Powder diffraction crystal structure determination using generative models

Qi Li, Rui Jiao, Liming Wu, Tiannian Zhu, Wenbing Huang (), Shifeng Jin (), Yang Liu, Hongming Weng and Xiaolong Chen
Additional contact information
Qi Li: Chinese Academy of Sciences
Rui Jiao: Tsinghua University
Liming Wu: Renmin University of China
Tiannian Zhu: Chinese Academy of Sciences
Wenbing Huang: Renmin University of China
Shifeng Jin: Chinese Academy of Sciences
Yang Liu: Tsinghua University
Hongming Weng: Chinese Academy of Sciences
Xiaolong Chen: Chinese Academy of Sciences

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: Abstract Accurate crystal structure determination is critical across all scientific disciplines involving crystalline materials. However, solving and refining crystal structures from powder X-ray diffraction (PXRD) data is traditionally a labor-intensive process that demands substantial expertise. Here we introduce PXRDGen, an end-to-end neural network that determines crystal structures by learning joint structural distributions from experimentally stable crystals and their PXRD, producing atomically accurate structures refined through PXRD data. PXRDGen integrates a pretrained XRD encoder, a diffusion/flow-based structure generator, and a Rietveld refinement module, solving structures with unparalleled accuracy in seconds. Evaluation on MP-20 dataset reveals a record high matching rate of 82% (1-sample) and 96% (20-samples) for valid compounds, with Root Mean Square Error (RMSE) approaching the precision limits of Rietveld refinement. PXRDGen effectively tackles key challenges in PXRD, such as the resolution of overlapping peaks, localization of light atoms, and differentiation of neighboring elements.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-62708-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-62708-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-62708-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().