Shengjie Tang

A perspective on training machine learning force fields for solid-state electrolyte materials

Abstract Machine learning force fields enable high-accuracy modeling of solid-state electrolytes (SSEs). This perspective evaluates dataset size, reference quality, and model architectures. We show that crystalline frameworks of bulk SSEs favor efficient learning, prioritizing da...