End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction
CoRR(2024)
摘要
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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