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<p>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.</p>

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The comparison between the Pt-Rh thermodynamic database from machine learning interatomic potential and first-principles calculations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月9日

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  • Arkapol Saengdeejing,
  • Ryoji Sahara,
  • Hiori Kino,
  • Yoshiyuki Kawazoe,
  • Kazuyuki Higashino,
  • Toyohiro Chikyow

摘要

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.