2026-08-10 アルゴンヌ国立研究所（ANL）
DONUT makes it easier to cut through layers of complex data and reach the sweet spot of scientific discovery, turning X-ray measurements into real-time insights for researchers at the APS. (Image by ChatGPT.)
＜関連情報＞
- https://www.anl.gov/article/a-taste-of-discovery-realtime-xray-data-analysis-with-donut-accelerates-materials-science-at-argonne
- https://www.nature.com/articles/s41524-025-01860-7
DONUT：リアルタイムX線ナノ回折解析のための物理法則を考慮した機械学習 DONUT: physics-aware machine learning for real-time X-ray nanodiffraction analysis
Aileen Luo,Tao Zhou,Ming Du,Martin V. Holt,Andrej Singer & Mathew J. Cherukara
npj Communications Materials Published:16 December 2025
DOI:https://doi.org/10.1038/s41524-025-01860-7
Abstract
Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experiments more accessible, real-time analysis remains a significant bottleneck, often hindered by artifacts and computational demands. In scanning X-ray nanodiffraction microscopy, which is widely used to spatially resolve structural heterogeneities, this challenge is compounded by the convolution of the divergent beam with the sample’s local structure. To address this, we introduce DONUT (Diffraction with Optics for Nanobeam by Unsupervised Training), a physics-aware neural network designed for the rapid and automated analysis of nanobeam diffraction data. By incorporating a differentiable geometric diffraction model directly into its architecture, DONUT learns to predict crystal lattice strain and orientation in real-time. Crucially, this is achieved without reliance on labeled datasets or pre-training, overcoming a fundamental limitation for supervised machine learning in X-ray science. We demonstrate experimentally that DONUT accurately extracts all features within the data over 200 times more efficiently than conventional fitting methods.
