Driving force of atomic ordering in Fe$_{1-x}$Pt$_{x}$, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations

TL;DR

This study combines density functional theory and machine-learning Monte Carlo simulations to elucidate the atomic ordering mechanisms in Fe-Pt alloys, emphasizing the crucial role of spin polarization in accurately predicting phase transitions.

Contribution

The paper introduces a machine-learning interatomic potential trained on spin-polarized DFT data, revealing the importance of spin effects in modeling atomic ordering in Fe-Pt alloys.

Findings

Spin polarization significantly enhances formation enthalpy of ordered phases.

MLIP-MC simulations with spin agree well with experimental transition temperatures.

Neglecting spin leads to underestimation of phase transition temperatures.

Abstract

We report the mechanisms of atomic ordering in Fe$_{1-x}$Pt$_{x}$ alloys using density functional theory (DFT) and machine-learning interatomic potential Monte Carlo (MLIP-MC) simulations. We clarified that the formation enthalpy of the ordered phase was significantly enhanced by spin polarization compared to that of the disordered phase. Analysis of the density of states indicated that coherence in local potentials in the ordered phase brings energy gain over the disordered phases, when spin is considered. MLIP-MC simulations were performed to investigate the phase transition of atomic ordering at a finite temperature. The model trained using the DFT dataset with spin polarization exhibited quantitatively good agreement with previous experiments and thermodynamic calculations across a wide range of Pt compositions, whereas the model without spin significantly underestimated the transition temperature. Through this study, we clarified that spin polarization is essential for accurately accounting for the ordered phase in Fe-Pt bimetallic alloys, even above the Curie temperature, possibly because of the remaining short-range spin order.