{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/modeling-crystal-defects-using-defect","title":"Modeling crystal defects using defect-informed neural networks","arxiv_id":"2503.15391","date":"2025-03-19","proceeding":null,"authors":["Ziduo Yang","Xiaoqing Liu","Xiuying Zhang","Pengru Huang","Kostya S. Novoselov","Lei Shen"],"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.","url_abs":"https://arxiv.org/abs/2503.15391v2","url_pdf":"https://arxiv.org/pdf/2503.15391v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"modeling-crystal-defects-using-defect","repo_url":"https://github.com/Shen-Group/DefiNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}