Constrained Diffusion for Accelerated Structure Relaxation of Inorganic Solids with Point Defects
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912917795897344 |
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| author | Cui, Jingyi Christopher, Jacob K. Biswas, Ankita Balachandran, Prasanna V. Fioretto, Ferdinando |
| author_facet | Cui, Jingyi Christopher, Jacob K. Biswas, Ankita Balachandran, Prasanna V. Fioretto, Ferdinando |
| contents | Point defects affect material properties by altering electronic states and modifying local bonding environments. However, high-throughput first-principles simulations of point defects are costly due to large simulation cells and complex energy landscapes. To this end, we propose a generative framework for simulating point defects, overcoming the limits of costly first-principles simulators. By leveraging a primal-dual algorithm, we introduce a constraint-aware diffusion model which outperforms existing constrained diffusion approaches in this domain. Across six defect configuration settings for Bi2Te3, the proposed approach provides state-of-the-art performance generating physically grounded structures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19153 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Constrained Diffusion for Accelerated Structure Relaxation of Inorganic Solids with Point Defects Cui, Jingyi Christopher, Jacob K. Biswas, Ankita Balachandran, Prasanna V. Fioretto, Ferdinando Materials Science Artificial Intelligence Machine Learning Point defects affect material properties by altering electronic states and modifying local bonding environments. However, high-throughput first-principles simulations of point defects are costly due to large simulation cells and complex energy landscapes. To this end, we propose a generative framework for simulating point defects, overcoming the limits of costly first-principles simulators. By leveraging a primal-dual algorithm, we introduce a constraint-aware diffusion model which outperforms existing constrained diffusion approaches in this domain. Across six defect configuration settings for Bi2Te3, the proposed approach provides state-of-the-art performance generating physically grounded structures. |
| title | Constrained Diffusion for Accelerated Structure Relaxation of Inorganic Solids with Point Defects |
| topic | Materials Science Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2602.19153 |