Construction of Machine Learning Potentials toward the Exploration of Alloy Cluster Catalysts
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.
Authors
- Kōji Shimizu (ORCID: https://orcid.org/0000-0001-5622-9582)
- Satoshi Watanabe (ORCID: https://orcid.org/0000-0002-8069-6938)
- Kentaro Miyamoto (ORCID: https://orcid.org/0000-0003-1386-0045)
- Anh Khoa Augustin Lu
Institutions
- National Institute for Materials Science (JP)
- National Institute of Advanced Industrial Science and Technology (JP)
- The University of Tokyo (JP)
Publication Details
- Journal
- e-Journal of Surface Science and Nanotechnology
- Published
- 2025-05-16
- DOI
- https://doi.org/10.1380/ejssnt.2025-028
- Citations
- 2
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 1.04
Funders
- Japan Science and Technology Agency
- Strategic International Collaborative Research Program