Machine Learning-Aided Prediction of Na Solid-State Interfaces

We extend machine learning-aided atomistic simulation methods to sodium solid-state battery interfaces, developing MLIP-based workflows to predict interfacial reactivity, SEI composition, and stability for Na-ion solid electrolyte systems.

Recommended citation: Li, B., Jeon, H., Persson, K.A. "Machine Learning-Aided Prediction of Na Solid-State Interfaces." In preparation.