Kavli Affiliate: Xian Chen
| First 5 Authors: Ka Hung Chan, Xinyue Huang, Nobumichi Tamura, Xian Chen,
| Summary:
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 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 CNN 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 micron-sized probes.
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