📝 SummarXiv

Abstract

Oxide Li-conducting solid-state electrolytes (SSEs) offer excellent chemical and thermal stability but typically exhibit lower ionic conductivity than sulfides and chlorides. This motivates the search for new oxide materials with enhanced conductivity. Crystal structure prediction is a powerful approach for identifying such candidates. However, the structural complexity of oxide SSEs, often involving unit cells with more than 100 atoms, presents significant challenges for conventional methods. In this study, we introduce TOPIC, a structure prediction algorithm that reduces configurational complexity by enforcing corner-sharing (CS) bond topology constraints. We demonstrate that TOPIC successfully reproduces the ground-state and metastable structures of known oxide SSEs, including LiTa$_2$PO$_8$ and Li$_7$La$_3$Zr$_2$O$_{12}$, which contain up to about 200 atoms per unit cell. By combining this approach with a pretrained machine-learning interatomic potential, we systematically screen quaternary oxide compositions and identify 92 promising candidates with CS frameworks. In particular, Li$_4$Hf$_2$Si$_3$O$_{12}$, which corresponds to the ground state at its composition, exhibits an ionic conductivity of 14 mS cm$^{-1}$, a hull energy of 21 meV atom$^{-1}$, and a band gap of 6.5 eV. Through our investigation, we identify the Li ratio as one of the key factors determining the stability of CS structures. Overall, our approach provides a practical and scalable pathway for discovering high-performance oxide solid electrolytes in previously unexplored chemical spaces.