End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction

Qingsi Lai,Lin Yao,Zhifeng Gao, Siyuan Liu, Hongshuai Wang,Shuqi Lu,Di He,Liwei Wang,Cheng Wang,Guolin Ke


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Powder X-ray diffraction (PXRD) is a crucial means for crystal structure determination. Such determination often involves external database matching to find a structural analogue and Rietveld refinement to obtain finer structure. However, databases may be incomplete and Rietveld refinement often requires intensive trial-and-error efforts from trained experimentalists, which remains ineffective in practice. To settle these issues, we propose XtalNet, the first end-to-end deep learning-based framework capable of ab initio generation of crystal structures that accurately match given PXRD patterns. The model employs contrastive learning and Diffusion-based conditional generation to enable the simultaneous execution of two tasks: crystal structure retrieval based on PXRD patterns and conditional structure generations. To validate the effectiveness of XtalNet, we curate a much more challenging and practical dataset hMOF-100, XtalNet performs well on this dataset, reaching 96.3% top-10 hit ratio on the database retrieval task and 95.0% top-10 match rate on the ranked structure generation task.
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