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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2605.16230 |
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| _version_ | 1866913132994101248 |
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| author | Chotrattanapituk, Abhijatmedhi Okabe, Ryotaro Rha, Eunbi Al-Hinai, Mariya Jiang, Eugene Pajerowski, Daniel Cheng, Yongqiang Turner, Joshua J. Li, Mingda |
| author_facet | Chotrattanapituk, Abhijatmedhi Okabe, Ryotaro Rha, Eunbi Al-Hinai, Mariya Jiang, Eugene Pajerowski, Daniel Cheng, Yongqiang Turner, Joshua J. Li, Mingda |
| contents | Magnetic order is a fundamental property of materials, governing collective behavior and enabling a broad range of functionalities. Yet magnetic structure remains difficult to determine: experiments are costly and specialized, while first-principles methods often struggle with the noncollinear and incommensurate orders found in real materials. Here we introduce magnetic structure network (MSN), an E(3) equivariant graph neural network that predicts both collinear and non-collinear magnetic structures directly from atomic crystal structures, trained directly on experimentally determined structures from MAGNDATA. By proposing the primitive modulated structure representation (PMSR), we are able to encode commensurate and incommensurate structures in a unified way without symmetry assumptions. The model achieves strong performance across all modulation components and reconstructs experimental magnetic structures with high fidelity. Our approach provides a scalable framework for rapid magnetic structure prediction and opens a route to data-driven discovery of magnetic materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16230 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy Chotrattanapituk, Abhijatmedhi Okabe, Ryotaro Rha, Eunbi Al-Hinai, Mariya Jiang, Eugene Pajerowski, Daniel Cheng, Yongqiang Turner, Joshua J. Li, Mingda Materials Science Machine Learning Magnetic order is a fundamental property of materials, governing collective behavior and enabling a broad range of functionalities. Yet magnetic structure remains difficult to determine: experiments are costly and specialized, while first-principles methods often struggle with the noncollinear and incommensurate orders found in real materials. Here we introduce magnetic structure network (MSN), an E(3) equivariant graph neural network that predicts both collinear and non-collinear magnetic structures directly from atomic crystal structures, trained directly on experimentally determined structures from MAGNDATA. By proposing the primitive modulated structure representation (PMSR), we are able to encode commensurate and incommensurate structures in a unified way without symmetry assumptions. The model achieves strong performance across all modulation components and reconstructs experimental magnetic structures with high fidelity. Our approach provides a scalable framework for rapid magnetic structure prediction and opens a route to data-driven discovery of magnetic materials. |
| title | Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2605.16230 |