arXiv subjects

Xiaoying Zhuang

Publications and source records attributed to Xiaoying Zhuang.

At least 73 records · Page 4Linked to original sources

arXiv2021

Silicon diphosphide (SiP2) and silicon diarsenide (SiAs2): Novel stable 2D semiconductors with high carrier mobilities, promising for water splitting photocatalysts

Two dimensional (2D) semiconducting light absorbers, have recently considered as promising components to improve the efficiency in the photocatalytic hydrogen production via water splitting. In this work, by employing density functional theory computations, we introduced novel SiX2 (X = P, As) nanosheets in tetragonal (penta-) and orthorhombic (rec-) phases, as promising light absorber semiconductors for overall water splitting. The predicted nanomembranes exhibit good mechanical, dynamical and thermal stabilities. They also show small cleavage energies in the range of 0.31 J/m2 to 0.39 J/m2, comparable to that of the graphene and thus suggesting the feasibility of their experimental exfoliation. Notably, predicted monolayers are semiconductors with indirect band gaps of 2.65 eV for penta-SiP2, 2.35 eV for penta-SiAs2, 1.89 eV for rec-SiAs2, and a direct band gap of 2.21 eV for rec-SiP2. These nanomaterials however show relatively large interlayer quantum confinement effects, resulting in smaller band gap values for bilayer lattices. We observed a huge difference between the electron and hole mobilities for penta-SiP2 and rec-SiAs2 monolayers and highly directional dependent electron and hole mobilities in rec-SiP2, yielding an effective separation of photogenerated charge carriers. Remarkably, these novel nanomembranes show strong absorption in the visible region of light as well as suitable band edge positions for photocatalytic water splitting reaction, specifically under neutral conditions.

cond-mat.mtrl-sci↗

arXiv2021

Outstandingly high thermal conductivity, elastic modulus, carrier mobility and piezoelectricity in two-dimensional semiconducting CrC2N4: A first-principles study

Experimental realization of single-layer MoSi2N4 is among the latest groundbreaking advances in the field of two-dimensional (2D) materials. Inspired by this accomplishment, herein we conduct first-principles calculations to explore the stability of MC2N4 (M= Cr, Mo, W, V, Nb, Ta, Ti, Zr, Hf) monolayers. Acquired results confirm the desirable thermal, dynamical and mechanical stability of MC2N4 (M= Cr, Mo, W, V) nanosheets. Interestingly, CrC2N4, MoC2N4 and WC2N4 monolayers are found to be semiconductors with band gaps of 2.32, 2.76 and 2.86 eV, respectively, using the HSE06 functional, whereas VC2N4 lattice shows a metallic nature. The direct gap semiconducting nature of CrC2N4 monolayer results in excellent absorption of visible light. The elastic modulus and tensile strength of CrC2N4 nanosheet are predicted to be remarkably high, 676 and 54.8 GPa, respectively. On the basis of iterative solutions of the Boltzmann transport equation, the room temperature lattice thermal conductivity of CrC2N4 monolayer is predicted to be 350 W/mK, among the highest in 2D semiconductors. CrC2N4 and WC2N4 lattices are also found to exhibit outstandingly high piezoelectric coefficients. This study introduces CrC2N4 nanosheet as a novel 2D semiconductor with outstandingly high mechanical strength, thermal conductivity, carrier mobility and piezoelectric coefficient.

cond-mat.mtrl-sci↗

arXiv2021

High tensile strength and thermal conductivity in BeO monolayer: A first-principles study

In a latest experimental advance, graphene-like and insulating BeO monolayer was successfully grown over silver surface by molecular beam epitaxy (ACS Nano 15(2021), 2497). Inspired by this accomplishment, in this work we conduct first-principles based simulations to explore the electronic, mechanical properties and thermal conductivity of graphene-like BeO, MgO and CaO monolayers. The considered nanosheets are found to show desirable thermal and dynamical stability. BeO monolayer is found to show remarkably high elastic modulus and tensile strength of 408 and 53.3 GPa, respectively. The electronic band gap of BeO, MgO and CaO monolayers are predicted to be 6.72, 4.79, and 3.80 eV, respectively, using the HSE06 functional. On the basis of iterative solutions of the Boltzmann transport equation, the room temperature lattice thermal conductivity of BeO, MgO and CaO monolayers are predicted to be 385, 64 and 15 W/mK, respectively. Our results reveal substantial decline in the electronic band gap, mechanical strength and thermal conductivity by increasing the weight of metal atoms. This work highlights outstandingly high thermal conductivity, carrier mobility and mechanical strength of insulating BeO nanosheets and suggest them as promising candidates to design strong and insulating components with high thermal conductivities.

cond-mat.mtrl-sci↗

arXiv2021

Ultrahigh stiffness and anisotropic Dirac cones in BeN4 and MgN4 monolayers: A first-principles study

Beryllium polynitrides, BeN4 is a novel layered material, which has been most recently fabricated under high pressure (Phys. Rev. Lett. 126(2021), 175501). As a new class of 2D materials, in this work we conduct first-principles calculations to examine the stability and explore the electronic nature of MN4 (M= Be, Mg, Ir, Rh, Ni, Cu, Au, Pd, Pt) monolayers. Acquired results confirm the dynamical and thermal stability of BeN4, MgN4, IrN4, PtN4 and RhN4 monolayers. Interestingly, BeN4 and MgN4 monolayers are found to show anisotropic Dirac cones in their electronic structure. While PtN4 monolayer is predicted to be a narrow band-gap semiconductor, IrN4 and RhN4 monolayers are found to be metallic systems. We also elaborately explore the effects of number of atomic layers on the electronic features of BeN4 nanosheets, which reveal highly appealing physics. Our results highlight that BeN4 nanosheet yield ultrahigh elastic modulus and mechanical strength, outperforming all other carbon-free 2D materials. Notably, RhN4 nanosheet is predicted to yield high capacities of 562, 450 and 900 mAh/g, for Li, Na and Ca ions storages, respectively. This study provides a comprehensive understanding on the intrinsic properties of MN4 nanosheets and highlight their outstanding physics.

cond-mat.mtrl-sci↗

arXiv2021

Machine learning assisted multiscale modeling of composite phase change materials for Li-ion batteries thermal management

In this work, we develop a combined convolutional neural networks (CNNs) and finite element method (FEM) to examine the effective thermal properties of composite phase change materials (CPCMs) consisting of paraffin and copper foam. In this approach, first the CPCM microstructures are modeled using FEM and next the image dataset with corresponding thermal properties is created. The image dataset is subsequently used to train and test the CNN performance, which is then compared with the performance of a popular network architecture for image classification tasks. The predicted thermal properties are employed to define the properties of the CPCM material of a battery pack. The heat generation and electrochemical response of a Li-ion cell during the charging/discharging is simulated by applying Newman battery model. Thermal management is achieved by the latent heat of paraffin, with copper foam for enhancing the thermal conductivity. The multiscale model is finally developed using FEM to investigate the effectiveness of the thermal management of the battery pack. In these models the thermal properties estimated by the FEM and the CNN are employed to define the CPCM materials properties of a battery pack. Our results confirm that the model developed on the basis of a CNN can evaluate the effectiveness of the battery packs thermal management system with an excellent accuracy in comparison with the original FEM models.

physics.comp-ph↗

arXiv2021

Nonlocal strong forms of thin plate, gradient elasticity, magneto-electro-elasticity and phase field fracture by nonlocal operator method

The derivation of nonlocal strong forms for many physical problems remains cumbersome in traditional methods. In this paper, we apply the variational principle/weighted residual method based on nonlocal operator method for the derivation of nonlocal forms for elasticity, thin plate, gradient elasticity, electro-magneto-elasticity and phase field fracture method. The nonlocal governing equations are expressed as integral form on support and dual-support. The first example shows that the nonlocal elasticity has the same form as dual-horizon non-ordinary state-based peridynamics. The derivation is simple and general and it can convert efficiently many local physical models into their corresponding nonlocal forms. In addition, a criterion based on the instability of the nonlocal gradient is proposed for the fracture modelling in linear elasticity. Several numerical examples are presented to validate nonlocal elasticity and the nonlocal thin plate .

math.NA↗

arXiv2021

A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate

In this paper, a deep collocation method (DCM) for thin plate bending problems is proposed. This method takes advantage of computational graphs and backpropagation algorithms involved in deep learning. Besides, the proposed DCM is based on a feedforward deep neural network (DNN) and differs from most previous applications of deep learning for mechanical problems. First, batches of randomly distributed collocation points are initially generated inside the domain and along the boundaries. A loss function is built with the aim that the governing partial differential equations (PDEs) of Kirchhoff plate bending problems, and the boundary/initial conditions are minimised at those collocation points. A combination of optimizers is adopted in the backpropagation process to minimize the loss function so as to obtain the optimal hyperparameters. In Kirchhoff plate bending problems, the C1 continuity requirement poses significant difficulties in traditional mesh-based methods. This can be solved by the proposed DCM, which uses a deep neural network to approximate the continuous transversal deflection, and is proved to be suitable to the bending analysis of Kirchhoff plate of various geometries.

math.NA↗

arXiv2020

Exceptional piezoelectricity, high thermal conductivity and stiffness and promising photocatalysis in two-dimensional MoSi2N4 family confirmed by first-principles

Chemical vapor deposition has been most recently employed to fabricate centimeter-scale high-quality single-layer MoSi2N4 (Science; 2020;369; 670). Motivated by this exciting experimental advance, herein we conduct extensive first-principles based simulations to explore the stability, mechanical properties, lattice thermal conductivity, piezoelectric and flexoelectric response, and photocatalytic and electronic features of MA2Z4 (M = Cr, Mo, W; A = Si, Ge; Z = N, P) monolayers. The considered nanosheets are found to exhibit dynamical stability and remarkably high mechanical properties. Moreover, they show diverse electronic properties from antiferromagnetic metal to half metal and to semiconductors with band gaps ranging from 0.31 to 2.57 eV. Among the studied nanosheets, the MoSi2N4 and WSi2N4 monolayers yield appropriate band edge positions, high electron and hole mobilities, and strong visible light absorption, highly promising for applications in optoelectronics and photocatalytic water splitting. The MoSi2N4 and WSi2N4 monolayers are also predicted to show outstandingly high lattice thermal conductivity of 440 and 500 W/mK, respectively. For the first time we show that machine learning interatomic potentials trained over small supercells can be employed to examine the flexoelectric and piezoelectric properties of complex structures. As the most exciting finding, WSi2N4, CrSi2N4 and MoSi2N4 are found to exhibit the highest piezoelectric coefficients, outperforming all other-known 2D materials. Our results highlight that MA2Z4 nanosheets not only undoubtedly outperform the transition metal dichalcogenides family but also can compete with graphene for applications in nanoelectronics, optoelectronic, energy storage/conversion and thermal management systems.

cond-mat.mtrl-sci↗

arXiv2020

Stochastic analysis of heterogeneous porous material with modified neural architecture search (NAS) based physics-informed neural networks using transfer learning

In this work, a modified neural architecture search method (NAS) based physics-informed deep learning model is presented for stochastic analysis in heterogeneous porous material. Monte Carlo method based on a randomized spectral representation is first employed to construct a stochastic model for simulation of flow through porous media. To solve the governing equations for stochastic groundwater flow problem, we build a modified NAS model based on physics-informed neural networks (PINNs) with transfer learning in this paper that will be able to fit different partial differential equations (PDEs) with less calculation. The performance estimation strategies adopted is constructed from an error estimation model using the method of manufactured solutions. A sensitivity analysis is performed to obtain the prior knowledge of the PINNs model and narrow down the range of parameters for search space and use hyper-parameter optimization algorithms to further determine the values of the parameters. Further the NAS based PINNs model also saves the weights and biases of the most favorable architectures, then used in the fine-tuning process. It is found that the log-conductivity field using Gaussian correlation function will perform much better than exponential correlation case, which is more fitted to the PINNs model and the modified neural architecture search based PINNs model shows a great potential in approximating solutions to PDEs. Moreover, a three dimensional stochastic flow model is built to provide a benchmark to the simulation of groundwater flow in highly heterogeneous aquifers. The NAS model based deep collocation method is verified to be effective and accurate through numerical examples in different dimensions using different manufactured solutions.

cs.LG↗

arXiv2020

Laser Exfoliation of Graphene from Graphite

Synthesis of graphene with reduced use of chemical reagents is essential for manufacturing scale-up and to control its structure and properties. In this paper, we report on a novel chemical-free mechanism of graphene exfoliation from graphite using laser impulse. Our experimental setup consists of a graphite slab irradiated with an Nd:YAG laser of wavelength 532 nm and 10 ns pulse width. The results show the formation of graphene layers with conformational morphology from electron microscopy and Raman spectra. Based on the experimental results, we develop a simulation set up within the framework of the molecular dynamics that supplies the laser-induced electromagnetic energies to atoms in the graphite slab. We investigate the influence of different laser fluence on the exfoliation process of graphene. The variations in inter-layer interaction energy and inter-layer distance are the confirmative measures for the possible graphene layer formation. The simulation results confirm the exfoliation of a single layer graphene sheet for the laser power ranging from 100x10^(-14) to 2000x10^(-14) J/nm2. With an increase of laser fluence from 2000x10^(-14) to 4000x10^(-14) J/nm2, there is an increase in the graphene yield via the layer-after-layer exfoliation. The bridging bond dynamics between the successive graphene layers govern the possibility of second-layer exfoliation. The experimental and simulation observations are useful and promising for producing chemical-free graphene on a large scale for industrial and commercial applications.

physics.app-ph↗

arXiv2020

Integrated intelligent Jaya Runge-Kutta method for solving Falkner-Skan equations for Various Wedge Angles

In this work, the hybrid intelligent computing method, which combines efficient Jaya algorithm with classical Runge-Kutta method is applied to solve the Falkner-Skan equations with various wedge angles, which is the fundamental equation for a variety of computational fluid mechanical problems. With some coordinate transformation, the Falkner-Skan boundary layer problem is then converted into a free boundary problem defined on a finite interval. Then using higher order reduction strategies, the whole problem can be boiled down to a solving of coupled differential equations with prescribed initial and boundary conditions. The hybrid Jaya Runge-Kutta method is found to yield stable and accurate results and able to extract those unknown parameters. The sensitivity of classical shooting method to the guess of initial values can be easily overcome by an integrated robust optimization method. In addition, the Jaya algorithm, without the need for tuning the algorithm-specific parameters, is proved to be effective and stable for minimizing the fitness function in application. By comparing the solutions using the Jaya method with PSO (particle swarm optimization), Genetic algorithm (GA), Hyperband, and the classical analytical methods, the hybrid Jaya Runge-Kutta method yields more stable and accurate results, which shows great potential for solving more complicated multi-field and multiphase flow problems.

math.NA↗

arXiv2020

Deep Autoencoder based Energy Method for the Bending, Vibration, and Buckling Analysis of Kirchhoff Plates

In this paper, we present a deep autoencoder based energy method (DAEM) for the bending, vibration and buckling analysis of Kirchhoff plates. The DAEM exploits the higher order continuity of the DAEM and integrates a deep autoencoder and the minimum total potential principle in one framework yielding an unsupervised feature learning method. The DAEM is a specific type of feedforward deep neural network (DNN) and can also serve as function approximator. With robust feature extraction capacity, the DAEM can more efficiently identify patterns behind the whole energy system, such as the field variables, natural frequency and critical buckling load factor studied in this paper. The objective function is to minimize the total potential energy. The DAEM performs unsupervised learning based on random generated points inside the physical domain so that the total potential energy is minimized at all points. For vibration and buckling analysis, the loss function is constructed based on Rayleigh's principle and the fundamental frequency and the critical buckling load is extracted. A scaled hyperbolic tangent activation function for the underlying mechanical model is presented which meets the continuity requirement and alleviates the gradient vanishing/explosive problems under bending analysis. The DAEM can be easily implemented and we employed the Pytorch library and the LBFGS optimizer. A comprehensive study of the DAEM configuration is performed for several numerical examples with various geometries, load conditions, and boundary conditions.

cs.LG↗

arXiv2020

A micropolar peridynamics model with non-unified horizon for damage of solids with different non-local effects

Most peridynamics models adopt regular point distribution and unified horizon, limiting their flexibility and engineering applications. In this work, a micropolar peridynamics approach with non-unified horizon (NHPD) is proposed. This approach is implemented in a conventional finite element framework, using element-based discretization. By modifying the dual horizon approach into the pre-processing part, point dependent horizon and non-unified beam-like bonds are built. By implementing a domain correction strategy, the equivalence of strain energy density is assured. Then, a novel energy density-based failure criterion is presented which directly bridges the critical stretch to the mechanical strength. The numerical results indicate the weak mesh dependency of NHPD and the effectiveness of the new failure criterion. Moreover, it is proven that damage of solid with different non-local effects can lead to similar results by only adjusting the mechanical strength.

cs.CE↗

arXiv2020

Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution

Accurate evaluation of the thermal conductivity of a material can be a challenging task from both experimental and theoretical points of view. In particular for the nanostructured materials, the experimental measurement of thermal conductivity is associated with diverse sources of uncertainty. As a viable alternative to experiment, the combination of density functional theory (DFT) simulations and the solution of Boltzmann transport equation is currently considered as the most trusted approach to examine thermal conductivity. The main bottleneck of the aforementioned method is to acquire the anharmonic interatomic force constants using the computationally demanding DFT calculations. In this work we propose a substantially accelerated approach for the evaluation of anharmonic interatomic force constants via employing machine-learning interatomic potentials (MLIPs) trained over short ab-initio molecular dynamics trajectories. The remarkable accuracy of the proposed accelerated method is confirmed by comparing the estimated thermal conductivities of several bulk and two-dimensional materials with those computed by the full-DFT approach. The MLIP-based method proposed in this study can be employed as a standard tool, which would substantially accelerate and facilitate the estimation of lattice thermal conductivity in comparison with the commonly used full-DFT solution.

cond-mat.mtrl-sci↗

arXiv2020

Machine-Learning Interatomic Potentials Enable First-Principles Multiscale Modeling of Lattice Thermal Conductivity in Graphene/Borophene Heterostructures

One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as the density functional theory (DFT) provide the best possible accuracy on electronic properties but they are limited to systems up to a few hundreds, or at most thousands of atoms. On the other hand, classical molecular dynamics (CMD) simulations and finite element method (FEM) are extensively employed to study larger and more realistic systems, but conversely depend on empirical information. Here, we show that machine-learning interatomic potentials (MLIPs) trained over short ab-initio molecular dynamics trajectories enable first-principles multiscale modeling, in which DFT simulations can be hierarchically bridged to efficiently simulate macroscopic structures. As a case study, we analyze the lattice thermal conductivity of coplanar graphene/borophene heterostructures, recently synthesized experimentally (Sci. Adv. 2019; 5: eaax6444), for which no viable classical modeling alternative is presently available. Our MLIP-based approach can efficiently predict the lattice thermal conductivity of graphene and borophene pristine phases, the thermal conductance of complex graphene/borophene interfaces and subsequently enable the study of effective thermal transport along the heterostructures at continuum level. This work highlights that MLIPs can be effectively and conveniently employed to enable first-principles multiscale modeling via hierarchical employment of DFT/CMD/FEM simulations, thus expanding the capability for computational design of novel nanostructures.

cond-mat.mtrl-sci↗

arXiv2020

High thermal conductivity in semiconducting Janus and non-Janus diamanes

Most recently, F-diamane monolayer was experimentally realized by the fluorination of bilayer graphene. In this work we elaborately explore the electronic and thermal conductivity responses of diamane lattices with homo or hetero functional groups, including: non-Janus C2H, C2F and C2Cl diamane and Janus counterparts of C4HF, C4HCl and C4FCl. Noticeably, C2H, C2F, C2Cl, C4HF, C4HCl and C4FCl diamanes are found to show electronic diverse band gaps of, 3.86, 5.68, 2.42, 4.17, 0.86, and 2.05 eV, on the basis of HSE06 method estimations. The thermal conductivity of diamane nanosheets was acquired using the full iterative solutions of the Boltzmann transport equation, with substantially accelerated calculations by employing machine-learning interatomic potentials in obtaining the anharmonic force constants. According to our results, the room temperature lattice thermal conductivity of graphene and C2H, C2F, C2Cl, C4HF, C4HCl and C4FCl diamane monolayers are estimated to be 3636, 1145, 377, 146, 454, 244 and 196 W/mK, respectively. The underlying mechanisms resulting in significant effects of functional groups on the thermal conductivity of diamane nanosheets were thoroughly explored. Our results highlight the substantial role of functional groups on the electronic and thermal conduction responses of diamane nanosheets.

cond-mat.mtrl-sci↗

arXiv2020

Nanoporous C3N4, C3N5 and C3N6 nanosheets; Novel strong semiconductors with low thermal conductivities and appealing optical/electronic properties

Carbon nitride two-dimensional (2D) materials are among the most attractive class of nanomaterials, with wide range of application prospects. As a continuous progress, most recently, two novel carbon nitride 2D lattices of C3N5 and C3N4 have been successfully experimentally realized. Motivated by these latest accomplishments and also by taking into account the well-known C3N4 triazine-based graphitic carbon nitride structures, we predicted two novel C3N6 and C3N4 counterparts. We then conducted extensive density functional theory simulations to explore the thermal stability, mechanical, electronic and optical properties of these novel nanoporous carbon-nitride nanosheets. According to our results all studied nanosheets were found to exhibit desirable thermal stability and mechanical properties. Non-equilibrium molecular dynamics simulations on the basis of machine learning interatomic potentials predict ultralow thermal conductivities for these novel nanosheets. Electronic structure analyses confirm direct band gap semiconducting electronic character and optical calculations reveal the ability of these novel 2D systems to adsorb visible range of light. Extensive first-principles based results by this study provide a comprehensive vision on the stability, mechanical, electronic and optical responses of C3N4, C3N5 and C3N6 as novel 2D semiconductors and suggest them as promising candidates for the design of advanced nanoelectronics and energy storage/conversion systems.

physics.comp-ph↗

arXiv2020

Exploring Phononic Properties of Two-Dimensional Materials using Machine Learning Interatomic Potentials

Phononic properties are commonly studied by calculating force constants using the density functional theory (DFT) simulations. Although DFT simulations offer accurate estimations of phonon dispersion relations or thermal properties, but for low-symmetry and nanoporous structures the computational cost quickly becomes very demanding. Moreover, the computational setups may yield nonphysical imaginary frequencies in the phonon dispersion curves, impeding the assessment of phononic properties and the dynamical stability of the considered system. Here, we compute phonon dispersion relations and examine the dynamical stability of a large ensemble of novel materials and compositions. We propose a fast and convenient alternative to DFT simulations which derived from machine-learning interatomic potentials passively trained over computationally efficient ab-initio molecular dynamics trajectories. Our results for diverse two-dimensional (2D) nanomaterials confirm that the proposed computational strategy can reproduce fundamental thermal properties in close agreement with those obtained via the DFT approach. The presented method offers a stable, efficient, and convenient solution for the examination of dynamical stability and exploring the phononic properties of low-symmetry and porous 2D materials.

cond-mat.mtrl-sci↗