A Density-Matrix Framework for Electronic-Structure Analysis of Electrolytes for Lithium Batteries

Electrolyte reactivity in lithium batteries is shaped by molecular functional groups, Li^{+} solvation and salt-anion participation. Conventional quantum chemistry is too computationally expensive for systematic analysis of diverse electrolyte molecules and their local solvation environments. Here we present EMolStudio, a density-matrix-centered AI platform for electronic-structure prediction and analysis. Its workflow integrates molecular functionalization, explicit Li^{+} first-shell assembly, density-matrix prediction, and electronic-structure parsing. Applied to 163,655 functionalized molecules and 22,500 first-shell clusters across four lithium salts, we find that 1) functionalization separates CO_{2}Me, CN, F/CF_{3}, and sulfonyl groups by distinct shifts in frontier levels, electrostatic potential, and Li^{+}-donor contact; 2) anion identity reshapes frontier-orbital localization, with LiTDI anchoring the highest occupied orbital on the anion across the library. By carrying a unified density-matrix representation from molecular functionalization to salt-resolved solvation shells, EMolStudio provides a general platform for understanding and designing battery electrolytes.