XDXD: End-to-end crystal structure determination with low resolution X-ray diffraction
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911222695198720 |
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| author | Zhao, Jiale Liu, Cong Zhang, Yuxuan Gong, Chengyue Zhang, Zhenyi Jin, Shifeng Liu, Zhenyu |
| author_facet | Zhao, Jiale Liu, Cong Zhang, Yuxuan Gong, Chengyue Zhang, Zhenyi Jin, Shifeng Liu, Zhenyu |
| contents | Determining crystal structures from X-ray diffraction data is fundamental across diverse scientific fields, yet remains a significant challenge when data is limited to low resolution. While recent deep learning models have made breakthroughs in solving the crystallographic phase problem, the resulting low-resolution electron density maps are often ambiguous and difficult to interpret. To overcome this critical bottleneck, we introduce XDXD, to our knowledge, the first end-to-end deep learning framework to determine a complete atomic model directly from low-resolution single-crystal X-ray diffraction data. Our diffusion-based generative model bypasses the need for manual map interpretation, producing chemically plausible crystal structures conditioned on the diffraction pattern. We demonstrate that XDXD achieves a 70.4\% match rate for structures with data limited to 2.0~Å resolution, with a root-mean-square error (RMSE) below 0.05. Evaluated on a benchmark of 24,000 experimental structures, our model proves to be robust and accurate. Furthermore, a case study on small peptides highlights the model's potential for extension to more complex systems, paving the way for automated structure solution in previously intractable cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17936 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | XDXD: End-to-end crystal structure determination with low resolution X-ray diffraction Zhao, Jiale Liu, Cong Zhang, Yuxuan Gong, Chengyue Zhang, Zhenyi Jin, Shifeng Liu, Zhenyu Materials Science Artificial Intelligence Machine Learning Determining crystal structures from X-ray diffraction data is fundamental across diverse scientific fields, yet remains a significant challenge when data is limited to low resolution. While recent deep learning models have made breakthroughs in solving the crystallographic phase problem, the resulting low-resolution electron density maps are often ambiguous and difficult to interpret. To overcome this critical bottleneck, we introduce XDXD, to our knowledge, the first end-to-end deep learning framework to determine a complete atomic model directly from low-resolution single-crystal X-ray diffraction data. Our diffusion-based generative model bypasses the need for manual map interpretation, producing chemically plausible crystal structures conditioned on the diffraction pattern. We demonstrate that XDXD achieves a 70.4\% match rate for structures with data limited to 2.0~Å resolution, with a root-mean-square error (RMSE) below 0.05. Evaluated on a benchmark of 24,000 experimental structures, our model proves to be robust and accurate. Furthermore, a case study on small peptides highlights the model's potential for extension to more complex systems, paving the way for automated structure solution in previously intractable cases. |
| title | XDXD: End-to-end crystal structure determination with low resolution X-ray diffraction |
| topic | Materials Science Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.17936 |