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.

通过对 PXRD 模式进行匹配和生成，XtalNet 是一种基于深度学习的全流程框架，能够准确匹配给定 PXRD 模式的晶体结构。