Fig. 1: Overview of the DPA-SSE model and its training dataset.

The DPA-SSE model is pre-trained on a diverse dataset covering sulfide-based chemistries, with a focus on LGPS-like and argyrodite electrolytes. It can be efficiently fine-tuned for downstream tasks using minimal additional data. Furthermore, a distillation framework enables the generation of faster, lightweight DeePMD models from the pre-trained or fine-tuned DPA-SSE, enabling efficient molecular dynamics simulations for target systems.