Machine Learning Aided Atomistic Modeling of Solid State Electrolyte Interphase Formation for Li/Li₇P₃S₁₁
Date:
Invited oral presentation on the application of machine learning interatomic potentials to atomistically model solid-electrolyte interphase (SEI) formation in sulfide-based solid-state batteries. Presented the end-to-end workflow from DFT data generation to MLIP training, large-scale MD simulation, and structural analysis of interfacial decomposition products.