Disorder-Dependent Li Diffusion in Li₆PS₅Cl Investigated by Machine-Learning Potential

Solid-state electrolytes with argyrodite structures, such as Li₆PS₅Cl, have attracted considerable attention due to their superior safety compared to liquid electrolytes and higher ionic conductivity than other solid electrolytes. Although experimental efforts have been made to enhance conductivity by controlling the degree of disorder, the underlying diffusion mechanism is not yet fully understood. Moreover, existing theoretical analyses based on <i>ab initio</i> molecular dynamics (MD) simulations have limitations in addressing various types of disorder at room temperature. In this study, we directly investigate Li-ion diffusion in Li₆PS₅Cl at 300 K using large-scale, long-term MD simulations empowered by machine-learning potentials (MLPs). To ensure the convergence of conductivity values within an error range of 10%, we employ a 25 ns simulation using a 5 × 5 × 5 supercell containing 6500 atoms. The computed Li-ion conductivity, activation energies, and equilibrium site occupancies align well with experimental observations. Notably, Li-ion conductivity peaks when Cl ions occupy 25% of the 4c sites rather than at 50% where the disorder is maximized. In addition, Li-ion diffusion shows non-Arrhenius behavior, leading to different activation energies at high temperatures (>400 K). These phenomena are explained by the interplay between inter- and intracage jumps. By elucidation of the key factors affecting Li-ion diffusion in Li₆PS₅Cl, this work paves the way for optimizing ionic conductivity in the argyrodite family.