arXiv subjects

Xiaobing Hu

Publications and source records attributed to Xiaobing Hu.

8 recordsLinked to original sources

arXiv2026

Physics-Informed Neural Networks for Sparse Strain-Field Reconstruction in 4D-STEM

Quantitative strain mapping using four-dimensional scanning transmission electron microscopy (4D-STEM) typically requires densely sampled scans that can damage beam-sensitive specimens. We develop a physics-informed neural network (PINN) for sparse 4D-STEM strain reconstruction that embeds elastic equilibrium and Saint-Venant compatibility in the training loss through automatic differentiation. The architecture combines a coordinate-based implicit representation, sine activations with stable second derivatives, frozen residual-scale normalization, an exponential physics-weight ramp, and residual-based adaptive collocation. We apply a sine-activated residual network to an experimental $180\times400$-pixel strain map of domain-structured PbGeSnSe$_{1.5}$Te$_{1.5}$. Across $1$-$75%$ sampling ($720$-$54{,}000$ probe positions), $R^2$ for $\varepsilon_{xx}$ reaches $0.80$ at $10%$ sampling and saturates near $0.86$ by $25%$; the chevron strain-band morphology is recovered from $10%$ of probe positions. At $10%$ sampling, the PINN reduces mean absolute error by approximately $26%$ relative to compressed sensing and $22%$ relative to Gaussian-process regression. An ablation against an equal-capacity data-only SIREN shows that the PDE prior improves accuracy at extreme sparsity and consistently improves physical self-consistency, but biases the reconstruction when data are abundant. Monte Carlo dropout and mean-field variational inference provide per-pixel epistemic uncertainty maps correlated with reconstruction error. With an appropriate constitutive model, the framework is adaptable to strain mapping across diverse material systems.

cond-mat.mtrl-sci↗

arXiv2025

Concentration-Dependent Tungsten Effects on Chemical Short-Range Order and Deformation Behavior in Ni-W alloys

Ni-W based medium heavy alloys offer a promising pathway to bridge the density-strength gap between tungsten heavy alloys and ultrahigh-strength steels. In this study, the effects of W concentration on chemical short-range order (CSRO), deformation behavior, and grain boundary chemistry of Ni-xW alloys in the range x = 0 to 38 wt% were systematically investigated using a suite of advanced characterization and modeling techniques, including synchrotron X-ray diffraction, transmission electron microscopy, atom probe tomography, and first-principles thermodynamic simulations. Our study reveals that strong CSRO emerges when W content exceeds about 30 wt%, producing distinct diffuse scattering and significantly enhancing strain-hardening capacity. During deformation, the presence of SRO promotes planar slip and twin formation, leading to strong dislocation interactions and elevated flow stress. Hall-Petch analysis demonstrates an exceptionally high grain boundary strengthening coefficient (ky about 1100 MPa micrometer^(1/2)) in Ni-38W, underscoring the intrinsic strengthening effect associated with CSRO. First-principles cluster expansion coupled with Monte Carlo simulations reveals that increasing W content enhances SRO tendency through the stabilization of Ni4W-type local configurations. These findings establish a mechanistic link between W concentration, CSRO evolution, and mechanical response, providing new insights for designing high-density, high-strength Ni-W based alloys with optimized performance.

cond-mat.mtrl-sci↗

arXiv2025

Scalable data-driven modeling of microstructure evolution by learning local dependency and spatiotemporal translation invariance rules in phase field simulation

Phase-field (PF) simulation provides a powerful framework for predicting microstructural evolution but suffers from prohibitive computational costs that severely limit accessible spatiotemporal scales in practical applications. While data-driven methods have emerged as promising approaches for accelerating PF simulations, existing methods require extensive training data from numerous evolution trajectories, and their inherent black-box nature raises concerns about long-term prediction reliability. This work demonstrates, through examples of grain growth and spinodal decomposition, that a minimalist Convolutional Neural Network (CNN) trained with a remarkably small dataset even from a single small-scale simulation can achieve seamless scalability to larger systems and reliable long-term predictions far beyond the temporal range of the training data. The key insight of this work lies in revealing that the success of CNN-based models stems from the alignment between their inductive biases and the physical priors of phase-field simulations specifically, locality and spatiotemporal translation invariance. Through effective receptive field analysis, we verify that the model captures these essential properties during training. Therefore, from a reductionist perspective, the surrogate model essentially establishes a spatiotemporally invariant regression mapping between a grid point's local environment and its subsequent state. Further analysis of the model's feature space demonstrates that microstructural evolution effectively represents a continuous redistribution of a finite set of local environments. When the model has already encountered nearly all possible local environments in the early-stage training data, it can reliably generalize to much longer evolution timescales, regardless of the dramatic changes in global microstructural morphology.

cond-mat.mtrl-sci↗

arXiv2024

Electron-Induced Radiation Chemistry in Environmental Transmission Electron Microscopy

Environmental transmission electron microscopy (E-TEM) enables direct observation of nanoscale chemical processes crucial for catalysis and materials design. However, the high-energy electron probe can dramatically alter reaction pathways through radiolysis - the dissociation of molecules under electron beam irradiation. While extensively studied in liquid-cell TEM, the impact of radiolysis in gas-phase reactions remains unexplored. Here, we present a numerical model elucidating radiation chemistry in both gas and liquid E-TEM environments. Our findings reveal that while gas-phase E-TEM generates radiolytic species with lower reactivity than liquid-phase systems, these species can accumulate to reaction-altering concentrations, particularly at elevated pressures. We validate our model through two case studies: the radiation-promoted oxidation of aluminum nanocubes and disproportionation of carbon monoxide. In both cases, increasing the electron beam dose rate directly accelerates their reaction kinetics, as demonstrated by enhanced AlOx growth and carbon deposition. Based on these insights, we establish practical guidelines for controlling radiolysis in closed-cell nanoreactors. This work not only resolves a fundamental challenge in electron microscopy but also advances our ability to rationally design materials with sub-Angstrom resolution.

cond-mat.mtrl-sci↗

arXiv2024

Operando Analysis of Adsorption-Limited Hydrogen Oxidation Reaction at Palladium Surfaces

Palladium (Pd) catalysts have been extensively studied for the direct synthesis of H2O through the hydrogen oxidation reaction at ambient conditions. This heterogeneous catalytic reaction not only holds considerable practical significance but also serves as a classical model for investigating fundamental mechanisms, including adsorption and reactions between adsorbates. Nonetheless, the governing mechanisms and kinetics of its intermediate reaction stages under varying gas conditions remains elusive. This is attributed to the intricate interplay between adsorption, atomic diffusion, and concurrent phase transformation of catalyst. Herein, the Pd-catalyzed, water-forming hydrogen oxidation is studied, in situ, to investigate intermediate reaction stages via fluid cell transmission electron microscopy. The dynamic behaviors of water generation, associated with reversible palladium hydride formation, are captured in real time with a nanoscale spatial resolution. Our findings suggest that the hydrogen oxidation rate catalyzed by Pd is significantly affected by the sequence in which gases are introduced. Through direct evidence of electron diffraction and density functional theory calculation, we demonstrate that the hydrogen oxidation rate is limited by adsorption processes of gas precursors. These nanoscale insights help identify the optimal reaction conditions for Pd-catalyzed hydrogen oxidation, which has substantial implications for water production technologies. The developed understanding also advocates a broader exploration of analogous mechanisms in other metal-catalyzed reactions.

cond-mat.mtrl-sci↗

arXiv2023

Unlocking the Structural Mystery of Vaterite CaCO3

Calcium carbonate (CaCO3), the most abundant biogenic mineral on earth, plays a crucial role in various fields. Of the four polymorphs, calcite, aragonite, vaterite, and amorphous CaCO3, vaterite is the most enigmatic one due to an ongoing debate regarding its structure that has persisted for nearly a century. In this work, based on systematic transmission electron microscopy characterizations, elaborate crystallographic analysis and machine learning aided molecular dynamics simulations with ab initio accuracy, we reveal that vaterite can be regarded as a polytypic structure. The basic phase is a monoclinic lattice possessing pseudohexagonal symmetry. Direct imaging and atomic-scale simulations provide evidence that a single grain of vaterite can have three orientation variants. Additionally, we find that vaterite undergoes a second-order phase transition. These atomic scale insights provide a comprehensive understanding of the structure of vaterite and offer new perspectives on the biomineralization process of calcium carbonate.

cond-mat.mtrl-sci↗

arXiv2021

Design Strength-Ductility Synergy of Metastable High-Entropy Alloys by Tailoring Unstable Fault Energies

Metastable alloys with transformation/twinning-induced plasticity (TRIP/TWIP) can overcome the strength-ductility trade-off in structural materials. Originated from the development of traditional alloys, the intrinsic stacking fault energy (ISFE) has been relied to tailor TRIP/TWIP in high-entropy alloys (HEA), but with limited quantitative success. Herein, we demonstrate a new strategy for designing metastable HEAs and validate its effectiveness by discovering seven new alloys with experimentally observed metastability for TRIP/TWIP. We propose unstable fault energies as the more effective design metric and attribute the deformation mechanism of metastable face-centered cubic alloys to UMFE (unstable martensite fault energy)/UTFE (unstable twin fault energy) rather than ISFE. Among the studied HEAs and steels, the traditional ISFE criterion fails in more than half of the cases, while the UMFE/UTFE criterion accurately predicts the deformation mechanisms in all cases. The UMFE/UTFE criterion provides a new paradigm for developing metastable alloys with TRIP/TWIP for enhanced strength-ductility synergy.

cond-mat.mtrl-sci↗

arXiv2021

Ingrained -- An automated framework for fusing atomic-scale image simulations into experiments

To fully leverage the power of image simulation to corroborate and explain patterns and structures in atomic resolution microscopy (e.g., electron and scanning probe), an initial correspondence between the simulation and experimental image must be established at the outset of further high accuracy simulations or calculations. Furthermore, if simulation is to be used in context of highly automated processes or high-throughput optimization, the process of finding this correspondence itself must be automated. In this work, we introduce ingrained, an open-source automation framework which solves for this correspondence and fuses atomic resolution image simulations into the experimental images to which they correspond. We describe herein the overall ingrained workflow, focusing on its application to interface structure approximations, and the development of an experimentally rationalized forward model for scanning tunneling microscopy simulation.

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