Bai-Xiang Xu

Technische Universität Darmstadt | TU · Institute of Materials Science

Machine learning (ML) 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 ML-assisted and data-driven advancements in this field, including mi...

The demand for strong, compact permanent magnets essential for the energy transition drives innovation in magnet manufacturing. Additive manufacturing, particularly Powder Bed Fusion of metals using a laser beam (PBF‐LB/M), offers potential for near‐net‐shaped Nd‐Fe‐B permanent magnets but often falls short compared to conventional methods. A less...

The ionic conductivity at the grain boundaries (GBs) in oxide ceramics is typically several orders of magnitude lower than that within the grain interior. This detrimental GB effect is the main bottleneck for designing high-performance ceramic electrolytes intended for use in solid-state lithium-ion batteries, fuel cells, and electrolyzer cells. Th...

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 with respect to the inverse of temperature, we proposed and analysed a structure-preserving approximation for the semi-discretisation in space and then a fully discret...

Around 17,000 micromagnetic simulations were performed with a wide variation of geometric and magnetic parameters of different cellular nanostructures in the samarium-cobalt-based 1:7-type (SmCo-1:7) magnets. A forward prediction neural network (NN) model is trained to unveil the influence of these parameters on the coercivity of materials, along w...

Understanding the failure mechanisms of lithium-ion batteries is essential for their greater adoption in diverse formats. Operando X-ray and electron microscopy enable the evaluation of concentration, phase, and stress heterogeneities in electrode architectures. Phase-field models are commonly used to capture multi-physics coupling including the in...

Dopants can significantly affect the properties of oxide ceramics through their impact on the property-determined microstructure characteristics such as grain boundary segregation, space charge layer formation in the grain boundary vicinity, the resultant microstructure features like bimodality due to abnormal grain growth. To support rational oxid...

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 ef...

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 t...

Unlike metals where dislocations carry strain singularity but no charge, dislocations in oxide ceramics are characterized by both a strain field and a local charge with a compensating charge envelope. Oxide ceramics with their deliberate engineering and manipulation are pivotal in numerous modern technologies such as semiconductors, superconductors...

Residual stress and plastic strain in additive manufactured materials can exhibit significant microscopic variation at the powder scale, profoundly influencing the overall properties of printed components. This variation depends on processing parameters and stems from multiple factors, including differences in powder bed morphology, non-uniform the...

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, inc...

The ionic conductivity at the grain boundaries (GBs) in oxide ceramics is typically several orders of magnitude lower than that within the grain interior. This detrimental GB effect is the main bottleneck for designing high-performance ceramic electrolytes intended for use in solid-state Lithium-ion batteries, fuel cells, and elec-trolyzer cells. T...

In this work, an efficient and robust isogeometric three-dimensional solid-beam finite element is developed for large deformations and finite rotations with merely displacements as degrees of freedom. The finite strain theory and hyperelastic constitutive models are considered and B-Spline and NURBS are employed for the finite element discretizatio...

In this work, an efficient and robust isogeometric three-dimensional solid-beam finite element is developed for large deformations and finite rotations with merely displacements as degrees of freedom. The finite strain theory and hyperelastic constitutive models are considered and B-Spline and NURBS are employed for the finite element discretizatio...

Residual stress and plastic strain in additive manufactured materials can exhibit significant microscopic variation at the powder scale, profoundly influencing the overall properties of printed components. This variation depends on processing parameters and stems from multiple factors, including differences in powder bed morphology , non-uniform th...

The pinning-controlled mobility of ferroelectric/ferroelastic domain walls is an important part of managing polarization switching and determining the final properties of ferroelectric and piezoelectric materials. Here, we assess the impact of temperature on dislocation-induced domain wall pinning as well as on dislocation-tuned dielectric and piez...

Macroscopic thermal properties of engineered or inherent composites depend substantially on the composite structure and the interface characteristics. While it is acknowledged that unveiling such dependency relation is essential for materials design, the complexity involved in, e.g., microstructure representation and limited data impedes the resear...

We present a multiphysics phase-field fracture model for thermo-elasto-plastic solids in the context of finite deformation and apply it to simulate the hot cracking phenomenon during metal additive manufacturing. The model is derived in a thermodynamically consistent manner, with the intercoupling mechanisms among elastoplasticity, phase-field crac...

Oxide dispersion-strengthened (ODS) steel is a sought-after composite material known for its high demand in high-temperature and corrosive environments. Achieving the desired ODS steel properties requires specific conditions for the size and nanoparticles (NP) distribution in the printed part. Laser ablation in liquid (LAL) enables precise NP size...

Purpose Confronting the unveiled sophisticated structural and physical characteristics of permanent magnets, notably the samarium–cobalt (Sm-Co) alloy, This work aims to introduce a simulation scheme that can link physics-based micromagnetics on the nanostructures and magnetostatic homogenization on the mesoscale polycrystalline structures. Design...

Severe plastic deformation of powder blends consisting of SmCo5-Cu results in magnetically hardened nanocomposite bulk materials. Previously it was reported that the microstructure is continuously refined with increasing deformation, yet, coercivity saturates at a certain level of strain and partial amorphization of the SmCo5 phase is observed. In...

Chemical substitution, which can be iso- or heterovalent, is the primary strategy to tailor material properties. There are various ways how a material can react to substitution. Isovalent substitution changes the density of states while heterovalent substitution, i.e. doping, can induce electronic compensation, ionic compensation, valence changes o...

Phase-field modeling has become a powerful tool in describing the complex pore-structure evolution and the intricate multiphysics in nonisothermal sintering processes. However, the quantitative validity of conventional variational phase-field models involving diffusive processes is a challenge. Artificial interface effects, like the trapping effect...

We report an intrinsic strain engineering, akin to thin filmlike approaches, via irreversible high-temperature plastic deformation of a tetragonal ferroelectric single-crystal BaTiO3. Dislocations well-aligned along the [001] axis and associated strain fields in plane defined by the [110]/[-110] plane are introduced into the volume, thus nucleating...

Designing the microstructure of Fe-Ni permalloy produced by additive manufacturing (AM) opens new avenues to tailor its magnetic properties. Yet, AM-produced parts suffer from spatially inhomogeneous thermal-mechanical and magnetic responses, which are less investigated in terms of process modeling and simulations. We present a powder-resolved mult...

(Accepted by Scripta Materialia (04,10,2023)) Severe plastic deformation of powder blends consisting of SmCo5-Cu results in magnetically hardened nanocomposite bulk materials. The microstructure is continuously refined with increasing torsional deformation, yet, coercivity saturates at a certain level of strain. Transmission electron microscopy (TE...

2D magnets can potentially revolutionize information technology, but their potential application to cooling technology and magnetocaloric effect (MCE) in a material down to the monolayer limit remain unexplored. Herein, it is revealed through multiscale calculations the existence of giant MCE and its strain tunability in monolayer magnets such as C...

Powder bed fusion of metals using a laser beam (PBF-LB/M) is an established additive manufacturing (AM) method that can be used to fabricate geometrically complex NdFe-B magnets. However, the magnetic properties of Nd-Fe-B magnets manufactured by PBF-LB/M are typically inferior to conventionally produced magnets. To overcome this drawback, we modif...

Permanent magnets draw their properties from a complex interplay, across multiple length scales, of the composition and distribution of their constituting phases, that act as building blocks, each with their associated intrinsic properties 1 . Gaining a fundamental understanding of these interactions is hence key to decipher the origins of their ma...

The battery cell performance is determined by electro-chemo-mechanical mechanisms on different length scales. Though there exist multi-field multiscale simulation frameworks, the high computation cost prevents their wide application. It is even more challenging when it comes to all-solid-state batteries where the influence of the interface damage a...

(Accepted in COMPEL) Confronting the unveiled sophisticated multiscale structural and physical characteristics of hysteresis simulation of permanent magnets, notably samarium-cobalt (Sm-Co) alloy, a novel scheme is proposed linking physics-based micromagnetics on the nanostructure level and magnetostatic homogenization on the mesoscale polycrystal...

High performance magnets play an important role in critical issues of modern life such as renewable energy supply, independence of fossile resource and electro mobility. The performance optimization of the established magnetic material system relies mostly on the microstructure control and modification. Here, finite element based in‐silico characte...

Modelling elastocaloric effect (eCE) is crucial for the design of environmentally friendly and energy-efficient eCE based solid-state cooling devices. Here, a thermodynamically consistent non-isothermal phase-field model (PFM) coupling martensitic transformation with mechanics and heat transfer is developed and applied for simulating eCE. The model...

Data-driven thermal and percolation analyses are conducted to elucidate the effects of various characteristics on the effective thermal conductivity of complex 3D composite structures. These characteristics include the thermal and geometric properties of the composite constituents, the interface resistance, and the existence of percolation paths. A...

(accepted in npj comput. mater.) Designing the microstructure of Fe-Ni permalloy by additive manufacturing (AM) opens new avenues to tailor the materials' magnetic properties. Yet, AM-produced parts suffer from spatially inhomogeneous thermal-mechanical and magnetic responses, which are less investigated in terms of process simulation and modeling...

Despite their rapid emergence as the dominant paradigm for electrochemical energy storage, the full promise of lithium-ion batteries is yet to be fully realized, partly because of challenges in adequately resolving common degradation mechanisms. Positive electrodes of Li-ion batteries store ions in interstitial sites based on redox reactions throug...

Magnetic materials find wide applications in modern technology. For further materials design and optimization, physics-grounded micromagnetic simulations play a critical role, as predictions of properties, regarding the materials to be examined, can be made on the basis of in silico characterizations. However, micromagnetism, in particular, the Lan...

A system of phase-field equations with strong-coupling through state and gradient dependent non-diagonal mobility matrices is studied. Existence of weak solutions is established by the Galerkin approximation and a-priori estimates in strong norms. Relative energy estimates are used to derive a general nonlinear stability estimate. As a consequence,...

Dislocations are usually expected to degrade electrical, thermal and optical functionality and to tune mechanical properties of materials. Here, we demonstrate a general framework for the control of dislocation–domain wall interactions in ferroics, employing an imprinted dislocation network. Anisotropic dielectric and electromechanical properties a...

Physics informed neural networks (PINNs) are capable of finding the solution for a given boundary value problem. Here, the training of the network is equivalent to the minimization of a loss function that includes the governing (partial differential) equations (PDE) as well as initial and boundary conditions. We employ several ideas from the finite...

Sintering, as a thermal process at elevated temperature below the melting point, is widely used to bond contacting particles into engineering products such as ceramics, metals, polymers, and cemented carbides. Modelling and simulation as important complement to experiments are essential for understanding the sintering mechanisms and for the optimiz...

The origins of performance degradation in batteries can be traced to atomistic phenomena, accumulated at mesoscale dimensions, and compounded up to the level of electrode architectures. Hyperspectral X-ray spectromicroscopy techniques allow for the mapping of compositional variations, and phase separation across length scales with high spatial and...

This contribution presents convolutional neural nets (CNN) based surrogate models for prediction of von Mises stress and equivalent plastic strain fields of commonly used Dual-Phase (DP) steels in automotive applications. The models predict field quantities in an end-to-end manner, driven by segmented phase images from real experimental scanning el...

accepted in Materials & Design (https://doi.org/10.1016/j.matdes.2023.111746)

(accepted on Phys. Rev, E) In this work, we present for the first time a variational and quantitative phase-field model for non-isothermal sintering processes. The model is derived via an extended non-diagonal model with no restriction on the range of diffusivity ratio. The model evolution equations have naturally cross-coupling terms between the...

Thermal fracture is prevalent in many engineering problems and is one of the most devastating defects in metal additive manufacturing. Due to the interactive underlying physics involved, the computational simulation of such a process is challenging. In this work, we propose a thermo-mechanical phase-field fracture model, which is based on a thermod...

This paper combines the finite element (FE) homogenisation and structural assessments to conduct the multiscale modelling of laminated functionally graded (FG) porous beams made of closed-cell foams, with a focus on the beam buckling and vibration performances. In the FE homogenisation, representative volume elements (RVEs) are built up according t...

Physics-informed neural networks (PINNs) are capable of finding the solution for a given boundary value problem. We employ several ideas from the finite element method (FEM) to enhance the performance of existing PINNs in engineering problems. The main contribution of the current work is to promote using the spatial gradient of the primary variable...

Coating the carbon layer onto the surface of TiO2 electrode is seen as an effective strategy to improve the electrochemical performance of lithium-ion batteries, particularly the reduced charge transfer resistance. The reduced charge transfer resistance was attributed to the chemical bonding between the phases. In this paper, however, we show that...

Dislocations in lithium-ion battery materials significantly influence the performance of the battery. A chemo-mechanical phase-field model combined with a non-singular continuum theory of dislocations is developed to simulate misfit dislocations at the phase boundary of two-phase LiFePO4 particles. The configurational mechanics of dislocations in t...

A three-dimensional phase-field model is proposed for simulating the magnetic martensitic phase transformation. The model considers a paramagnetic cubic austenite to ferromagnetic tetragonal martensite transition, as it occurs in magnetic Heusler alloys like Ni2 MnGa, and is based on a Landau 2-3-4 polynomial with temperature dependent coefficients...

A seamless and lossless transition of the constitutive description of the elastic response of materials between atomic and continuum scales has been so far elusive. Here we show how this problem can be overcome by using artificial intelligence (AI). A convolutional neural network (CNN) model is trained, by taking the structure image of a nanoporous...

Automated particle segmentation and feature analysis of experimental image data are indispensable for data-driven material science. Deep learning-based image segmentation algorithms are promising techniques to achieve this goal but are challenging to use due to the acquisition of a large number of training images. In the present work, synthetic ima...