Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning
Organizations: Quantum Measurement Group, MIT, Cambridge, MA 02139, USA · Center for Computational Science and Engineering, MIT, Cambridge, MA 02139, USA · Department of Materials Science and Engineering, MIT, Cambridge, MA 02139, USA · Department of Physics, MIT, Cambridge, MA 02139, USA · Department of Nuclear Science and Engineering, MIT, Cambridge, MA 02139, USA · Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL 60439, USA · Materials Science Institute, University of Oregon, Eugene, OR 97403, USA · Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA · Department of Applied Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA · National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY 11973, USA · Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA · SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
Abstract
Grain-boundary (GB) dynamics control the stability, mechanical, and functional response of nanocrystalline materials, but direct experimental access to their slow non-equilibrium motion has been limited. Here we establish X-ray photon correlation spectroscopy (XPCS), combined with domain-adaptive machine learning, as a quantitative probe of GB dynamics. Temperature- and grain-size-dependent two-time XPCS measurements in nanocrystalline silicon reveal pronounced departures from time-translation invariance, showing that GB relaxation can remain far from equilibrium over experimental timescales. However, direct extraction of quantitative physical information from these high-dimensional, noisy fluctuation maps faces a significant challenge. To overcome this barrier, we develop a semi-supervised learning framework that transfers physical parameter labels from continuum simulations to unlabeled experimental XPCS maps through domain-adaptive representation alignment. This AI-augmented approach enables the extraction of key kinetic parameters, including bulk diffusivity, GB stiffness, and effective GB concentration, directly from experimental XPCS measurements. Our results show how machine learning can transform indirect fluctuation signals into quantitative materials dynamics, providing a general route to study non-equilibrium defect motion in solids.