Ab-initio Crystal Structure Determination from Powder X-Ray Diffraction
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866913159300775936 |
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| author | Su, Kaixiang Ridwan, Osman Goni Xue, Hongfei Zhu, Qiang |
| author_facet | Su, Kaixiang Ridwan, Osman Goni Xue, Hongfei Zhu, Qiang |
| contents | Determining crystal structures from powder X-ray diffraction (PXRD) has been a significant challenge in materials science, particularly when experimental data contain noise or the target structure has a high complexity. While recent AI generative models show promise for rapid structure generation, they predominantly employ data-driven approaches to learn direct mappings between PXRD patterns and crystal structures, often failing on complex or out-of-distribution cases. In this work, we present a hybrid ab-initio approach that decomposes structure determination into a two-stage optimization problem: (1) discrete selection of space group symmetry, unit cell parameters, and Wyckoff site combinations; and (2) continuous optimization of atomic coordinates within the selected Wyckoff positions. By integrating AI-based techniques for peak profile analysis, density estimation and energy minimization with physics-informed constraints, our method systematically overcomes limitations of purely data-driven PXRD solvers. We demonstrate that this hierarchical optimization framework enables robust structure determination even for challenging cases with high structural complexity or limited experimental data quality. Our approach provides a principled pathway for incorporating crystallographic knowledge into AI models for more reliable and generalizable crystal structure determination. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24594 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Ab-initio Crystal Structure Determination from Powder X-Ray Diffraction Su, Kaixiang Ridwan, Osman Goni Xue, Hongfei Zhu, Qiang Materials Science Determining crystal structures from powder X-ray diffraction (PXRD) has been a significant challenge in materials science, particularly when experimental data contain noise or the target structure has a high complexity. While recent AI generative models show promise for rapid structure generation, they predominantly employ data-driven approaches to learn direct mappings between PXRD patterns and crystal structures, often failing on complex or out-of-distribution cases. In this work, we present a hybrid ab-initio approach that decomposes structure determination into a two-stage optimization problem: (1) discrete selection of space group symmetry, unit cell parameters, and Wyckoff site combinations; and (2) continuous optimization of atomic coordinates within the selected Wyckoff positions. By integrating AI-based techniques for peak profile analysis, density estimation and energy minimization with physics-informed constraints, our method systematically overcomes limitations of purely data-driven PXRD solvers. We demonstrate that this hierarchical optimization framework enables robust structure determination even for challenging cases with high structural complexity or limited experimental data quality. Our approach provides a principled pathway for incorporating crystallographic knowledge into AI models for more reliable and generalizable crystal structure determination. |
| title | Ab-initio Crystal Structure Determination from Powder X-Ray Diffraction |
| topic | Materials Science |
| url | https://arxiv.org/abs/2605.24594 |