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Hongrui He

Publications and source records attributed to Hongrui He.

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Dual migration modes of unfaulted disconnections on curved twin boundaries

Grain boundary migration governs microstructural evolution in crystalline materials, directly influencing mechanical properties such as strength and thermal stability. Disconnections, which are line defects formed at grain boundaries in response to local curvature, have been identified as critical carriers of boundary migration. Here, we investigate the glide of unfaulted disconnections (UFDs) on a coherent twin boundary in aluminum at elevated temperatures using molecular dynamics simulations combined with the Nudged Elastic Band (NEB) method. Our results reveal a striking bifurcation in migration behavior depending on the disconnection core structure. UFDs with a pure edge Burgers vector migrate via a thermally activated double-kink mechanism, exhibiting a migration velocity that increases monotonically with temperature. In contrast, UFDs containing a screw dipole component possess an energy barrier approximately eight times lower, and their core structure undergoes a continuous transformation during glide, giving rise to stochastic, bidirectional motion with no systematic temperature dependence. These findings demonstrate that the disconnection core structure fundamentally dictates the migration mode and kinetics of twin boundaries, offering new mechanistic insights into disconnection-mediated grain boundary migration.

cond-mat.mtrl-sci

Elucidation of Relaxation Dynamics Beyond Equilibrium Through AI-informed X-ray Photon Correlation Spectroscopy

Understanding and interpreting dynamics of functional materials \textit{in situ} is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales, however spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work we have developed an unsupervised deep learning (DL) framework for automated classification and interpretation of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system behavior. We demonstrate how this method can be used to rapidly explore large datasets to identify samples of interest, and we apply this approach to directly correlate bulk properties of a model system to microscopic dynamics. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery.

cond-mat.mtrl-sci