Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909664599343104 |
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| author | Xia, Weiyi Moraru, Maxim Li, Ying Wai Liao, Timothy Chelikowsky, James R. Wang, Cai-Zhuang |
| author_facet | Xia, Weiyi Moraru, Maxim Li, Ying Wai Liao, Timothy Chelikowsky, James R. Wang, Cai-Zhuang |
| contents | Rare earth (RE)-free permanent magnets, as alternative substitutes for RE-containing magnets for sustainable energy technologies and modern electronics, have attracted considerable interest. We performed a comprehensive search for new hard magnetic materials in the ternary Fe-Co-Zr space by leveraging a scalable, machine learning-assisted materials discovery framework running on GPU-enabled exascale computing resources. This framework integrates crystal graph convolutional neural network (CGCNN) machine learning (ML) method with first-principles calculations to efficiently navigate the vast composition-structure space. The efficiency and accuracy of the ML approach enable us to reveal 9 new thermodynamically stable ternary Fe-Co-Zr compounds and 81 promising low-energy metastable phases with their formation energies within 0.1 eV/atom above the convex hull. The predicted compounds span a wide range of crystal symmetries and magnetic behaviors, providing a rich platform for tuning functional properties. Based on the analysis of site-specific magnetic properties, we show that the Fe6Co17Zr6 compound obtained from our ML discovery can be further optimized by chemical doping. Chemical substitutions lead to a ternary Fe5Co18Zr6 phase with a strong anisotropy of K1 = 1.1 MJ/m3, and a stable quaternary magnetic Fe5Co16Zr6Mn4 compound. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_22627 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing Xia, Weiyi Moraru, Maxim Li, Ying Wai Liao, Timothy Chelikowsky, James R. Wang, Cai-Zhuang Materials Science Computational Physics Rare earth (RE)-free permanent magnets, as alternative substitutes for RE-containing magnets for sustainable energy technologies and modern electronics, have attracted considerable interest. We performed a comprehensive search for new hard magnetic materials in the ternary Fe-Co-Zr space by leveraging a scalable, machine learning-assisted materials discovery framework running on GPU-enabled exascale computing resources. This framework integrates crystal graph convolutional neural network (CGCNN) machine learning (ML) method with first-principles calculations to efficiently navigate the vast composition-structure space. The efficiency and accuracy of the ML approach enable us to reveal 9 new thermodynamically stable ternary Fe-Co-Zr compounds and 81 promising low-energy metastable phases with their formation energies within 0.1 eV/atom above the convex hull. The predicted compounds span a wide range of crystal symmetries and magnetic behaviors, providing a rich platform for tuning functional properties. Based on the analysis of site-specific magnetic properties, we show that the Fe6Co17Zr6 compound obtained from our ML discovery can be further optimized by chemical doping. Chemical substitutions lead to a ternary Fe5Co18Zr6 phase with a strong anisotropy of K1 = 1.1 MJ/m3, and a stable quaternary magnetic Fe5Co16Zr6Mn4 compound. |
| title | Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2506.22627 |