arXiv subjects

Qiwei Wan

Publications and source records attributed to Qiwei Wan.

3 recordsLinked to original sources

arXiv2026

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

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.

cond-mat.mtrl-sci↗

arXiv2025

Reinforcement learning-guided optimization of critical current in high-temperature superconductors

High-temperature superconductors are essential for next-generation energy and quantum technologies, yet their performance is often limited by the critical current density ($J_c$), which is strongly influenced by microstructural defects. Optimizing $J_c$ through defect engineering is challenging due to the complex interplay of defect type, density, and spatial correlation. Here we present an integrated workflow that combines reinforcement learning (RL) with time-dependent Ginzburg-Landau (TDGL) simulations to autonomously identify optimal defect configurations that maximize $J_c$. In our framework, TDGL simulations generate current-voltage characteristics to evaluate $J_c$, which serves as the reward signal that guides the RL agent to iteratively refine defect configurations. We find that the agent discovers optimal defect densities and correlations in two-dimensional thin-film geometries, enhancing vortex pinning and $J_c$ relative to the pristine thin-film, approaching 60\% of theoretical depairing limit with up to 15-fold enhancement compared to random initialization. This RL-driven approach provides a scalable strategy for defect engineering, with broad implications for advancing HTS applications in fusion magnets, particle accelerators, and other high-field technologies.

cond-mat.mtrl-sci↗

arXiv2025

The impact of dimensionality on universality of quantum Hall transitions

Regardless of model and platform details, the critical phenomena exhibit universal behaviors that are remarkably consistent across various experiments and theories, resulting in a significant scientific success of condensed matter physics. One widely known and commonly used example is the 2D quantum Hall transition; yet, its universal exponents still somewhat conflict between experiments, theoretical models, and numerical ansatzes. We study critical behaviors of quasi-2D Weyl semimetal systems with a finite thickness $L_z>1$, disorder, and external magnetic field $B_z$. By analyzing the scaling behaviors of the localization lengths and local density of states using recursive methods, we find that the finite thickness yields a deviation from the 2D quantum Hall universality ($L_z=1$ case) and a crossover toward the 3D Gaussian Unitary Ensemble ($L_z\rightarrow \infty$ limit), potentially offering another cause of the discrepancy. Our work demonstrates the often-overlooked importance of auxiliary degrees of freedom, such as thickness, and that 3D quantum Hall physics is not merely a trivial finite-thickness extension of its 2D counterpart.

cond-mat.mes-hall↗