerating electrolyte discovery toward high‐energy lithium metal batteries.