From Composition to Ionic Conductivity: Machine Learning‐Guided Discovery and Experimental Validation of Argyrodite‐Type Lithium‐Ion Electrolytes (Small 2/2026)

Lithium-Ion Electrolytes The cover illustrates the composition-based machine learning framework Elements-To-Ionics (E2I) for predicting and optimizing ionic conductivities of argyrodite-type solid-state electrolytes. Guided by E2I predictions, in article number 2509918, Kota Suzuki, Ryoji Kanno, and co-workers successfully explored Si–Sn, Ge–Si, and Ge–Sn co-doped Li6PS5I systems, achieving ionic conductivities comparable to superionic conductors. This work highlights the potential of composition-based informatics for accelerating next-generation solid-state battery development.