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

[Display omitted] • 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. • Using ACE force field, the molecular dynamics simulation of Fe-Co alloy was carried out and the temperature related phase transformation range of Fe-Co alloy was correctly predicted. • The ACE force field is able to predict well the phase transition hysteresis behavior, which contributes to the development of the force field of Fe-Co and the use of molecular dynamics to simulate the physical properties of such alloy materials. 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. [ABSTRACT FROM AUTHOR]