A pre-trained deep potential model for sulfide solid electrolytes with broad coverage and high accuracy

Solid electrolytes with fast ion transport are crucial for solid state lithium metal batteries. Chemical doping has been the most effective strategy for improving ion condictiviy, and atomistic simulation with machine-learning potentials helps optimize doping by predicting ion conductivity for various composition. Yet most existing machine-learning models are trained on narrow chemistry, requiring retraining for each new system, which wastes transferable knowledge and incurs significant cost. Here, we propose a pre-trained deep potential model purpose-built for sulfide solid electrolytes with attention mechanism, known as DPA-SSE. The training set includes 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 with remarkable accuracy. DPA-SSE generalizes well to complex electrolytes with mixes of cation and anion atoms, and enables highly efficient dynamical simulation via model distillation. DPA-SSE also serves as a platform for continuous learning and can be fine-tuned with minimal downstream data. These results demonstrate the possibility of a new pathway for the AI-driven development of solid electrolytes with exceptional performance.