Quantum-accurate multiscale modeling of ramp compressions and magneto-elastic phase transitions in iron

Magnetic spin fluctuations have a significant impact on the thermodynamic properties of magnetic metals. Accurately predicting magneto-structural phase transitions in compressed iron hence requires accounting for those effects.
We achieved this by constructing a magneto-elastic Hamiltonian. Following the Spectral Neighbor Analysis Potential approach, a machine-learning interatomic potential for iron was trained on ab initio calculations performed on the pressure and temperature range of interest. This potential was combined to a magnetic Hamiltonian accounting for transverse and longitudinal spin fluctuations.
Leveraging the numerical capability combining lattice and magnetic degrees of freedom that was recently implemented in LAMMPS, large scale spin-lattice simulations of ramp compressions and phase transitions in iron are performed based on the developed Hamiltonian.