Modeling crystal defects using defect informed neural networks
Abstract Most AI-for-Materials research to date has focused on ideal crystals, whereas real-world materials inevitably contain defects that play a critical role in modern functional technologies. The defects break geometric symmetry and increase interaction complexity, posing particular challenges f...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01728-w |
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| _version_ | 1849763955796344832 |
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| author | Ziduo Yang Xiaoqing Liu Xiuying Zhang Pengru Huang Kostya S. Novoselov Lei Shen |
| author_facet | Ziduo Yang Xiaoqing Liu Xiuying Zhang Pengru Huang Kostya S. Novoselov Lei Shen |
| author_sort | Ziduo Yang |
| collection | DOAJ |
| description | Abstract Most AI-for-Materials research to date has focused on ideal crystals, whereas real-world materials inevitably contain defects that play a critical role in modern functional technologies. The defects break geometric symmetry and increase interaction complexity, posing particular challenges for traditional ML models. Here, we introduce Defect-Informed Equivariant Graph Neural Network (DefiNet), a model specifically designed to accurately capture defect-related interactions and geometric configurations in point-defect structures. DefiNet achieves near-DFT-level structural predictions in milliseconds using a single GPU. To validate its accuracy, we perform DFT relaxations using DefiNet-predicted structures as initial configurations and measure the residual ionic steps. For most defect structures, regardless of defect complexity or system size, only 3 ionic steps are required to reach the DFT-level ground state. Finally, comparisons with scanning transmission electron microscopy (STEM) images confirm DefiNet’s scalability and extrapolation beyond point defects, positioning it as a valuable tool for defect-focused materials research. |
| format | Article |
| id | doaj-art-6f17b29ad93449e7a2ae44ff034471d8 |
| institution | DOAJ |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-6f17b29ad93449e7a2ae44ff034471d82025-08-20T03:05:15ZengNature Portfolionpj Computational Materials2057-39602025-07-0111111210.1038/s41524-025-01728-wModeling crystal defects using defect informed neural networksZiduo Yang0Xiaoqing Liu1Xiuying Zhang2Pengru Huang3Kostya S. Novoselov4Lei Shen5Department of Electronic Engineering, College of Information Science and Technology, Jinan UniversityDepartment of Mechanical Engineering, National University of SingaporeDepartment of Mechanical Engineering, National University of SingaporeInstitute for Functional Intelligent Materials, National University of SingaporeInstitute for Functional Intelligent Materials, National University of SingaporeDepartment of Mechanical Engineering, National University of SingaporeAbstract Most AI-for-Materials research to date has focused on ideal crystals, whereas real-world materials inevitably contain defects that play a critical role in modern functional technologies. The defects break geometric symmetry and increase interaction complexity, posing particular challenges for traditional ML models. Here, we introduce Defect-Informed Equivariant Graph Neural Network (DefiNet), a model specifically designed to accurately capture defect-related interactions and geometric configurations in point-defect structures. DefiNet achieves near-DFT-level structural predictions in milliseconds using a single GPU. To validate its accuracy, we perform DFT relaxations using DefiNet-predicted structures as initial configurations and measure the residual ionic steps. For most defect structures, regardless of defect complexity or system size, only 3 ionic steps are required to reach the DFT-level ground state. Finally, comparisons with scanning transmission electron microscopy (STEM) images confirm DefiNet’s scalability and extrapolation beyond point defects, positioning it as a valuable tool for defect-focused materials research.https://doi.org/10.1038/s41524-025-01728-w |
| spellingShingle | Ziduo Yang Xiaoqing Liu Xiuying Zhang Pengru Huang Kostya S. Novoselov Lei Shen Modeling crystal defects using defect informed neural networks npj Computational Materials |
| title | Modeling crystal defects using defect informed neural networks |
| title_full | Modeling crystal defects using defect informed neural networks |
| title_fullStr | Modeling crystal defects using defect informed neural networks |
| title_full_unstemmed | Modeling crystal defects using defect informed neural networks |
| title_short | Modeling crystal defects using defect informed neural networks |
| title_sort | modeling crystal defects using defect informed neural networks |
| url | https://doi.org/10.1038/s41524-025-01728-w |
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