固态电解质具有碳铁矿结构,如Li6PS5Cl,由于与液体电解质相比具有优越的安全性和比其他固体电解质更高的离子电导率,因此引起了相当大的关注。尽管已经做出了实验努力通过控制无序程度来增强电导率,潜在的扩散机制尚未完全了解。此外,基于从头算分子动力学(MD)模拟的现有理论分析在解决室温下的各种类型的紊乱方面存在局限性。在这项研究中,我们直接研究了300K下Li6PS5Cl中的锂离子扩散,由机器学习潜力(MLP)授权的长期MD模拟。为保证电导率值在10%的误差范围内收敛,我们采用25ns的模拟,使用包含6500个原子的5×5×5超级单元。计算的锂离子电导率,活化能,平衡场地的占用与实验观察结果吻合良好。值得注意的是,当Cl离子占据4c位点的25%而不是无序最大化的50%时,锂离子电导率达到峰值。此外,锂离子扩散表现出非阿伦尼乌斯行为,导致高温(>400K)下不同的活化能。这些现象可以通过跳间和跳间之间的相互作用来解释。通过阐明影响Li6PS5Cl中锂离子扩散的关键因素,这项工作铺平了道路,为优化离子电导率在银铁矿家族。
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 ab initio 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.