Park, Kanguk Jang, Myeongcho Kwon, Eunji Lee, Yongheum Jung, Hun-Gi Chung, Kyung Yoon Yu, Seung-Ho Yu, Seungho 2026-02-26T08:00:09Z 2026-02-26T08:00:09Z 2026-02-26 2026-04 2211-2855 https://pubs.kist.re.kr/handle/201004/154377 Lithium thioantimonate argyrodite solid electrolytes, Li6 +xMxSb1–xS5I (M=Si, Ge), are promising candidates for all-solid-state batteries due to their exceptional ionic conductivity. However, limited mechanistic understanding hinders the rational design of these materials. In this study, we systematically investigate the underlying Li-ion conduction mechanisms and propose a cation-disorder-driven design strategy using machine-learned interatomic potentials (MLIPs). While inter-cage migration via the Wyckoff 16e (T4) site remains significant, enhanced inter-cage migration through Wyckoff 48 h (T2) sites induced by Si and Ge dopants emerges as a critical factor for achieving high ionic conductivity. Additionally, Si and Ge exhibit distinct inductive effects: Si requires higher substitution to activate T2 pathways, while Ge achieves optimal conductivity at lower levels. Co-substitution of Si and Ge further increases cation disorder, yielding ionic conductivity up to ∼50 mS/cm. This study demonstrates the effectiveness of MLIPs in elucidating conduction mechanisms and facilitating the rational design of advanced argyrodite electrolytes. English Elsevier BV Design principles for cation-disordered superionic thioantimonate argyrodite solid electrolytes Article 10.1016/j.nanoen.2026.111777 1 Nano Energy, v.150 Nano Energy 150 Y scie scopus 001691583800001 2-s2.0-105029663019 Chemistry, Physical Nanoscience & Nanotechnology Materials Science, Multidisciplinary Physics, Applied Chemistry Science & Technology - Other Topics Materials Science Physics Article STRUCTURAL DISORDER IONIC-CONDUCTIVITY LI6PS5X X LITHIUM DIFFUSION BETA-LI3PS4 MECHANISM BR CL Cation disorder Machine-learning Interatomic potentials Thioantimonate argyrodite Ionic conductivity Conduction mechanism