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

Our work presents a physics-informed machine learning approach that advances materials characterization by overcoming the fundamental resolution limits of synchrotron X-ray Laue microdiffraction. By integrating deep learning with physical constraints, we demonstrate that nanoscale grain mapping can be achieved even when using micrometre-sized X-ray probes.