Driving force of atomic ordering in Fe–Pt alloys, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations

We report the mechanisms of atomic ordering in Fe-Pt bimetallic 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 phase, when spin is considered. MLIP-MC simulations were performed to investigate the phase transition of atomic ordering at finite temperatures. 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. In contrast, 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.

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Publication Details

Journal
Journal of Physics Condensed Matter
Published
2025-02-28
DOI
https://doi.org/10.1088/1361-648x/adbba5
Citations
2
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
1.04
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article

Driving force of atomic ordering in Fe–Pt alloys, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations

Hiroki Tanaka, Jian‐Gang Zhu, Takeshi Kaneshita, Hirotada Ohashi et al.
2 citations
Journal of Physics Condensed Matter
Machine Learning in Materials Science
1.04
article

Driving force of atomic ordering in Fe–Pt alloys, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations

Hiroki Tanaka, Jian‐Gang Zhu, Takeshi Kaneshita, Hirotada Ohashi, Rogério Toshiaki Kondo, Kazuhiko Ochiai, Akira Matsui, Atsushi Hashimoto, T. Fukushima, Yoshishige Okuno, Tomoyuki Tsuyama
article en
2 citations

Abstract

We report the mechanisms of atomic ordering in Fe-Pt bimetallic 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 phase, when spin is considered. MLIP-MC simulations were performed to investigate the phase transition of atomic ordering at finite temperatures. 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. In contrast, 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.

Journal of Physics Condensed MatterVol. 37(16)
DIC (Japan) (JP), TDK (Japan) (JP), Carnegie Mellon University (US), JGC (Japan) (JP)
Affordable and clean energy
Openalex Percentile: Top 27%
Machine Learning in Materials Science
1.04
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