Yizhou Zhu

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...

Computational Discovery of Novel Sulfide Lithium Superionic Conductors via a Structural‐Feature‐Constrained Generative Model

ABSTRACT Recent advances in generative models have opened new avenues for discovering novel materials with desired properties by steering generation toward property constraints. However, for properties that are scarce, inconsistent across different sources, or difficult to obtain...