Machine learning assisted search for Fe–Co–C ternary compounds with high magnetic anisotropy
We employ a machine learning (ML)-guided framework to explore rare earth free magnetic materials, specifically focusing on Fe–Co–C ternary compounds for potential use in permanent magnets. Utilizing a specifically trained crystal graph convolutional neural network model, we efficiently screen a vast space of nearly a million substitutional structures to select 620 promising structures for further investigation by first-principles calculation. We predict five low-energy metastable Fe–Co–C compounds with formation energy less than 150 meV/atom above the convex hull. These compounds exhibit high magnetization (Js > 1.0 T) and significant magnetic anisotropy (K1 > 1.0 MJ/m3), making them promising candidates for permanent magnet applications. The phonon calculations indicate these compounds are dynamically stable. Our ML-guided framework demonstrates the utility of rapidly identifying novel materials with tailored magnetic properties.
Authors
- Masahiro Sakurai (ORCID: https://orcid.org/0000-0002-4324-8511)
- Renhai Wang (ORCID: https://orcid.org/0000-0003-4539-9273)
- Huaijun Sun (ORCID: https://orcid.org/0000-0002-9886-1479)
- Weiyi Xia (ORCID: https://orcid.org/0000-0001-7669-6305)
- Kai‐Ming Ho (ORCID: https://orcid.org/0000-0003-2703-0560)
- James R. Chelikowsky (ORCID: https://orcid.org/0000-0003-2181-3070)
- Cai‐Zhuang Wang (ORCID: https://orcid.org/0000-0002-0269-4785)
- Timothy Liao (ORCID: https://orcid.org/0000-0001-6175-7734)
- Chao Zhang (ORCID: https://orcid.org/0000-0002-5957-2287)
Institutions
- Zhejiang A & F University (CN)
- United States Department of Energy (US)
- Guangdong University of Technology (CN)
- Iowa State University (US)
- Yantai University (CN)
- Ames National Laboratory (US)
- Kavli Institute for the Physics and Mathematics of the Universe (JP)
- Jiyang College of Zhejiang A&F University (CN)
- The University of Tokyo (JP)
- The University of Texas at Austin (US)
Publication Details
- Journal
- APL Machine Learning
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1063/5.0208761
- Citations
- 5
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.59
Funders
- National Science Foundation
- U.S. Department of Energy
- Iowa State University
- National Energy Research Scientific Computing Center
- Guangdong University of Technology
- Office of Science
- Basic and Applied Basic Research Foundation of Guangdong Province
- Basic Energy Sciences