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Description:

High entropy alloys (HEAs) are expected to show excellent performance in various fields, such as catalysts and high-temperature structural materials, but the huge number of configurations makes it difficult to find the optimal compositions for HEAs. In this study, machine learning potentials were developed to accurately predict the total and H/CO adsorption energies of multi-element slab models and cluster models of various sizes and shapes, based on density functional theory calculations.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: High entropy alloys, Machine learning, Density functional theory, Catalysts, CO2 reduction reaction

Date published: 2025-05-17

Publisher: Surface Science Society Japan

Journal:

• e-Journal of Surface Science and Nanotechnology (ISSN: 13480391) vol. 23 issue. 2 p. 188-192 2025-028

Funding:

• Japan Science and Technology Agency (JST) JPMJSC21E2 (触媒・電池応用に向けたハイエントロピー合金材料の理論的設計)

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1380/ejssnt.2025-028

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Updated at: 2026-05-18 15:04:41 +0900

Published on MDR: 2026-05-18 16:23:12 +0900