Molecular dynamics simulation of the transformation of Fe-Co alloy by machine learning force field based on atomic cluster expansion

YL Li and F Xu and L Hou and LC Sun and HJ Su and X Li and W Ren, CHEMICAL PHYSICS LETTERS, 826, 140646 (2023).

DOI: 10.1016/j.cplett.2023.140646

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.

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