Modeling crystal defects using defect-informed neural networks
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908389168119808 |
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| author | Yang, Ziduo Liu, Xiaoqing Zhang, Xiuying Huang, Pengru Novoselov, Kostya S. Shen, Lei |
| author_facet | Yang, Ziduo Liu, Xiaoqing Zhang, Xiuying Huang, Pengru Novoselov, Kostya S. Shen, Lei |
| contents | 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 | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15391 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling crystal defects using defect-informed neural networks Yang, Ziduo Liu, Xiaoqing Zhang, Xiuying Huang, Pengru Novoselov, Kostya S. Shen, Lei Materials Science Computational Physics 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. |
| title | Modeling crystal defects using defect-informed neural networks |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2503.15391 |