Interpretable machine learning framework for designing high ionic conductivity in low‐temperature lithium‐ion battery electrolytes

Abstract Ionic conductivity is a critical determinant of electrolyte performance in lithium‐ion batteries, governing functionalities such as rate capability and low‐temperature operability. Conventional optimizations, empirical or simulation‐based, face significant limitations in either resource efficiency or predictive accuracy. To address these challenges, we developed an interpretable machine learning (ML) framework that combines least absolute shrinkage and selection operator (LASSO) regression with SHapley Additive exPlanations analysis to elucidate structure–property relationships in multicomponent electrolytes. This framework proposes a novel descriptor, model‐input‐weighted sum of LASSO features, which quantitatively captures the collective influence of molecular characteristics on ionic conductivity. Our approach achieves state‐of‐the‐art predictive accuracy (RMSE = 1.33 mS cm −1 , = 0.88) while identifying two dominant molecular features: PEOE_VSA1, representing surface charge distribution, and NumAtomStereoCenters, reflecting stereochemical complexity. This led to the design of an optimized ternary electrolyte (1 mol L −1 LiTFSI in MA:THF:DMF, 5:3:2 molar ratio) demonstrating unprecedented conductivity values: 15.74 mS cm −1 at 25°C and 2.69 mS cm −1 at −70°C. These results validate our framework's ability to guide the development of high‐performance electrolytes for low‐temperature applications. This study establishes a robust ML framework for accelerated electrolyte discovery, providing fundamental insights into molecular determinants of ionic conductivity.