Phase Transition of Fe-Co Alloy under Magnetic Field with Machine Learning Force Field

The phase transition behavior of alloys can be modulated by magnetic fields, which affects the phase transition process by arranging magnetic moments, thereby changing the thermodynamic and kinetic behaviors of the phase transition. In this work, based on our previous works, we have successfully simulated the process of the phase transition of Fe-Co alloys combining molecular dynamics and spin-lattice dynamics, under a machine-learning force field, with magnetic interaction parameters obtained from the first-principles density functional theory, with and without external magnetic field. From the simulated results, we have demonstrated the mechanism of both its solid-solid and solid-liquid phase transitions, under either 0 T or 10 T external magnetic field.