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 the arranging of magnetic moments, and 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 the molecular dynamics and spin-lattice dynamics, under a machine-learning force field, with the 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 mechanics of both its solid-solid and solid-liquid phase transitions, under either zero or 10 T external magnetic field.