Physics-informed machine learning analysis for nanoscale grain mapping by synchrotron Laue microdiffraction

Journal Article · Mon Dec 01 00:00:00 EST 2025 · Journal of Applied Crystallography

DOI:https://doi.org/10.1107/s160057672500826x· OSTI ID:3013078

Chan, Ka Hung; Huang, Xinyue; Tamura, Nobumichi; Chen, Xian

Understanding the grain morphology, orientation distribution and crystal structure of nanocrystals is essential for optimizing the mechanical and physical properties of functional materials. Synchrotron X-ray Laue microdiffraction is a powerful technique for characterizing crystal structures and orientation mapping using focused X-rays. However, when the grain sizes are smaller than the beam size, mixed peaks in the Laue pattern from neighboring grains limit the resolution of grain morphology mapping. We propose a physics-informed machine learning (PIML) approach that combines a convolutional neural network feature extractor with a physics-informed filtering algorithm to overcome the spatial resolution limits of X-rays, achieving nanoscale resolution for grain mapping. Our PIML method successfully resolves the grain size, orientation distribution and morphology of Au nanocrystals through synchrotron microdiffraction scans, showing good agreement with electron backscatter diffraction results. This PIML-assisted synchrotron microdiffraction analysis can be generalized to other diffraction-based probes, enabling the characterization of nanosized structures with micrometre-sized probes.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: US Department of Energy; USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22), Scientific User Facilities Division (SC-22.3 )
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 3013078
• Journal Information: Journal of Applied Crystallography, Journal Name: Journal of Applied Crystallography Journal Issue: Pt 6 Vol. 58
• Country of Publication: United States
• Language: English