  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.
