  Solid electrolytes with fast ion transport are one of the key challenges for
solid state lithium metal batteries. To improve ion conductivity, chemical
doping has been the most effective strategy, and atomistic simulation with
machine-learning potential helps find optimized doping by predicting ion
conductivity for arbitrary composition. Yet most existing machine-learning
models are trained on narrow chemistry, and new model has to be trained for
each system, wasting transferable knowledge and incurring significant cost.
Here, we propose a pre-trained deep potential model purpose-built for sulfide
electrolytes with attention mechanism, known as DPA-SSE. The training set
encompasses 15 elements and consists of both equilibrium and extensive
out-of-equilibrium configurations. DPA-SSE achieves a high energy resolution of
less than 2 meV/atom for dynamical trajectories up to 1150 K, and reproduces
experimental ion conductivity of sulfide electrolytes with remarkable accuracy.
DPA-SSE exhibits good transferability, covering a range of complex electrolytes
with mixes of cation and anion atoms. Highly efficient dynamical simulation
with DPA-SSE can be realized by model distillation which generates a faster
model for given systems. DPA-SSE also serves as a platform for continuous
learning, and the model fine-tune requires only a portion of downstream data.
These results demonstrate the possibility of a new pathway for AI-driven
development of solid electrolytes with exceptional performance.
