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Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning
Mouyang Chen · 2026-05-13 · via cs.LG updates on arXiv.org

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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.
Comments: 14 pages, 4 figures
Subjects: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
Cite as: arXiv:2605.12194 [cond-mat.mtrl-sci]
  (or arXiv:2605.12194v1 [cond-mat.mtrl-sci] for this version)
  https://doi.org/10.48550/arXiv.2605.12194

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Mouyang Cheng [view email]
[v1] Tue, 12 May 2026 14:37:17 UTC (8,099 KB)

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