INS10-0511
Exploring the Ion Conduction Mechanism of Glass Solid-State Electrolytes
Topic
S10. AI-assisted Design and Simulation of Polymers
When and Where
Sep 30, 2026
10:45 - 11:10
Room 109
Session Chairs
Charles SING
Presenter(s)
Bong June Sung (Sogang University)
Co-Author(s)
Abstract
Inorganic glass solid-state electrolytes (IGSSEs) have emerged as promising materials for next-generation all-solid-state lithium batteries because of their remarkable room-temperature ionic conductivity. However, the microscopic mechanism behind such superionic transport remains unclear. A long-standing debate centers on the “paddlewheel effect,” which proposes that lithium ion diffusion is assisted by the rotational motion of surrounding polyanions, though evidence for this mechanism has been inconclusive. In this talk, I will present a systematic computational framework combining large-scale machine learning molecular dynamics (MLMD) simulations with hop function analysis to investigate ion conduction in IGSSEs. A machine learning interatomic potential was developed for model systems of LixAlCl3+x (x = 0.25–3), accurately reproducing the experimentally observed non-monotonic composition dependence of lithium-ion conductivity, with a maximum at x = 1. Detailed analysis reveals that lithium transport occurs mainly through cascading hopping events rather than paddlewheel-like motions. We extend this approach to different types of electrolytes including organic ionic plastic crystals (OIPCs), Li3PS4 and NaTaOCl4. This work provides mechanistic insight into ion transport in electrolytes and demonstrates how machine learning–driven molecular simulations can uncover complex diffusion behaviours in disordered materials.
