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

TL;DR

This paper introduces a novel combination of X-ray photon correlation spectroscopy and domain-adaptive machine learning to quantitatively analyze non-equilibrium grain boundary dynamics in nanocrystalline materials.

Contribution

It develops a semi-supervised learning framework that transfers simulation-based physical labels to experimental data, enabling direct extraction of kinetic parameters from noisy XPCS measurements.

Findings

Revealed non-equilibrium grain boundary relaxation in nanocrystalline silicon.

Successfully extracted diffusivity, GB stiffness, and concentration from experimental data.

Demonstrated machine learning's potential to quantify complex materials dynamics.

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.