Classification of (Dis)ordered Structures as Superionic Lithium Conductors with an Experimental Structure-Conductivity Database

Abstract

Solid-state electrolytes (SSEs) are critical for the development of high-performance all-solid-state batteries. Data-driven efforts to discover novel SSEs have been constrained by the absence of databases linking ionic conductivity with structure, as well as by challenges in encoding structural information for the disorder that is often found in superionic conductors. Here, we construct the largest database to date of experimentally measured ionic conductivity values paired with corresponding crystal structures, comprising 548 Li-containing compounds. Graph-based features, derived using a transfer learning framework, enable learning directly from disordered crystals, and AtomSets models leveraging these features outperform domain-specific features in a classification task. These models are employed to screen the Inorganic Crystal Structure Database (ICSD) and Materials Project for superionic Li-containing compounds. We identify 241 compounds with predicted superionic conductivity and band gaps greater than 1 eV. Experimental validation confirming superionic conductivity in of one of these candidates, Li₉B₁₉S₃₃, demonstrates the utility of this approach for the discovery and development of advanced SSEs for all-solid-state batteries.