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

https://mdr.nims.go.jp/datasets/44d29fdb-8672-48a1-abe1-9bc0f42d68ee

## File

- [npjComputMater12(2026)362.pdf](https://mdr.nims.go.jp/filesets/10d1abc3-3970-475d-85c2-b277f92faa9d/download) ([Detail](https://mdr.nims.go.jp/filesets/10d1abc3-3970-475d-85c2-b277f92faa9d.md))

## Id

44d29fdb-8672-48a1-abe1-9bc0f42d68ee

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-11-05T03:49:01.165527Z

## Updated at

2026-11-05T04:08:38.108597Z

## Published at

2026-11-05T05:27:47.915703Z

## Doi



## First published url

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

## Date published

2026-07-09

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: The comparison between the Pt-Rh thermodynamic database from machine learning
    interatomic potential and first-principles calculations
  title_type: original
  lang: en

## Description

- description: 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.
  description_type: abstract
  lang: und

## Creator

- name: Arkapol Saengdeejing
  role: author
  orcid: https://orcid.org/0000-0001-8739-3262
  organization: National Institute for Materials Science
- name: Ryoji Sahara
  role: author
  orcid: https://orcid.org/0000-0003-0788-2985
  organization: National Institute for Materials Science
- name: Hiori Kino
  role: author
  orcid: https://orcid.org/0000-0002-8912-686X
  organization: National Institute for Materials Science
- name: Yoshiyuki Kawazoe
  role: author
- name: Kazuyuki Higashino
  role: author
- name: Toyohiro Chikyow
  role: author
  orcid: https://orcid.org/0000-0003-3860-4806
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

## Managing organization



## Keyword

- subject: Pt-Rh alloy
  schema: not_defined
- subject: machine learning potential
  schema: not_defined
- subject: density functional theory
  schema: not_defined
- subject: phase diagram
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by-nc-nd/4.0/
  date_licensed: 2026-11-02

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: npj Computational Materials
  issn: '20573960'
  volume: '12'
  issue: '1'
  article_number: '362'

## Conference



## Related item



## Funding

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

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## Fileset

- id: 10d1abc3-3970-475d-85c2-b277f92faa9d
  filename: npjComputMater12(2026)362.pdf
  content_type: application/pdf
  size: 1231507
  md5: e1b7432f540f5e20d7c6fa729060bd4b

## Thumbnail

fileset_id: 10d1abc3-3970-475d-85c2-b277f92faa9d
filename: npjComputMater12(2026)362.pdf