Machine learning molecular dynamics simulation reveals lithium ion diffusion mechanisms in solid-state electrolytes, suggesting composition impacts conductivity.
Inorganic glass solid‐state electrolytes (IGSSEs) exhibit superionic conductivity at ambient temperature. Understanding their ion conduction mechanism remains challenging but is essential for the development of next‐generation all‐solid‐state batteries. The coupling between lithium ion diffusion and the rotation of neighboring polyanions, known as the paddlewheel effect, has been proposed as a possible mechanism, though its existence remains controversial. Herein, a systematic and extendible approach is proposed to explore the ion conduction mechanism of IGSSEs using large‐scale machine learning molecular dynamics (MLMD) simulations and hop function analysis. A machine learning potential is constructed for model IGSSEs of Li x AlCl 3 + x ( x = 0.25 to 3). MLMD simulation results reproduce the experimentally observed non‐monotonic composition dependence of lithium‐ion conductivity, with a maximum at x = 1. Hop function analysis reveals that lithium ion diffusion occurs mainly via cascading hopping events rather than paddlewheel motions. The cascading hops are composition‐dependent and account for the observed non‐monotonic composition dependence. The non‐monotonic composition dependence arises from a delicate balance between the local concentrations of lithium ions and the lithium vacancies.
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Kang et al. (2025) studied this question.
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