用XPCS和自适应机器学习，实测纳米硅晶界非平衡运动
Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning
- 结合XPCS与域自适应机器学习，从实验数据中提取动态参数
- 发现晶界弛豫在实验时间尺度上长期偏离平衡态
- 适合材料动力学、计算材料学与人工智能交叉研究者
晶界（GB）动力学决定纳米晶材料的稳定性、力学与功能响应，但其缓慢的非平衡运动一直缺乏直接实验观测。本文建立基于X射线光子相关光谱（XPCS）与域自适应机器学习的定量探测方法。在纳米晶硅中进行温度与晶粒尺寸依赖的双时间XPCS测量，揭示显著偏离时间平移不变性，表明晶界弛豫可在实验时间尺度上长期远离平衡。然而，从高维噪声波动图中直接提取物理信息面临巨大挑战。为此，我们提出半监督学习框架，通过域自适应表示对齐，将连续体模拟中的物理参数标签迁移至未标注的实验XPCS图。该AI增强方法实现了从实验XPCS直接提取关键动力学参数，包括体扩散系数、晶界刚度及有效晶界浓度。结果表明，机器学习可将间接波动信号转化为定量材料动力学信息，为固体中非平衡缺陷运动研究提供通用路径。
原文摘要 · Abstract (English)
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.
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