Representing Born effective charges with equivariant graph convolutional neural networks
Abstract Graph convolutional neural networks have been instrumental in machine learning of material properties. When representing tensorial properties, weights and descriptors of a physics-informed network must obey certain transformation rules to ensure the independence of the property on the choic...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01250-5 |
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| _version_ | 1849326731988566016 |
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| author | Alex Kutana Koji Shimizu Satoshi Watanabe Ryoji Asahi |
| author_facet | Alex Kutana Koji Shimizu Satoshi Watanabe Ryoji Asahi |
| author_sort | Alex Kutana |
| collection | DOAJ |
| description | Abstract Graph convolutional neural networks have been instrumental in machine learning of material properties. When representing tensorial properties, weights and descriptors of a physics-informed network must obey certain transformation rules to ensure the independence of the property on the choice of the reference frame. Here we explicitly encode such properties using an equivariant graph convolutional neural network. The network respects rotational symmetries of the crystal throughout by using equivariant weights and descriptors and provides a tensorial output of the target value. Applications to tensors of atomic Born effective charges in diverse materials including perovskite oxides, Li3PO4, and ZrO2, are demonstrated, and good performance and generalization ability is obtained. |
| format | Article |
| id | doaj-art-b33f8e113a5e408f96803ebfe804df64 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b33f8e113a5e408f96803ebfe804df642025-08-20T03:48:05ZengNature PortfolioScientific Reports2045-23222025-05-0115111010.1038/s41598-025-01250-5Representing Born effective charges with equivariant graph convolutional neural networksAlex Kutana0Koji Shimizu1Satoshi Watanabe2Ryoji Asahi3Nagoya UniversityNational Institute of Advanced Industrial Science and Technology (AIST)The University of TokyoNagoya UniversityAbstract Graph convolutional neural networks have been instrumental in machine learning of material properties. When representing tensorial properties, weights and descriptors of a physics-informed network must obey certain transformation rules to ensure the independence of the property on the choice of the reference frame. Here we explicitly encode such properties using an equivariant graph convolutional neural network. The network respects rotational symmetries of the crystal throughout by using equivariant weights and descriptors and provides a tensorial output of the target value. Applications to tensors of atomic Born effective charges in diverse materials including perovskite oxides, Li3PO4, and ZrO2, are demonstrated, and good performance and generalization ability is obtained.https://doi.org/10.1038/s41598-025-01250-5Equivariant graph convolutional neural networksPhysics-informed neural networksTensor of atomic Born effective chargesLinear responseOxides |
| spellingShingle | Alex Kutana Koji Shimizu Satoshi Watanabe Ryoji Asahi Representing Born effective charges with equivariant graph convolutional neural networks Scientific Reports Equivariant graph convolutional neural networks Physics-informed neural networks Tensor of atomic Born effective charges Linear response Oxides |
| title | Representing Born effective charges with equivariant graph convolutional neural networks |
| title_full | Representing Born effective charges with equivariant graph convolutional neural networks |
| title_fullStr | Representing Born effective charges with equivariant graph convolutional neural networks |
| title_full_unstemmed | Representing Born effective charges with equivariant graph convolutional neural networks |
| title_short | Representing Born effective charges with equivariant graph convolutional neural networks |
| title_sort | representing born effective charges with equivariant graph convolutional neural networks |
| topic | Equivariant graph convolutional neural networks Physics-informed neural networks Tensor of atomic Born effective charges Linear response Oxides |
| url | https://doi.org/10.1038/s41598-025-01250-5 |
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