High‐Throughput Data‐Driven Prediction of Stable High‐Performance Na‐Ion Sulfide Solid Electrolytes

Abstract Designing solid electrolytes (SEs) for solid‐state batteries is a challenging issue. Herein, by using data‐driven techniques and a multi‐stage density functional theory molecular dynamics (DFT‐MD) sampling workflow that is developed, 523 443 978 cell structure samples of in‐silico Na‐based sulfides with isolated tetrahedral framework units are generated and a largely unexplored chemical space of the filtered 170 samples is examined to find novel SE candidates. Given the DFT‐accurate MD ion trajectory configurations for the largest cell structure dataset so far, the three (meta)stable solid‐solution series with high Na‐ion conductivity σ Na,300K = 10 −3 –10 −2 S cm −1 : Na 5−2 x Al 1 − x V x S 4 , Na 5−2 x Al 1− x Ta x S 4 , and Na 5−2 x In 1− x Sb x S 4 (0.375 ≤ x ≤ 0.625) are suggested. Moreover, robust descriptors for Na‐ion self‐diffusion coefficient D Na,300K accumulated from the sampling workflow by exhaustive multiple regression modeling is revealed, indicating that crystal structures with wide NaS 3 solid angles and high‐valence dopants with small ionic radii result in high Na‐ion diffusivity.