arXiv subjects

Bai-Xiang Xu

Publications and source records attributed to Bai-Xiang Xu.

At least 19 recordsLinked to original sources

arXiv2026

Accurate Nanoscale Mapping of Electric Fields across Random Grain Boundaries in Polycrystalline Oxides Using Precession-Assisted 4D-STEM

Space charge layers (SCLs) at grain boundaries play a crucial role in modulating local electric fields and influencing the functional properties of materials, such as oxygen vacancy migration and ionic conductivity in oxide ceramics. However, the direct experimental analysis of such localized electric fields and the corresponding charge distribution remains challenging. Conventional center-of-mass (CoM) analysis in scanning transmission electron microscopy differential phase contrast (STEM-DPC) is strongly affected by orientation-dependent contrast and dynamical scattering. Here, we demonstrate that combining electron beam precession with advanced post-processing, employing iterative edge detection via a Sobel filter and singular value decomposition (SVD), enables reliable and accurate, unbiased diffraction shift measurements with minimal crystallographic artefacts. The new method accurately refines the central disk position in nanobeam electron diffraction (NBED) patterns and thus significantly improves the extraction of the local electric field and corresponding charge distribution. Comparative analysis with conventional CoM methods shows superior accuracy and robustness for random grain boundaries in BaTiO3 and SrTiO3 as exemplary case studies. The experimental work is complemented by atomistic simulations to separate the electric field of the SCL from the mean inner potential difference of the grain boundary and the elemental segregation around the grain boundary. The in-depth analysis shows that our approach enables high-fidelity mapping of electromagnetic fields and their charge distribution in complex polycrystalline specimens, laying the groundwork for improved quantitative analysis using STEM-DPC.

cond-mat.mtrl-sci↗

arXiv2026

Impact of charge transition levels on grain boundary properties in acceptor doped oxide ceramics: A phase-field study

Advanced doping strategies enable oxide ceramic functionalities by tailoring bulk defect chemistry and space-charge-layer (SCL) behavior at interfaces. Charge transition levels (CTLs), defined as the Fermi level at which a defect changes its stable charge state, play a central role. Their alignment governs bulk defect chemistry, while their bending within SCLs induces additional charge-state transitions. Incorporating CTLs is therefore essential for a consistent description of defect equilibria and SCL formation. In this work, we propose a defect-chemistry-consistent phase-field model explicitly coupled with CTLs to investigate their role in SCL evolution. The model includes multivalent oxygen vacancies, multivalent acceptor dopants, electrons, and holes. It is applied to Fe-doped SrTiO3 over wide ranges of oxygen partial pressure and temperature, capturing both symmetric SCLs at stationary grain boundaries and asymmetric SCLs during migration. Two distinct grain boundary types, slow and fast boundaries, emerge during migration, consistent with experimental observations. Simulations reveal that CTL-governed bulk defect chemistry, together with CTL-induced charge-state transitions within SCLs, critically determine SCL characteristics. Moreover, CTL-mediated hole transport is significantly faster than acceptor dopant diffusion, modulating solute drag and grain boundary kinetics. Finally, the model predicts grain boundary properties dependent on both thermal history and boundary type, with slow and fast boundaries exhibiting distinct behaviors. This framework links defect chemistry, Fermi level, CTLs, and grain boundary kinetics, providing new insights for designing oxide ceramics with tailored properties.

cond-mat.mtrl-sci↗

arXiv2026

Phase-field investigation of non-isothermal solidification coupled with melt flow dynamics

Solidification, coupled with melt flow, plays a critical role in determining the microstructure and properties of materials in several manufacturing processes. Phase-field models coupled with the Navier-Stokes equations are widely used to model and simulate these dynamics. However, most existing models neglect essential thermodynamic couplings, particularly the capillary (Korteweg) stress in the momentum equation. This stress, which arises from the coupling between the phase field and the melt flow, accounts for thermal capillary effects during non-isothermal solidification. Neglecting it leads to models inconsistent with non-equilibrium thermodynamics and incapable of capturing capillarity-driven melt flow. In this work, we present a thermodynamically consistent, non-isothermal phase-field model for solidification coupled with melt flow, incorporating cross-coupling terms and explicitly including the Korteweg stress in the momentum equation. Model validation is performed for solidification-only cases, followed by simulations of dendritic growth under melt flow. The results show that thermal capillary effects induce flow near the interface, influencing dendrite tip velocity and morphology. Simulations under forced convection further demonstrate asymmetric dendrite growth due to the imposed flow field. Additionally, we numerically demonstrate the influence of viscosity interpolation schemes on enforcing the no-slip boundary condition in phase-field models with melt flow.

physics.flu-dyn↗

arXiv2026

Machine learning-enabled inverse design of bimaterial thermoelastic lattice metamaterials

The thermoelastic metamaterial based on a bimaterial hybrid-honeycomb structure, exhibiting simultaneously negative Poisson's ratios and negative thermal expansion coefficients is very promising for various application. This work is dedicated to the machine learning (ML)-enabled inverse design of such structure, on the basis of high-throughput simulation and neural network models. A large dataset is generated through computational homogenization of structures with varying geometrical features and base material properties. A forward ML model is first trained to efficiently and accurately predict the effective thermoelastic properties for a given structure design. Subsequently, inverse ML models are developed to suggest geometrical features and base materials for desired target properties. To address various inverse design scenarios, six different models are proposed, each defined by different combinations of target effective properties and structural design variables. The trained forward model is integrated into the loss functions of the inverse models and is also employed to generate additional datasets for cases with fixed base materials. The good predictive performance of the forward and inverse ML models is demonstrated by representative design examples. These ML models can be applied to efficiently solving specific inverse design tasks involved in the practical application of the thermoelastic metamaterial in novel engineering systems.

physics.app-ph↗

arXiv2026

Thermal and Microstructural Simulations of Photonic Sintering of Oxide Ceramics: A Two-Scale Scheme

Photonic sintering (PS) offers an ultra-fast, contact-free alternative to conventional sintering and has demonstrated its potential for enhancing the sinterability of acceptor-doped barium zirconate (BZY) ceramics. However, a central challenge in the PS process lies in achieving precise control over thermal self-stabilization in the presence of complex microstructural effects arising from photonic-ray--induced thermal profiles. To elucidate the interplay among thermal fields, microstructural evolution, and PS process parameters, this study establishes a two-scale, non-isothermal simulation framework. The framework integrates macroscopic heat-transfer simulations, incorporating effective heat conduction and photonic-ray--induced volumetric heating in the porous media, with microscopic non-isothermal phase-field sintering simulations that resolve microstructure evolution under local thermal profile. Scale bridging is achieved through a temperature field transferring and mapping that satisfies Hill-Mandel condition between the macroscopic and microscopic simulations, while maintaining synchronization between their asynchronous time-stepping schemes. After calibrating model parameters against experimental measurements, the framework successfully reproduces the experimentally observed porosity inhomogeneity along the sample depth. The influence of enhanced localized mass transport is further examined through a parametric investigation of surface and grain boundary diffusivities. Overall, the proposed framework demonstrates its feasibility and physical interpretability in establishing process-microstructure relationships for the scalable fabrication of high-performance protonic ceramics.

cond-mat.mtrl-sci↗

arXiv2025

Identifying grain boundary and intragranular pinning centres in Sm2(Co,Fe,Cu,Zr)17 permanent magnets to guide performance optimisation

Permanent magnets draw their properties from a complex interplay of chemical composition and phase, each with their associated intrinsic magnetic properties. Gaining an understanding of these interactions is the key to deciphering the origins of a permanent magnets' magnetic performance and facilitate the engineering of much improved-performing magnets. Here, we use advanced multiscale microscopy and microanalysis on a bulk Sm2(Co,Fe,Cu,Zr)17 pinning-type high-performance magnet with outstanding thermal and chemical stability. Comparison of the microstructure in regions of different composition, we demonstrate that the pinning of magnetic domains, imaged by nanoscale magnetic induction mapping, is controlled by the composition and atomic arrangement of copper. This is confirmed by micromagnetic simulations. Contrary to the belief that grain boundaries are "weak links" in magnetic materials, we demonstrate grain boundaries undergo magnetization reversal at relatively low fields (0.1-0.3 T), but this remains confined to these regions and does not significantly impact the magnet's coercivity. Our results showcase that it is the optimal microstructure within the grain itself that is crucial for achieving the desired magnetic properties.

cond-mat.mtrl-sci↗

arXiv2025

A defect-chemistry-informed phase-field model of grain growth in oxide ceramics: application to Fe-doped SrTiO3

Dopants can significantly affect the properties of oxide ceramics through their impact on the property-determined microstructure characteristics such as grain boundary (GB) segregation, space charge layer formation in the GB vicinity, and the grain growth deviating from normal patterns. To support the rational design of oxide ceramics, we propose a defect-chemistry-informed phase-field grain growth model to simulate the microstructure evolution of oxide ceramics. It fully respects the defect-chemistry theory by accounting for the distinct segregation energies and available site densities of charged point defects (oxygen vacancies and acceptor dopants) in both the grain interior and boundaries, and it considers the competing kinetics of defect diffusion and GB movement. The proposed phase-field model is benchmarked against well-known bicrystal models, including the Mott-Schottky and Gouy-Chapman models. Various simulation results are presented to reveal the effect of different defect-chemistry parameters on the space charge layer formation and key microstructural aspects. In particular, simulation results confirm that the solute drag effect alone can lead to skewed grain size distribution that do not follow the log-normal distribution, without any contribution from grain misorientation and other anisotropy. Interestingly, simulations also demonstrate that grain boundary potentials can vary substantially: GBs of larger grains tend to have lower potentials than those of smaller grains. Such heterogeneous GB potential distribution may inspire a new material optimization strategy through microstructure design. This study provides a comprehensive framework for defect-chemistry-consistent investigations of microstructure evolution in polycrystalline oxide ceramics, offering fundamental insights into microscopic processes during critical manufacturing stages.

cond-mat.mtrl-sci↗

arXiv2025

Coercivity influence of nanostructure in SmCo-1:7 magnets: Machine learning of high-throughput micromagnetic data

As a pinning-controlled permanent magnet, tailoring the cellular nanostructure of samarium-cobalt-based 1:7-type (SmCo-1:7) magnets remains crucial for improving magnetic performance. Jointing forward and inverse machine learning models with the high-throughput micromagnetic simulations (42,300 runs), we identify the nanostructural and magnetic features that are most effective for coercivity, combining both nucleation and pinning mechanisms. Sensitivity analyses reveal that the 1:5-phase enhances coercivity by providing high anisotropy, and the Z-phase strengthens pinning through fluctuations in domain wall energy. Cu additions in the 1:5-phase significantly reduce coercivity, while Fe substitutions in the 2:17-phase modestly reduce coercivity but improve pinning locally and increase saturation magnetization. Among all examined features, magnetocrystalline misorientation emerges as the dominant factor. Finally, the framework enables the inverse design of nanostructures with prescribed coercivity, demonstrating a computationally cost-effective toolkit for guiding the performance tailoring of SmCo-1:7 magnets.

cond-mat.mtrl-sci↗

arXiv2025

Direct observation of nanoscale pinning centers in Ce(Co0.8Cu0.2)5.4 permanent magnets

Permanent magnets containing rare earth elements are essential components for the electrification of society. Ce(Co1-xCux)5 permanent magnets are a model system known for their substantial coercivity, yet the underlying mechanism remains unclear. Here, we investigate Ce(Co0.8Cu0.2)5.4 magnets with a coercivity of ~1 T. Using transmission electron microscopy (TEM) and atom probe tomography (APT), we identify a nanoscale cellular structure formed by spinodal decomposition. Cu-poor cylindrical cells (~5-10 nm in diameter, ~20 nm long) have a disordered CeCo5-type structure and a composition Ce(Co0.9Cu0.1)5.3. Cu-rich cell boundaries are ~ 5 nm thick and exhibit a modified CeCo5 structure, with Cu ordered on the Co sites and a composition Ce(Co0.7Cu0.3)5.0. Micromagnetic simulations demonstrate that the intrinsic Cu concentration gradients up to 12 at.% Cu/nm lead to a spatial variation in magnetocrystalline anisotropy and domain wall energy, resulting in effective pinning and high coercivity. Compared to Sm2Co17-type magnets, Ce(Co0.8Cu0.2)5.4 displays a finer-scale variation of conventional pinning with lower structural and chemical contrast in its underlying nanostructure. The identification of nanoscale chemical segregation in nearly single-phase Ce(Co0.8Cu0.2)5.4 magnets provides a microstructural basis for the long-standing phenomenon of "giant intrinsic magnetic hardness" in systems such as SmCo5-xMx, highlighting avenues for designing rare-earth-lean permanent magnets via controlled nanoscale segregation.

cond-mat.mtrl-sci↗

arXiv2025

Multiphysics simulations of microstructure influence on hysteresis and eddy current losses of electrical steel

Improving efficiency of electrical machines requires fundamental knowledge on the mechanisms behind magnetic and eddy current losses of the magnetic core materials, with Fe-Si alloy as a prototype. These losses are intrinsically influenced by the microstructure of the materials. This necessitates physics-based, microstructure-informed multiscale simulations. In the present paper, we utilised micromagnetic simulations and computational homogenization methods to calculate the effective hysteresis and effective conductivities of Fe-Si electrical steels. To demonstrate the methodology, binder-jet printed electrical steel material samples with different microstructure were investigated. The microstructure samples were digitized based on both the descriptor-based synthetic reconstruction and SEM-image-based digitization. More samples were generated with varying microstructure features such as grain size and grain boundary phases. The micromagnetic simulations were then performed to investigate the magnetic hysteresis and hysteresis loss. The eddy current loss was also evaluated by using the effective conductivity through computational homogenization. By performing parameter research on a series of synthetic microstructures, effects of average grain size and grain boundary (GB) phase thickness on the hysteresis loss and eddy current loss were unveiled. An average grain size around 120 \si{\micro m} has the lowest hysteresis loss, although the eddy current loss increases with the grain size. Increasing GB-phase thickness helps reduce both losses. Results indicate the potential to decrease loss of magnetic core materials by microstructure optimization.

cond-mat.mtrl-sci↗

arXiv2025

Deep learning-enabled large-scale analysis of particle geometry-lithiation correlations in battery cathode materials

A deep learning model is employed to address the challenging problem of V2O5 nanoparticle segmentation and the correlation between the chemical composition and the geometrical features of lithiated V2O5 nanoparticles as an exemplar of a phase-transforming battery cathode material. First, the deep learning-enabled segmentation model is integrated with the singular value decomposition technique and a spectral database to generate accurate composition and phase maps capturing lithiation heterogeneities as imaged using scanning transmission X-ray microscopy. These phase maps act as the output properties for correlation analysis. Subsequently, the quantitative influences of the geometrical features of nanoparticles such as the particle size (i.e., projected perimeter and area), the aspect ratio, circularity, convexity, and orientation on the lithiation phase maps are revealed. These findings inform strategies to improve lithiation uniformity and reduce stress in phase-transforming lithium battery materials via optimized particle geometry.

cond-mat.mtrl-sci↗

arXiv2025

Deciphering the interplay between wetting and chemo-mechanical fracture in lithium-ion battery cathode materials

Crack growth in lithium-ion battery electrodes is typically detrimental and undesirable. However, recent experiments suggest that stabilized fracture of cathode active materials in liquid electrolytes can increase electrochemically active surfaces, shorten diffusion pathway, enhance (de)lithiation and improve overall capacity. To decipher the fundamental couplings between electrolyte wetting and fracture evolution and evaluate their influences on macroscopic battery performance, we conducted an integrated experiment-simulation study on $α$-V2O5 single crystals and polycrystalline NCM as model cathode materials. Despite synthesis challenges, single-crystal $α$-V2O5 offers clearer fundamental insights than polycrystalline counterparts with grain-boundary complexities. Fracture patterns and lithiation heterogeneities on the samples were mapped using advanced scanning techniques after chemical (de)lithiation cycles, exhibiting excellent agreements with simulations by the developed multiphysics model. Results reveal a mutually reinforcing interplay between wetting and fracture: (i) electrolyte infiltration at fracture surfaces enhances (de)lithiation and compositional heterogeneity; (ii) wetting influences fracture dynamics, including fracture modes, propagation distance and directionality. The validated modelling framework is further applied to simulations on polycrystalline NCM particles under constant-current (dis)charging, highlighting the critical role of wetting in promoting fracture and improving overall capacity. This work bridges fundamental understanding of wetting-fracture coupling with practical implications for battery performance optimization via controlled fracture engineering.

cond-mat.mtrl-sci↗

arXiv2025

STEM Diffraction Pattern Analysis with Deep Learning Networks

Accurate grain orientation mapping is essential for understanding and optimizing the performance of polycrystalline materials, particularly in energy-related applications. Lithium nickel oxide (LiNiO$_{2}$) is a promising cathode material for next-generation lithium-ion batteries, and its electrochemical behaviour is closely linked to microstructural features such as grain size and crystallographic orientations. Traditional orientation mapping methods--such as manual indexing, template matching (TM), or Hough transform-based techniques--are often slow and noise-sensitive when handling complex or overlapping patterns, creating a bottleneck in large-scale microstructural analysis. This work presents a machine learning-based approach for predicting Euler angles directly from scanning transmission electron microscopy (STEM) diffraction patterns (DPs). This enables the automated generation of high-resolution crystal orientation maps, facilitating the analysis of internal microstructures at the nanoscale. Three deep learning architectures--convolutional neural networks (CNNs), Dense Convolutional Networks (DenseNets), and Shifted Windows (Swin) Transformers--are evaluated, using an experimentally acquired dataset labelled via a commercial TM algorithm. While the CNN model serves as a baseline, both DenseNets and Swin Transformers demonstrate superior performance, with the Swin Transformer achieving the highest evaluation scores and the most consistent microstructural predictions. The resulting crystal maps exhibit clear grain boundary delineation and coherent intra-grain orientation distributions, underscoring the potential of attention-based architectures for analyzing diffraction-based image data. These findings highlight the promise of combining advanced machine learning models with STEM data for robust, high-throughput microstructural characterization.

cond-mat.dis-nn↗

arXiv2025

Determining the grain orientations of battery materials from electron diffraction patterns using convolutional neural networks

Polycrystalline materials have numerous applications due to their unique properties, which are often determined by the grain boundaries. Hence, quantitative characterization of grain as well as interface orientation is essential to optimize these materials, particularly energy materials. Using scanning transmission electron microscopy, matter can be analysed in an extremely fine grid of scan points via electron diffraction patterns at each scan point. By matching the diffraction patterns to a simulated database, the crystal orientation of the material as well as the orientation of the grain boundaries at each scan point can be determined. This pattern matching approach is highly time intensive. Artificial intelligence promises to be a very powerful tool for pattern recognition. In this work, we train convolutional neural networks (CNNs) on dynamically simulated diffraction patterns of LiNiO2, an important cathode-active material for Lithium-ion batteries, to predict the orientation of grains in terms of three Euler angles for the complete fundamental orientation region. Results demonstrate that these networks outperform the conventional pattern matching algorithm with increased accuracy and efficiency. The increased accuracy of the CNN models can be attributed to the fact that these models are trained by data incorporating dynamical effects. This work is the first attempt to apply deep learning for analysis of electron diffraction data and enlightens the great potential of ML to accelerate the analysis of electron microscopy data, toward high-throughput characterization technique.

cond-mat.mtrl-sci↗

arXiv2024

Variational approximation for a non-isothermal coupled phase-field system: Structure-preservation & Nonlinear stability

A Cahn-Hilliard-Allen-Cahn phase-field model coupled with a heat transfer equation, particularly with full non-diagonal mobility matrices, is studied. After reformulating the problem w.r.t. the inverse of temperature, we proposed and analysed a structure-preserving approximation for the semi-discretisation in space and then a fully discrete approximation using conforming finite elements and time-stepping methods. We prove structure-preserving property and discrete stability using relative entropy methods for the semi-discrete and fully discrete case. The theoretical results are illustrated by numerical experiments.

math.NA↗

arXiv2024

A length-scale insensitive cohesive phase-field interface model: application to concurrent bulk and interface fracture simulation in Lithium-ion battery materials

A new cohesive phase-field (CPF) interface fracture model is proposed on the basis of the Euler-Lagrange equation of the phase-field theory and the interface fracture energy check w.r.t. that of the cohesive zone model. It employs an exponential function for the interpolation of fracture energy between the bulk phase and the interface, while the effective interface fracture energy $\tilde{G}_i$ is derived in such a way that the integrated phase-field fracture energy across the diffusive interface region remains consistent with the sharp interface fracture energy $G_i$ defined in the classical cohesive zone model. This consistency is the key to ensure that the numerical results remain insensitive to the choice of length-scale parameters, particularly the regularized interface thickness $L$ and the regularized fracture surface thickness $b$. By employing this energy consistency check, various CPF interface models in the literature are reviewed. Besides the length-scale insensitivity, the proposed CPF interface model offers further advantages. Thanks to the fact that the exponential interpolation function can be obtained conveniently from the relaxation solution of an Allen-Cahn equation, the proposed CPF model is advantageous over other models with high flexibility in handling structures containing complicated interface topology. In order to demonstrate this merit and to check the length-scale insensitivity in multiphysics context, the proposed CPF interface model is employed further to derive a thermodynamically consistent chemo-mechanical model relevant to Lithium-ion battery materials. Finite element simulation results of the concurrent bulk and interface fracture in polycrystalline electrode particles, reconstructed from images with segmented interfaces, confirm the expected computational advantages and the length-scale insensitivity in chemo-mechanical context.

cond-mat.mtrl-sci↗

arXiv2024

ML-extendable framework for multiphysics-multiscale simulation workflow and data management using Kadi4Mat

As material modeling and simulation has become vital for modern materials science, research data with distinctive physical principles and extensive volume are generally required for full elucidation of the material behavior across all relevant scales. Effective workflow and data management, with corresponding metadata descriptions, helps leverage the full potential of data-driven analyses for computer-aided material design. In this work, we propose a research workflow and data management (RWDM) framework to manage complex workflows and resulting research (meta)data, while following FAIR principles. Multiphysics multiscale simulations for additive manufacturing investigations are treated as showcase and implemented on Kadi4Mat: an open source research data infrastructure. The input and output data of the simulations, together with the associated setups and scripts realizing the simulation workflow, are curated in corresponding standardized Kadi4Mat records with extendibility for further research and data-driven analyses. These records are interlinked to indicate information flow and form an ontology based knowledge graph. Automation scheme for performing high-throughput simulation and post-processing integrated with the proposed RWDM framework is also presented.

cond-mat.mtrl-sci↗

arXiv2024

What can machine learning help with microstructure-informed materials modeling and design?

Machine learning techniques have been widely employed as effective tools in addressing various engineering challenges in recent years, particularly for the challenging task of microstructure-informed materials modeling. This work provides a comprehensive review of the current machine learning-assisted and data-driven advancements in this field, including microstructure characterization and reconstruction, multiscale simulation, correlations among process, microstructure, and properties, as well as microstructure optimization and inverse design. It outlines the achievements of existing research through best practices and suggests potential avenues for future investigations. Moreover, it prepares the readers with educative instructions of basic knowledge and an overview on machine learning, microstructure descriptors and machine learning-assisted material modeling, lowering the interdisciplinary hurdles. It should help to stimulate and attract more research attention to the rapidly growing field of machine learning-based modeling and design of microstructured materials.

cond-mat.mtrl-sci↗