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arXiv · 2503.15391

Modeling crystal defects using defect-informed neural networks

Ziduo Yang·Xiaoqing Liu·Xiuying Zhang·Pengru Huang·Kostya S. Novoselov·Lei Shen
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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.

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Ziduo Yang, Xiaoqing Liu, Xiuying Zhang, Pengru Huang, Kostya S. Novoselov, Lei Shen. 2025-03-19. Modeling crystal defects using defect-informed neural networks. https://arxiv.org/abs/2503.15391

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