ジャーナル論文 The comparison between the Pt-Rh thermodynamic database from machine learning interatomic potential and first-principles calculations
Arkapol Saengdeejing (author) (この著者で検索)
ORCID SAMURAI ;
Ryoji Sahara (author) (この著者で検索)
ORCID SAMURAI ;
Hiori Kino (author) (この著者で検索)
ORCID https://orcid.org/0000-0002-8912-686X
National Institute for Materials Science
ORCID ;
Yoshiyuki Kawazoe (author) (この著者で検索)
;
Kazuyuki Higashino (author) (この著者で検索)
;
Toyohiro Chikyow (author) (この著者で検索)
ORCID SAMURAI
コレクション

引用
Arkapol Saengdeejing, Ryoji Sahara, Hiori Kino, Yoshiyuki Kawazoe, Kazuyuki Higashino, Toyohiro Chikyow. The comparison between the Pt-Rh thermodynamic database from machine learning interatomic potential and first-principles calculations. npj Computational Materials. 2026, 12 (1), 362. https://doi.org/10.1038/s41524-026-02212-9

説明:

(abstract)

Platinum–rhodium alloys are one of the prominent alloys used in high-temperature and high-corrosion environments. Pt and Rh maintain a single solid-solution phase up to high temperatures. To develop and design a part for use in the field, many techniques, tools, and software in combination, such as the phase-field method for microstructure prediction and the finite element method for stress analysis, are required. These methods require additional data, such as thermodynamic stability. Developing a thermodynamic database is costly and time-consuming. To accelerate the construction of a thermodynamic database, advanced computational methods are usually incorporated into the process to facilitate data acquisition required for thermodynamic assessment. With the advancement of machine learning techniques, many tools for computational materials science have been available at a fraction of the computational resources required when performing similar calculations using traditional techniques. In this study, we employed machine learning interatomic potential (MLIP) to calculate the thermodynamic properties required for the CALPHAD-type thermodynamic assessment of the Pt-Rh binary system. First-principles calculations were performed to compare the thermodynamic database constructed from the MLIP and first-principles calculation data. The phase diagram calculated from MLIP achieved an accuracy similar to that of the phase diagram calculated using more first-principles calculations.

権利情報:

キーワード: Pt-Rh alloy, machine learning potential, density functional theory, phase diagram

刊行年月日: 2026-07-09

出版者: Springer Science and Business Media LLC

掲載誌:

  • npj Computational Materials (ISSN: 20573960) vol. 12 issue. 1 362

研究助成金:

  • Japan Society for the Promotion of Science 24K01149
  • Acquisition, Technology & Logistic Agency, Japan JPJ004596
  • Ministry of Education, Culture, Sports, Science and Technology JPMXP1122715503

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1038/s41524-026-02212-9

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更新時刻: 2026-11-05 13:08:38 +0900

MDRでの公開時刻: 2026-11-05 14:27:47 +0900

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