High-throughput computation and machine learning modeling of magnetic moments and Mössbauer parameters for Fe-based intermetallics
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908275833831424 |
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| author | Zhao, Bo Zhang, Hongbin |
| author_facet | Zhao, Bo Zhang, Hongbin |
| contents | Based on high-throughput density functional theory calculations, we evaluate the local magnetic moments and Mössbauer properties for Fe-based intermetallic compounds and employ machine learning to map the local crystalline environments to such properties. It is observed that magnetic moments and Mössbauer parameters provide complementary insights into the local crystalline environment, where the statistical features cannot be captured using phenomenological models. Furthermore, we find that the scarcity of existing data in Materials Project (MP) poses a significant challenge in developing predictive machine learning models, whereas SOAP-based descriptors can be applied for reliable modeling of the enriched datasets with extra structural prototypes. This work advances the mapping of local crystalline structures to magnetic and spectroscopic properties, bridging the gap between empirical observations and theoretical models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08249 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-throughput computation and machine learning modeling of magnetic moments and Mössbauer parameters for Fe-based intermetallics Zhao, Bo Zhang, Hongbin Materials Science Based on high-throughput density functional theory calculations, we evaluate the local magnetic moments and Mössbauer properties for Fe-based intermetallic compounds and employ machine learning to map the local crystalline environments to such properties. It is observed that magnetic moments and Mössbauer parameters provide complementary insights into the local crystalline environment, where the statistical features cannot be captured using phenomenological models. Furthermore, we find that the scarcity of existing data in Materials Project (MP) poses a significant challenge in developing predictive machine learning models, whereas SOAP-based descriptors can be applied for reliable modeling of the enriched datasets with extra structural prototypes. This work advances the mapping of local crystalline structures to magnetic and spectroscopic properties, bridging the gap between empirical observations and theoretical models. |
| title | High-throughput computation and machine learning modeling of magnetic moments and Mössbauer parameters for Fe-based intermetallics |
| topic | Materials Science |
| url | https://arxiv.org/abs/2503.08249 |