  Machine learning interatomic potentials (MLPs) are a promising technique for
atomic modeling. While high accuracy and small errors are widely reported for
MLPs, an open concern is whether MLPs can accurately reproduce atomistic
dynamics and related physical properties in their applications in molecular
dynamics (MD) simulations. In this study, we examine the current
state-of-the-art MLPs and uncover a number of discrepancies related to atom
dynamics, defects, and rare events (REs), in their MD simulations compared to
ab initio methods. Our findings reveal that low averaged errors by current MLP
testing are insufficient, leading us to develop novel quantitative metrics that
better indicate the accurate prediction of related properties by MLPs in MD
simulations. The MLPs optimized by the RE-based evaluation metrics are
demonstrated to have improved prediction in multiple properties. The identified
errors, the developed evaluation metrics, and the proposed process of
developing such metrics are general to MLPs, thus providing valuable guidance
for future testing, development, and improvements of accurate, robust, and
reliable MLPs for atomistic modeling.
