Construction of Machine Learning Interatomic Potentials for Metals

Abstract

Molecular dynamics (MD) is a powerful tool for modeling the phase and structural transformations and the evolution of defects and their influence on the metallic material properties. The accuracy of MD modeling directly depends on the quality of interatomic potentials. Modern machine-learning potentials are typically trained on random atomic configurations. This approach has significantly improved the quality of new potentials over traditional EAM potentials. In this work, exact solutions to the equations of atomic motion are offered to train the machine learning potentials.