Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Journal of Applied Crystallography

DOI: 10.1107/s160057672500826x

Authors: Ka Hung Chan, Xinyue Huang, Nobumichi Tamura, Xian Chen

Institutions: Lawrence Berkeley National Laboratory, Hong Kong University of Science and Technology, Advanced Light Source

Language: English

Topic: X-ray Diffraction in Crystallography

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $5,110

Funders: U.S. Department of Energy, Research Grants Council, University Grants Committee, Office of Science, Advanced Light Source