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 _6.7 Ge _0.595 Si _0.105 P _0.3 S ₅ I 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 conductivity, values comparable to those of Li ₁₀ GeP ₂ S ₁₂ ‐type superionic conductors are achieved (>10 ⁻² S cm ⁻¹ ). The developed model reliably identifies both high‐ and low‐conductivity regions and significantly reduces the experimental workload. These results highlight the potential of composition‐based informatics for accelerating the discovery of high‐performance SSEs within complex chemical spaces, and provide a valuable methodology for the development of next‐generation solid‐state battery technologies.