Generalized machine-learning descriptor for identifying superionic materials

Superionic materials have attracted considerable interest due to their unique electronic and thermal transporting properties, with far-reaching implications for solid-state electrolyte batteries and planetary science. Despite extensive studies, the physicochemical origin of superionic behavior remains insufficiently understood. In this work, we develop a generalized machine-learning descriptor for superionic materials by combining structural, elemental, and density-functional theory primary features with the Sure Independence Screening and Sparsifying Operator (sisso) framework. We identify four key factors to recognize superionic materials: electronegativity, chemical hardness, density, and bond strength. The developed machine-learning descriptor applies well beyond conventional lithium- and sodium-based superionic systems and can be extended to high-pressure conditions. Based on our machine-learning descriptor, we conducted a high-throughput screening of 13 019 materials from the materials project database, and 353 potential superionic candidates were identified. This study not only sheds light on the underlying physical origin of the superionic state but also provides guidance and insights for the design and experimental synthesis of future superionic materials.