[1F4-GS-10] AI application: Chemistry / Physics

[1F4-GS-10-01] Improvement and Evaluation of Loss Functions for Avoiding Simulation Failures in Machine Learning Molecular Dynamics Calculations

〇Gen Li¹, Takeichiro Nishikawa¹, Yousuke Isowaki¹, Kazuki Ise¹, Naoki Kurokawa¹, Takashi Yoshida¹, Yasuhiro Harada¹ (1. TOSHIBA Corporation)

Keywords:materials informatics, molecular dynamics, machine learning, acceleration, simulation breakdown

Machine learning molecular dynamics (MLMD) have gained attention due to their ability to simulate large-scale and long-time simulations of materials that were previously impossible. Despite recent progress in force prediction accuracy on machine learning force fields, high force accuracy doesn’t always guarantee simulation’s success. In this study, we investigate the factors contributing to simulation failure and proposed a novel loss function which can lead to simulation success. Our analysis using the MD17 dataset reveals that light atoms are abnormally close to other atoms frequently, and acceleration error for light atoms is relatively large. Further, new loss function which takes acceleration error into account, has been shown to prevent simulation failure or extend the time until failure. Therefore, we assume that reducing the acceleration error is important for machine learning force field.