  Ensuring solid-state lithium batteries perform well across a wide temperature
range is crucial for their practical use. Molecular dynamics (MD) simulations
can provide valuable insights into the temperature dependence of the battery
materials, however, the high computational cost of ab initio MD poses
challenges for simulating ion migration dynamics at low temperatures. To
address this issue, accurate machine-learning interatomic potentials were
trained, which enable efficient and reliable simulations of the ionic diffusion
processes in Li6PS5Cl over a large temperature range for long-time evolution.
Our study revealed the significant impact of subtle lattice parameter
variations on Li+ diffusion at low temperatures and identified the increasing
influence of surface contributions as the temperature decreases. Our findings
elucidate the factors influencing low temperature performance and present
strategic guidance towards improving the performance of solid-state lithium
batteries under these conditions.
