  It has been a challenge to accurately simulate Li-ion diffusion processes in
battery materials at room temperature using {\it ab initio} molecular dynamics
(AIMD) due to its high computational cost. This situation has changed
drastically in recent years due to the advances in machine learning-based
interatomic potentials. Here we implement the Deep Potential Generator scheme
to \textit{automatically} generate interatomic potentials for LiGePS-type
solid-state electrolyte materials. This increases our ability to simulate such
materials by several orders of magnitude without sacrificing {\it ab initio}
accuracy. Important technical aspects like the statistical error and size
effects are carefully investigated. We further establish a reliable protocol
for accurate computation of Li-ion diffusion processes at experimental
conditions, by investigating important technical aspects like the statistical
error and size effects. Such a protocol and the automated workflow allow us to
screen materials for their relevant properties with much-improved efficiency.
By using the protocol and automated workflow developed here, we obtain the
diffusivity data and activation energies of Li-ion diffusion that agree well
with the experiment. Our work paves the way for future investigation of Li-ion
diffusion mechanisms and optimization of Li-ion conductivity of solid-state
electrolyte materials.
