Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning

2026-05-12

AI Summary

This study addresses the challenge of directly observing the slow, nonequilibrium dynamics of grain boundaries in nanocrystalline materials. By innovatively integrating X-ray photon correlation spectroscopy (XPCS), continuum modeling, and domain-adaptive semi-supervised machine learning, the authors achieve the first quantitative extraction of grain boundary kinetic parameters from high-dimensional, noisy experimental data. Through domain-adaptive representation alignment that bridges simulated and experimental XPCS signals, key parameters—including bulk diffusivity, grain boundary stiffness, and effective grain boundary concentration—are successfully resolved. This approach reveals the intrinsic, long-lived deviation of grain boundary relaxation processes from thermodynamic equilibrium, offering new insights into the nonequilibrium nature of nanoscale microstructural evolution.

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.

Problem

• grain boundary dynamics
• non-equilibrium
• XPCS
• quantitative probing
• nanocrystalline materials

Innovation

• XPCS
• domain-adaptive machine learning
• grain boundary dynamics
• non-equilibrium materials
• semi-supervised learning