Coercivity influence of nanostructure in SmCo-1:7 magnets: Machine learning of high-throughput micromagnetic data
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912603138162688 |
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| author | Yang, Yangyiwei Kühn, Patrick Fathidoost, Mozhdeh Adabifiroozjaei, Esmaeil Xie, Ruiwen Foya, Eren Ohmer, Dominik Skokov, Konstantin Molina-Luna, Leopoldo Gutfleisch, Oliver Zhang, Hongbin Xu, Bai-Xiang |
| author_facet | Yang, Yangyiwei Kühn, Patrick Fathidoost, Mozhdeh Adabifiroozjaei, Esmaeil Xie, Ruiwen Foya, Eren Ohmer, Dominik Skokov, Konstantin Molina-Luna, Leopoldo Gutfleisch, Oliver Zhang, Hongbin Xu, Bai-Xiang |
| contents | As a pinning-controlled permanent magnet, tailoring the cellular nanostructure of samarium-cobalt-based 1:7-type (SmCo-1:7) magnets remains crucial for improving magnetic performance. Jointing forward and inverse machine learning models with the high-throughput micromagnetic simulations (42,300 runs), we identify the nanostructural and magnetic features that are most effective for coercivity, combining both nucleation and pinning mechanisms. Sensitivity analyses reveal that the 1:5-phase enhances coercivity by providing high anisotropy, and the Z-phase strengthens pinning through fluctuations in domain wall energy. Cu additions in the 1:5-phase significantly reduce coercivity, while Fe substitutions in the 2:17-phase modestly reduce coercivity but improve pinning locally and increase saturation magnetization. Among all examined features, magnetocrystalline misorientation emerges as the dominant factor. Finally, the framework enables the inverse design of nanostructures with prescribed coercivity, demonstrating a computationally cost-effective toolkit for guiding the performance tailoring of SmCo-1:7 magnets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03198 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Coercivity influence of nanostructure in SmCo-1:7 magnets: Machine learning of high-throughput micromagnetic data Yang, Yangyiwei Kühn, Patrick Fathidoost, Mozhdeh Adabifiroozjaei, Esmaeil Xie, Ruiwen Foya, Eren Ohmer, Dominik Skokov, Konstantin Molina-Luna, Leopoldo Gutfleisch, Oliver Zhang, Hongbin Xu, Bai-Xiang Materials Science Disordered Systems and Neural Networks Computational Physics As a pinning-controlled permanent magnet, tailoring the cellular nanostructure of samarium-cobalt-based 1:7-type (SmCo-1:7) magnets remains crucial for improving magnetic performance. Jointing forward and inverse machine learning models with the high-throughput micromagnetic simulations (42,300 runs), we identify the nanostructural and magnetic features that are most effective for coercivity, combining both nucleation and pinning mechanisms. Sensitivity analyses reveal that the 1:5-phase enhances coercivity by providing high anisotropy, and the Z-phase strengthens pinning through fluctuations in domain wall energy. Cu additions in the 1:5-phase significantly reduce coercivity, while Fe substitutions in the 2:17-phase modestly reduce coercivity but improve pinning locally and increase saturation magnetization. Among all examined features, magnetocrystalline misorientation emerges as the dominant factor. Finally, the framework enables the inverse design of nanostructures with prescribed coercivity, demonstrating a computationally cost-effective toolkit for guiding the performance tailoring of SmCo-1:7 magnets. |
| title | Coercivity influence of nanostructure in SmCo-1:7 magnets: Machine learning of high-throughput micromagnetic data |
| topic | Materials Science Disordered Systems and Neural Networks Computational Physics |
| url | https://arxiv.org/abs/2408.03198 |