arXiv subjects

Xiaoqin Zeng

Publications and source records attributed to Xiaoqin Zeng.

9 recordsLinked to original sources

arXiv2026

A benchmark dataset and baseline methods for four-dimensional STEM diffraction patterns

Four-dimensional scanning transmission electron microscopy (4D-STEM) records a two-dimensional diffraction pattern at each electron-probe position, yielding spatially resolved reciprocal-space information but large, heterogeneous data volumes. Here we describe 4D-ImageNet, a collection of 174,000 diffraction patterns comprising 145,000 experimental patterns selected from 29 acquisitions and 29,000 multislice simulations. The experimental data cover acquisition-level labels for Ag, Au, mixed Au-Ag, CoO, Pd and ZnO specimens across multiple fields of view, scan dimensions, camera lengths and exposure times. Each acquisition contributes 5,000 quality-ranked patterns with source scan coordinates and acquisition metadata. A set-prediction detector provides model-derived pseudo-labels for the direct-beam position and Bragg-disk centres, with a confidence score for each disk. The simulation data cover 13 crystal structures and include Euler rotations, reciprocal-space sampling and approximate low-index beam directions. A grouped mixed-domain masked-reconstruction benchmark is provided to assess leakage-resistant loading and evaluation across experimental and simulated data. The dataset is intended for representation learning, disk detection, diffraction-pattern retrieval, orientation analysis and simulation-to-experiment studies.

cs.CV↗

arXiv2026

Multi4D: an end-to-end neural network for structural determination at complex material interfaces

Heterogeneous interfaces dictate the performance and degradation of functional materials, making it essential to link local structural variations with macroscopic failure mechanisms to guide future materials design. Yet structural heterogeneity, phase overlap, and local disorder produce highly convoluted diffraction signatures, making extended transition regions difficult to interpret at atomic resolution across large fields of view. Here, we introduce Multi4D, a physics-informed neural network framework for automated multi-component crystallographic identification using four-dimensional scanning transmission electron microscopy (4D-STEM). By combining a latent-space Diffusion Transformer for physics-constrained style translation with a rotation-invariant convolutional neural network for orientation-agnostic classification, this approach translates multi-components diffraction datasets into deterministic crystallographic maps with 98.82% accuracy. In addition, we introduce Diffraction-Inferred Structural Complexity as an information-theoretic entropy metric derived from classifier predictive uncertainty that quantifies local structural ambiguity. We apply Multi4D to generate high-fidelity structural maps of complex superconducting heterostructures, corroded alloy surfaces, and degraded solid-state battery interfaces down to single-nanometer spatial resolution. This framework establishes a statistically robust analytical paradigm for automated microscopy, facilitating both industrial quality control and the data-driven discovery of interfacial design principles.

cond-mat.mtrl-sci↗

arXiv2025

4D-MISR: A unified model for low-dose super-resolution imaging via feature fusion

While electron microscopy offers crucial atomic-resolution insights into structure-property relationships, radiation damage severely limits its use on beam-sensitive materials like proteins and 2D materials. To overcome this challenge, we push beyond the electron dose limits of conventional electron microscopy by adapting principles from multi-image super-resolution (MISR) that have been widely used in remote sensing. Our method fuses multiple low-resolution, sub-pixel-shifted views and enhances the reconstruction with a convolutional neural network (CNN) that integrates features from synthetic, multi-angle observations. We developed a dual-path, attention-guided network for 4D-STEM that achieves atomic-scale super-resolution from ultra-low-dose data. This provides robust atomic-scale visualization across amorphous, semi-crystalline, and crystalline beam-sensitive specimens. Systematic evaluations on representative materials demonstrate comparable spatial resolution to conventional ptychography under ultra-low-dose conditions. Our work expands the capabilities of 4D-STEM, offering a new and generalizable method for the structural analysis of radiation-vulnerable materials.

cs.CV↗

arXiv2024

The effect of LPSO phase on the high-temperature oxidation of a stainless Mg-Y-Al alloy

In this study, we investigated the oxidation of the Mg-11Y-1Al alloy at 500°C in an Ar-20%O2 environment. Multiscale analysis showed the network-like long-period stacking ordered (LPSO) phase transformed into needle-like LPSO and polygonal Mg24Y5 phases, leading to the formation of a high-dense network of needle-like oxides at the oxidation front. These oxides grew laterally along the oxide/matrix interfaces, forming a thicker, continuous scale that effectively blocked elemental diffusion. Hence, the preferential oxidation along the needle-like LPSO is believed to accelerate the formation of a thicker and continuous oxide scale, further improving the oxidation resistance of the Mg-11Y-1Al alloy.

cond-mat.mtrl-sci↗

arXiv2023

An easy zero-shot learning combination: Texture Sensitive Semantic Segmentation IceHrNet and Advanced Style Transfer Learning Strategy

We proposed an easy method of Zero-Shot semantic segmentation by using style transfer. In this case, we successfully used a medical imaging dataset (Blood Cell Imagery) to train a model for river ice semantic segmentation. First, we built a river ice semantic segmentation dataset IPC_RI_SEG using a fixed camera and covering the entire ice melting process of the river. Second, a high-resolution texture fusion semantic segmentation network named IceHrNet is proposed. The network used HRNet as the backbone and added ASPP and Decoder segmentation heads to retain low-level texture features for fine semantic segmentation. Finally, a simple and effective advanced style transfer learning strategy was proposed, which can perform zero-shot transfer learning based on cross-domain semantic segmentation datasets, achieving a practical effect of 87% mIoU for semantic segmentation of river ice without target training dataset (25% mIoU for None Stylized, 65% mIoU for Conventional Stylized, our strategy improved by 22%). Experiments showed that the IceHrNet outperformed the state-of-the-art methods on the texture-focused dataset IPC_RI_SEG, and achieved an excellent result on the shape-focused river ice datasets. In zero-shot transfer learning, IceHrNet achieved an increase of 2 percentage points compared to other methods. Our code and model are published on https://github.com/PL23K/IceHrNet.

cs.CV↗

arXiv2023

Critical resolved shear stresses for slip and twinning in Mg-Y-Ca alloys and their effect on the ductility

The deformation mechanisms of an extruded Mg-5Y-0.08Ca (wt. %) alloy were analyzed by means of micropillar compression tests on single crystals along different orientations -- selected to activate specific deformation modes -- as well as slip trace analysis, transmission electron microscopy and transmission Kikuchi diffraction. The polycrystalline alloy presented a remarkable ductility in tension (~32%) and negligible differences in the yield strength between tension and compression. It was found that the presence of Y and Ca in solid solution led to a huge increase in the CRSS for basal slip (29 $\pm$ 5 MPa), pyramidal slip (203 $\pm$ 7 MPa) and tensile twin nucleation (above 148 MPa), while the CRSS for prismatic slip only increases up to 105 $\pm$ 4 MPa. The changes in the CRSS for slip and tensile twinning in Mg-Y-Ca alloys expectedly modify the dominant deformation mechanisms in polycrystals. In particular, tensile twinning is replaced by prismatic slip during compressive deformation along the a-axis. The reduction of twinning (which generally induces strong anisotropy in the plastic deformation in textured alloys), and the activation of prismatic slip (which provides an additional plastic deformation mechanism with limited hardening) were responsible for the large tensile ductility of the alloy.

cond-mat.mtrl-sci↗

arXiv2022

High-throughput calculations combining machine learning to investigate the corrosion properties of binary Mg alloys

Magnesium (Mg) alloys have shown great prospects as both structural and biomedical materials, while poor corrosion resistance limits their further application. In this work, to avoid the time-consuming and laborious experiment trial, a high-throughput computational strategy based on first-principles calculations is designed for screening corrosion-resistant binary Mg alloy with intermetallics, from both the thermodynamic and kinetic perspectives. The stable binary Mg intermetallics with low equilibrium potential difference with respect to the Mg matrix are firstly identified. Then, the hydrogen adsorption energies on the surfaces of these Mg intermetallics are calculated, and the corrosion exchange current density is further calculated by a hydrogen evolution reaction (HER) kinetic model. Several intermetallics, e.g. Y3Mg, Y2Mg and La5Mg, are identified to be promising intermetallics which might effectively hinder the cathodic HER. Furthermore, machine learning (ML) models are developed to predict Mg intermetallics with proper hydrogen adsorption energy employing work function (W_f) and weighted first ionization energy (WFIE). The generalization of the ML models is tested on five new binary Mg intermetallics with the average root mean square error (RMSE) of 0.11 eV. This study not only predicts some promising binary Mg intermetallics which may suppress the galvanic corrosion, but also provides a high-throughput screening strategy and ML models for the design of corrosion-resistant alloy, which can be extended to ternary Mg alloys or other alloy systems.

cond-mat.mtrl-sci↗

arXiv2021

Deformation mechanisms of Mg-Ca-Zn alloys studied by means of micropillar compression tests

The effect of Ca and Zn in solid solution on the critical resolved shear stress (CRSS) of basal slip, tensile twinning and pyramidal slip in Mg alloys has been measured through compression tests on single crystal micropillars with different orientations. The solute atoms increased the CRSS for basal slip to ~ 13.5 MPa, while the CRSS for pyramidal slip was lower than 85 MPa, reducing significantly the plastic anisotropy in comparison with pure Mg. Moreover, the CRSSs for twin nucleation and growth were very similar (~ 37 MPa) and the large value of the CRSS for twin growth hindered the growth of twins during thermo-mechanical processing. Finally, evidence of prismatic slip and cross-slip between basal and prismatic dislocations was found. It is concluded that the reduction of plastic anisotropy, the activation of different slip systems and cross-slip and the weak basal texture promoted by the large CRSS for twin growth are responsible for the improved ductility and formability of Mg-Ca-Zn alloys.

cond-mat.mtrl-sci↗

arXiv2021

Exploring the Correlation between Solvent Diffusion and Creep Resistance of Mg-Ga HCP Alloys from High Throughput Liquid-Solid Diffusion Couple

The liquid-solid diffusion couple technique, supported by phenomenological analysis and nano-indentation tests, is proposed on account of the relatively low melting points of Mg to explore the diffusion mobility and creep deformation. The potential of this strategy is demonstrated in Mg-Ga hcp alloys where Ga solute (i.e. impurity) and Mg solvent diffusions in hcp Mg-Ga alloys were both unveiled. It was followed by mapping the compressive creep behavior via nanoindentation along the composition arrays within the same Mg-Ga couple sample. The compressive creep resistance of Mg-Ga hcp alloys increased with the Ga content, and this enhancement was similar to the one found in Mg-Zn alloys and superior to the one reported in Mg-Al alloys though Al is a slower impurity diffuser in hcp-Mg than Zn and Ga. Thereby, the solvent diffusion and its variation with the composition, rather than the solute diffusion, was suggested to govern the creep properties at high temperatures and low stresses.

cond-mat.mtrl-sci↗