Computational Materials Science最新文献

Four layer hexagonal SiC (4H-SiC) is a promising material for high temperature and high radiation environments, attributed to its excellent thermal conductivity and radiation resistance. However, the mechanism of radiation displacement cascades in 4H-SiC remains incomplete. This study employs molecular dynamics (MD) to explore the effects of radiation energy, direction and environmental temperature on displacement cascades in 4H-SiC. We simulated radiation displacement cascades in 4H-SiC under radiation energy ranging from 2 KeV to 10 KeV and temperature ranging from 0 K to 2100 K. We analyzed the variation pattern of radiation defects and clusters. We derived the empirical formulas describing the variation of defects and clusters with radiation energy and radiation direction. We revealed patterns in the number of radiation defects and clusters under different temperature. The findings enhance our understanding of radiation displacement cascades in 4H-SiC, providing valuable empirical formulas for predicting the behaviors of defects and clusters under varying radiation energy and temperature conditions, and have practical implications for designing materials resilient to radiation in semiconductor devices.

Diffusivity of species and defects on grain boundaries is usually several orders of magnitude larger than that inside grains. Such strongly inhomogeneous diffusivity requires prohibitively high computational demands for modeling microstructural evolution. This paper presents a highly efficient numerical solver, combining the Finite Difference method and Random Walk model, designed for accurately modeling strongly inhomogeneous diffusion within polycrystalline structures. The proposed solver, termed Finite Difference informed Random Walk (FDiRW), integrates a customized Finite Difference (cFD) scheme tailored for fast diffusion along thin grain boundaries represented by a single layer of nodes. Numerical experiments demonstrate that the FDiRW solver achieves an impressive efficiency gain of 1560x compared to traditional Finite Difference methods while maintaining accuracy, making it feasible for personal computer machines to handle diffusional systems with strongly inhomogeneous diffusivity across static polycrystalline microstructures. The model has been successfully applied to simulate radiation defect evolution, showcasing its scalability to engineering scales in both length and time dimensions.

In the present study, we systematically explored the kinetic and thermodynamic properties of the ceramic phase MgAlB₄ based on the first-principles calculations, and the adhesion work (W_ad), interfacial energy (γ), atomic structure, and interfacial fracture mechanism of semi-coherent Al(111)/MgAlB₄(0002) interfaces were also explored. The results show that the interfacial constructions of the MT (bridge) sites are unstable and the atoms at the interface move to the interior after relaxation. In addition, the obtained adhesion work and interfacial energy indicate that the stability of the HCP (hollow) sites interfacial configurations are higher than the MT and OT (on-top) sites. The interfacial structure of B-terminated Al(111)/MgAlB₄(0002) HCP site is the most stable because it has the largest adhesion work and the smallest interfacial energy. The interfacial electronic structures indicate the B-Al covalent bonds are formed at the Al(111)/ MgAlB₄(0002) interface, while mechanical failure in the B-terminated HCP site interfacial configuration occurs in the Al phase. Ultimately, the results show that the ceramic phase MgAlB₄ particle reinforcement can effectively enhance the strength and plasticity of the Al-based composites.

Polymer crystallization is an important research topic in materials sciences. The phase field model is employed to simulate the growth of spherulites and shish-kebabs for the semi-crystalline polymer under melt flows. Firstly, the phase field equation is discretized by the finite difference method(FDM), the energy equation is solved by the finite volume method(FVM), and the governing equation of viscous polymer melts is modeled and solved by the lattice Boltzmann method(LBM). And then the numerical simulations are conducted for the growth process of spherulites and shish-kebabs under the static and flowing conditions, respectively. Morever, the growth of shish-kebabs are simulated in two different mold cavities under melt flows and compared with each other. Finally, the growth of co-existed spherulites and shish-kebabs are simulated in both static and flowing states. Numerical results show that the coupled FD-FV-LB algorithm could successfully capture the growth interfaces of spherulites and shish-kebabs. The complex cavity makes the flow more complex, thereby changing the crystal morphologies. The melt flow makes the polymer crystals grow faster and densely towards the upstream direction, and increase the temperatures of the crystals.

A new mixed-mode cohesive zone model based on Lennard-Jones potential (LJCZM) is proposed to simulate the interface failure between graphene and epoxy matrix. The values of model parameters are obtained from a large number of molecular dynamics simulations, and a UMAT subroutine is programmed and validated to introduce this model into the ABAQUS platform. This process spans from the nanoscale to the microscale, which provides a new routine for the multiscale damage modeling of the graphene reinforced epoxy nanocomposite at microscale. In addition, the continuous damage phase-field model is used to simulate the matrix damage, and the values of model parameters are determined from the molecular dynamic simulations of the bulk epoxy at nanoscale. At last, the effects of parameters such as volume fraction, aspect ratio, orientation, and curvature of graphene nanoplatelets are investigated. The results indicate that the nanocomposite reinforced with high content and large aspect ratio graphene nanoplatelets presents the lower ultimate stress and fracture strain. In addition, the orientation and waviness of the graphene also significantly affect the mechanical properties of the nanocomposites. The nanocomposite reinforced with graphene platelets with greater waviness has higher stiffness and strength but lower toughness. The rationality and effectiveness of the model are verified through comparison with other existing results.

Molecular dynamics simulations were conducted on two model antiparallel β-sheet crystallites [GA]n and [GAS]n to study deformation in chain, sheet stacking, and hydrogen bonding directions under uniaxial loading. In chain direction, both models were mechanically stable, even beyond the 570 K amorphousation temperature of silk fiber; however, [GA]n model displayed higher yield strain, stress, elastic modulus, and resilience than [GAS]n. In transverse directions, they had similar stress–strain behavior and demonstrated significant anisotropic mechanical behavior. Hence, inclusion of an amino acid with a rich side chain group extending between β-sheets reduces the stiffness of crystallite in chain direction. Serine and alanine residues maintained existing H-bonds and established new ones during stretching in chain direction and shrinking in transverse directions which affected the mechanical response near the yield point. Comparison between β-sheet crystallite and PPTA (Kevlar) showed that the mechanical performance of these crystal polymers were very similar in chain direction, but contrarily β-sheet crystallite had higher stiffness in H-bonding and sheet stacking directions than PPTA. This study may provide a guideline in designing of polyaminoacid based biocompatible materials with superior mechanical performance.

Recently, the stanene (Sn)/hexagonal boron nitride (h-BN) van der Waals heterostructure (vdW) has garnered significant attention among the scientific community due to its distinctive electrical, optical, and thermal characteristics. Despite the promising potential of this heterostructure, the interfacial thermal resistance (ITR) between the Sn and h-BN layers remains unexplored. Understanding and modulating this ITR are essential steps towards harnessing the maximum potential of these materials in practical nanodevices. This study aims to investigate the interfacial thermal resistance (ITR) between the Sn and h-BN layers through the use of conventional molecular dynamics (MD) simulation. The transient pump–probe heating technique, commonly referred to as the Fast Pump Probe (FPP) approach, is utilized to analyze the ITR of the Sn/h-BN heterostructure. The estimated ITR value of a 30 × 10 nm² Sn/h-BN nanosheet is found to be around ∼ 7 × 10^-8 K.m²/W at room temperature. This study comprehensively investigates the impact of various internal and external parameters including nanosheet size, system temperature, contact pressure, vacancy concentration, and mechanical tensile strain (uniaxial and biaxial) on ITR, providing an extensive understanding of how these factors collectively affect the thermal resistance between Sn and h-BN layers. The simulation results demonstrate a consistent decline in ITR by approximately ∼ 93 %, ∼45 %, ∼65 %, and ∼ 33 % with the increasing system size, temperature, contact pressure, and defect concentration, respectively. In contrast, increasing mechanical strain leads to a substantial enhancement in ITR, with a maximum increase of approximately ∼ 47 % under uniaxial tensile strain and almost ∼ 99 % under biaxial tensile strain. Moreover, the pristine Sn/h-BN heterostructure exhibits no significant thermal rectification effect. The Phonon Density of States (PDOS) profile of the Sn and h-BN layer is calculated to elucidate this underlying behavior of ITR. The PDOS analysis reveals that heat is transported from h-BN to the Sn layer through efficient coupling of low-frequency flexural phonons between these two materials. This work will provide both theoretical support and logical guidelines for modulating thermal resistance across diverse dissimilar material interfaces, which will be necessary for the development of advanced nanodevices used in next-generation nanoelectronics, nanophotonic, and optoelectronics applications.

Powder metallurgy hot isostatic pressing (PM-HIP) has emerged as a promising alternative to welding for joining dissimilar metals. During HIP, interfacial bonding is mediated by solid state diffusion. The interdiffusion zone across the interface depends on processing conditions, calling for the need for accurate numerical tools capable of simulating interdiffusion and possible phase transformation in order to optimize processing parameters. Here, a phase-field (PF) model based on CALPHAD-based free energy functionals is developed to simulate the interdiffusion and phase evolution between dissimilar Fe–Cr–Ni based steels undergoing HIP and is demonstrated using the interface between 316L and SA508 steels. To overcome the numerical challenges caused by the singular magnetic and entropy terms in the CALPHAD free energy models in the Fe–Cr-Ni system, polynomial functions are fitted with temperature dependent coefficients represented by Fourier series to accurately describe the phase stability of both fcc and bcc phases in the composition and temperature space. This enables simulations of non-isothermal HIP cycles. Diffusivity data from commercial software and literature are taken to parameterize the kinetic parameters. A discrete nucleation model is incorporated for possible phase transformation. The modified thermodynamic models are validated against previous experiments at 923 K and 1273 K. The interdiffusion kinetics are benchmarked against new HIP experiments joining powder and bulk 316L to bulk SA508 with three different HIP cycles. The good agreement between simulations and experiments on both phase stability and interdiffusion indicate that the model is suitable for simulating interdiffusion between Fe–Cr–Ni alloys during HIP cycles. It is also found that using powder and bulk 316L gives similar interdiffusion profiles at elevated temperature when a dense interface forms during HIP.

The paper considers electronic structure of pristine and defective nickel ferrite (spinel $\text{Ni}{\text{Fe}}_2{\text{O}}_4$). The orbital ordering, band gap and charge transfer are studied in the framework of density functional theory with account of strong electronic correlations (DFT+U method). The possibility of changing the type of polaron transport in the presence of oxygen vacancies and nickel antisites has been demonstrated. The corresponding non-adiabatic activation barriers of polaron transport is considered. The resulting hopping energies are in general agreement with experimentally observed activation energies. The highlighted influence of point defects on the polaron conductivity mechanism could be a suitable explanation for the large variability of activation energies in previous experimental works. NEGF-DFT calculations were also performed to consider a possible band conduction mechanism. The enhanced conduction with the presence of oxygen bi-vacancies, and a change in carrier type is also observed.

Data-driven techniques are used to predict the actuation strain (AS) of NiTiHfX shape memory alloy (SMA). A Machine Learning (ML) approach is used to overcome the high dimensional dependency of NiTiHfX AS on numerous factors, as well as the lack of fully known governing physics. Detailed data extraction on available experimental studies is performed to gather any related information about the actuation strain. The elemental composition, manufacturing approaches, thermal treatments, applied stress, and post-processing steps that are commonly used to process NiTiHfX and have an impact on the material AS are used as input parameters of the ML models. Since a broad data collection is performed the information for each input factor was sufficient for the use of the majority of the accessible information in the literature on NiTiHfX AS. Considering most of the regular NiTiHfX processing factors also enables the option of tuning additional characteristics of NiTiHfX in addition to the ASs. The work is unique as is the first to fully investigate the NiTiHfX actuation strain prediction.

To forecast the NiTiHfX AS, a total of 901 data sets or 17,119 data points for eighteen inputs and one output were gathered, verified, and selected. Several machine-learning approaches were applied and joined to gather to guarantee robust modeling. The global model’s overall determination factor (R²) was 0.96, suggesting the viability of the proposed NN model. Such a model opens the possibility of intelligent material selection and processing to maximize the AS or shape memory effect of NiTiHf SMA.