Journal Article

Discrepancies and error evaluation metrics for machine learning interatomic potentials

Mo, Yifei, Liu, Yunsheng, He, Xingfeng

2023

Overview

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

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 639/301/1034 / 639/301/1034/1037 / Characterization and Evaluation of Materials / Chemistry and Materials Science / Computational Intelligence / Datasets / Diffusion / Equilibrium / Errors / Learning algorithms / Machine learning / Materials Science / Mathematical and Computational Engineering / Mathematical and Computational Physics / Mathematical Modeling and Industrial Mathematics / Modelling / Molecular dynamics / Physical properties / Point defects / Simulation / Theoretical