Year: 2025
Type: article
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.
Source: npj Computational Materials
Authors: Aileen Luo, Tao Zhou, Ming Du, Martin V. Holt, Andrej Singer, Mathew J. Cherukara
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,490
Awards: G102163711, G1055550527, G107546177, G1286251239, G1313983767, G1538770391, G1664757510, G17040919, G2287611682, G2587618039, G262962599, G2917895714, G3075337988, CREATING NEW QUANTUM STATES OF MATTER IN TIME AND SPACE THROUGH ENGINEERING ARTIFICIAL INTERFACES AND STRUCTURES, G426311843, G473123490, G473396326, G498139845, G5387317844, G5398989114, G601061201, G6017181652, G6089394659, G6371315619, G6848031779, G6918803902, G7583137537, G7619220232, G8053029320, G8570346810, G878601127, G969889393, G993149817
OpenAlex ID: W4417399154