The force field describing the calculated interaction between atoms or molecules is the key to the accuracy of many molecular dynamics (MD) simulation results. Compared with traditional or semi-empirical force fields, machine learning force fields have the advantages of faster speed and higher precision. We have employed the method of atomic cluster expansion (ACE) combined with first-principles density functional theory (DFT) calculations for machine learning, and successfully obtained the force field of the binary Fe-Co alloy. Molecular dynamics simulations of Fe-Co alloy carried out using this ACE force field predicted the correct phase transition range of Fe-Co alloy.
@article{arxiv.2303.00753,
title = {Molecular dynamics simulation of the transformation of Fe-Co alloy by machine learning force field based on atomic cluster expansion},
author = {Yongle Li and Feng Xu and Long Hou and Luchao Sun and Haijun Su and Xi Li and Wei Ren},
journal= {arXiv preprint arXiv:2303.00753},
year = {2023}
}