From Composition to Ionic Conductivity: Machine Learning-Guided Discovery and Experimental Validation of Argyrodite-Type Lithium-Ion Electrolytes.

ABSTRACT: The discovery of solid-state electrolytes (SSEs) with high lithium-ion conductivities is critical for advancing all-solid-state batteries. However, prior efforts have largely focused on structure-driven design. This study presents a composition-based machine learning framework, Elements-To-Ionics (E2I), for the accurate prediction and optimization of the ionic conductivities of argyrodite-type SSEs using only their elemental compositions. Guided by these predictions, a series of Si-Sn, Ge-Si, and Ge-Sn co-substituted argyrodites are synthesized. Li_6.7Ge_0.595Si_0.105P_0.3S₅I achieves the highest ionic conductivity (7.2 × 10^-3 S cm^-1) with a low activation energy (0.20 eV). Using hot-pressing to optimize the conductiv

SUBMITTER: Kong S 

PROVIDER: S-EPMC12781620 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature