We study the electrical and optical properties of the van der Waals (vdW) heterojunction Be2C/graphene by first principles. The constructed heterojunction exhibits p-type Ohmic contact. It is found that the varied interlayer distance and biaxial stress can influence the electronic structure and charge transfer of heterojunction. While the small variety of the difference of electrostatic potential of the heterojunction induces the small change of charge transfer at the contact interface, so the p-type Ohmic contact can still be formed. Meanwhile, the decreased interlayer distance and tensile strain can enhance the optical absorption of heterojunction. Then we calculate the external quantum efficiency of the optoelectronic device based on Be2C/graphene that reaches to 27.8%. This work provides a way to design electronic and optoelectronic devices based on Be2C/graphene in the future.

A charge-optimized many-body (COMB3) potential has been developed for the Al-O-Se-Zn system using first-principles data. This work updates COMB3 for zinc and extends it to include selenium as well as other pairwise and three-body interactions that were not developed previously. We tested the empirical potential for known crystalline phases and find reasonable agreement between the calculated structural, elastic, and vibrational properties and experimental data. We use the anisotropic coefficients of the thermal expansion method to calculate the temperature-dependent properties of a few selected materials. We then compare the total energy of the system and its first- and second-order positional derivatives, as well as the second-order strain derivative with respect to the atomic potential energy surface to thermal properties obtained from molecular dynamics simulations using the fitted COMB3 potential. The results of our temperature-dependent calculations show reasonable agreement with previous work for temperatures below the system’s melting point. With this fitting, we could utilize a versatile, charge-dependent empirical potential to model the interface between ZnSe and Al2O3 by molecular dynamics simulations.

The structural, magnetic and electronic properties of Mn2YSn (Y=Sc, Ti, and V) Heusler alloys under applied hydrostatic pressure are studied by the first-principle calculations and Monte Carlo simulations. We find that two magnetic reference states with low and high magnetic moment at the low and high crystal lattice volume, respectively, can coexist together due to the almost equal energy under applied pressure of 3.4, −2.9 and −3.25 GPa for Mn2ScSn, Mn2TiSn, and Mn2VSn, correspondingly. The positive/negative pressure correspond to the uniform lattice contraction/expansion. We show that for all compounds, the low magnetic state (LMS) is characterized by the almost half-metallic behavior and it is maintained against hydrostatic pressure. However, the electronic structure of the high magnetic state (HMS) takes on a metallic character. For HMS, the magnetic exchange parameters and Curie temperatures are found to be sufficiently larger values as compared to those of LMS. To identify stable phases at given pressures, the phase diagrams are constructed. The pressure-induced switching mechanism between almost half-metallic and metallic states with different magnetization is proposed.

The austenitization of an initial pearlitic microstructure is simulated using the phase field model to achieve insight into White Etching Layer (WEL) formation in pearlitic railway steels. The simulations take into account the resolution of the cementite lamellae within a pearlite colony as well as the presence of pro-eutectoid ferrite. The austenite growth kinetics and morphology obtained via simulations are compared with dilatometry and microscopy observations. The influence of γ/θ and γ/α mobilities on the austenite growth morphology are studied. The simulations reproduce the microstructural features as well as the experimentally observed kinetics behavior of austenite formation, involving the correlation between mobilities and nucleation behavior.

Applying the advanced CALYPSO structure searching method and first principles calculations, we predict a novel superhard structure for Ti2B5, which belongs to the monoclinic C2/m space group. The previously known Hf2B5, W2B5, and δ-V2O5-type structures are also selected as the potential structures for Ti2B5. It is shown that the predicted C2/m structure is the most stable phase among these considered structures within the range of 0-100GPa. The calculations of the phonon dispersion and elastic constants affirm that the predicted C2/m phase is dynamically and mechanically stable. The predicted high shear modulus (249 GPa) and large hardness (46.9 GPa) show that it should be an underlying superhard material. In addition, the elastic anisotropy of Ti2B5 is also explored by investigating the directional dependence of the Young's modulus, shear modulus, and Poisson's ratio as well as some well anisotropy indices. The particular analyses of the density of states and electron charge density distribution verify that the strong B-B and Ti-B covalent bonds in the predicted C2/m-Ti2B5 phase play an important part in its hardness and structural stability.

Machine learning (ML) algorithms have recently shown considerable success in the field of materials science, particularly for modeling novel materials, analyzing enormous volumes of data, and making material property predictions using such data. In this study, the best-suited machine learning algorithm was investigated to predict the fracture behavior of Al2O3-Cr2O3 ceramics in different Cr2O3 volume ratios (0.5, 1, 5) under bending. To improve the performance of the model, the hyperparameters were optimized and MLP (Multi-layer Perceptron), RF (Random Forest), and XGBoost (Extreme Gradient Boosting) algorithms were used. The leave-one-out technique was employed as a cross-validation method to make sure the consistency of the results. The heat map technique was used to choose the appropriate input and output parameters from the dataset. The effect of the input factors was also studied using the experimental method of surface response. The RF model provided the closest predictions to the experimental values for most of the samples. The most important input variable was found to be the Cr2O3 ratio for the relative density, diameter for the fracture strength, and thickness for the total crack length according to the feature importance results from the RF algorithm. The optimal solution found by the GA (Genetic Algorithm) is 0.7 for Cr2O3% concentration, with a corresponding diameter of 28.5 mm and a thickness of 2.2 mm with a fracture strength of 325.8 MPa.

Abstract not available

The band-gap frequencies of elastic metamaterials are ideally determined by a metamaterial architecture; yet, in practical situations, are often dependent on the material damping in their constituent(s). The analysis of viscoelastic metamaterials requires however substantial computational resources and, except for oversimplified cases, is solely done numerically. Here, we propose an analytical procedure based on the spectral element method (SEM) to analyze bulk metamaterials with viscoelastic damping as continuous systems. Due to intrinsic limitations of the SEM to deal with complex geometries, we develop a procedure to build an approximate model based on SEM frame elements. The viscoelastic behavior is included by means of complex viscoelasticity moduli expressed by the generalized Maxwell mechanical model. We validate this approach by analyzing metamaterial plates and verify the findings experimentally. We demonstrate that our SEM-based analytical model can accurately capture wave transmission around the first band-gap frequencies. Therefore, our extension of the SEM approach to analyze three-dimensional meta-structures is promising to characterize wave propagation in realistic viscoelastic structures (with any type of linear viscoelastic behavior) in an accurate and computationally efficient way.

The paper presents results of atomistic simulations of hardening in model bcc iron-based alloys due to secondary phase precipitates formation. The simulations are based on shear stress relaxation technique and experimental data on morphology of precipitates. Atomistic methods were developed to reproduce the shear strength of bcc iron-based materials. The large-scale atomistic simulations were carried out for model binary alloys: Fe–Cr imitating low-carbon ferritic and ferritic–martensitic steels and Fe–Cu imitating low-carbon bainitic steel. A representative set of atomic structures is compiled with account for the chemical composition and concentration and size of secondary phase, comparable with those in steels of the considered classes and available experimental data on radiation and thermal induced precipitation. Obtained results on hardening were verified against experimental data and the dispersed barrier hardening model.

Sampling potential energy surface (PES) is critical for many problems in materials science, chemistry, physics, and biology and requires highly efficient saddle point searches (SPS). In the study, we introduce the concept of dynamic active volume (DAV) in addition to the active volume in self-evolving atomistic kinetic Monte Carlo (SEAKMC). The DAV method has further reduced the dimensionality of the PES at the elevation stage of a SPS. At the subsequent converging stage, the dynamic boundary in DAV is lifted to allow the system to converge to the right location of a saddle point. Coupled with the dimer method, the DAV method not only significantly reduces the time cost for a given search attempt, but also dramatically increases the probability of finding relevant saddle points for PES sampling. A Python software package within the framework SEAKMC (SEAKMC_py) with the DAV method has been developed.

Recent advances in improving the computational efficiency of the phase-field simulations of solidification microstructures are reviewed. The parallel progress of four typical approaches, namely, multigrid, adaptive mesh refinement method, semi-implicit Fourier spectral method, and graphical processing units (GPUs) architecture, is highlighted. Large-scale spatiotemporal simulations are successfully performed to cover essential aspects of multiphysics with the capability of these algorithms. Focus is put on the principles, applications, and comparison of the four algorithms, while solidification theories and discretization methods are outlined.

This study comprehensively examined the structural, dynamical, electronic, and optical characteristics of SrMoO3 as an electrode material for the confining conducting filament in resistive random access memory (RRAM). This study is novel in that it examines the compositional control and interaction between extrinsic defects (dopants) and intrinsic defects (oxygen vacancies) acting as a transition driving force in thermodynamically stable composites for a resistive switching mechanism. In the absence of oxygen vacancies (Vos), relatively strong, confined metal-cation-based filaments formed around the dopant proved that extrinsic defects caused by substitutional dopants replacing Mo atoms are more efficient at producing low-resistance states in SrMoO3 than intrinsic defects. Filaments of the order of a few Ås around the defect site will help to solve the uniformity and scaling problems associated with resistive switching. This finding further supported the usefulness of noise (in this case, a dopant or Vos is the source of an external or internal perturbation). The optical conductivity produced in response to incident photons in the infrared energy range also verified the suitability of doped SrMoO3 systems for optoelectronic RRAM synaptic devices. This study's potential energy lineup-based dopant selection rule suggested that substitutional doping using the heavy transition metal Hf as an acceptor dopant yields relatively excellent results for applications involving low-power SrMoO3-based RRAM synaptic devices.

In order to better understand thermal aging mechanism of reactor pressure vessel steels with the action of dislocation loop, the hardening behavior and microstructure evolution of Fe-10at. %Cu-3at. %Mn-1.5at. %Ni-1.5at. %Al alloy is investigated using phase-field method. The results showed that the precipitation includes spinodal decomposition of Cu-rich precipitates (Cu/Ni (Al, Mn) core–shell structure) assisted by dislocation loops, spinodal decomposition assisted by new dislocations, and nucleation and growth induced by thermal aging. Ultimately, a gradient of Cu-rich nanoprecipitates is formed, which can result in high strength-ductility synergy. Phase-field method provides better details for predicting the morphology, distribution, and size of Cu-rich precipitates in Fe-Cu-Mn-Ni-Al alloy with dislocation loops. The simulation results are introduced into the hardening model of copper-rich precipitates to achieve a closed-loop of process → microstructure → performance.

The thermodynamic stability and growth kinetics of Pt-Sn phases are investigated by atomistic simulations utilizing a neural-network machine learning (NN-ML) interatomic potential. The physical properties of Pt-Sn crystalline phases described by the NN-ML interatomic potential, such as equation of states, formation energy convex hull, and phonon vibrational spectrum, are in in accord well with first-principles calculations and experimental data. The calculations of temperature dependent Gibbs free energies of the crystalline Pt-Sn phases by the NN-ML potential are in the efficiency of empirical interatomic potentials and accuracy of density functional theory (DFT). The developed NN-ML potential is also used to investigate the structures and dynamics of liquid phases of Pt-Sn alloys by molecular dynamics (MD) simulations. The crystallization of PtSn and Pt3Sn phases from the solid–liquid interface are also studied by MD simulations using the NN-ML potential. The results obtained from our studies provide useful insight into thermodynamics stability and growth kinetics of Pt-Sn binary phases.

This article presents advances and computing optimizations on the CAPTN model which couples the Cellular Automaton (CA) and the Parabolic Thick Needle (PTN) methods. This optimized CAPTN model, which is developed in 2D for now, is evaluated on its ability to reproduce two physical quantities developed during directional growth in a constant temperature gradient G with isotherm velocity vL: the interdendritic primary spacing and the grain boundary orientation angle between two grains of different orientations. It is shown that the CAPTN model can reproduce selection between primary branches and creation of new branches from tertiary branches as long as cell size is sufficiently small to model solute interactions between branches. In these conditions, simulations converge toward a distribution of primary branches which depends on the history of the branching events, as has been observed in experimental studies. Average primary spacing obtained tends to decrease with G and vL, in agreement with the theoretical G−bvL−c power law. Contrary to the classical CA model, the grain boundary orientation angle obtained in CAPTN simulations is stable with cell size and in good agreement with previous phase field studies for various gradients. Moreover, the grain boundary orientation angle is found to follow an exponential law with the ratio G/vL.

Zener pinning refers to the dispersion of fine particles which influences grain size distribution via movement of grain boundaries in a polycrystalline material. Grain size distribution in polycrystals has a significant impact on their properties including physical, chemical, mechanical, and optical to name a few. We explore the use of Phase-field modeling and machine-learning techniques to understand and improve the control of grain size distribution via Zener pinning in polycrystalline materials. We develop a machine learning model that determines the relative importance of various parameters to exercise microstructure control via Zener pinning. Our workflow combines high-throughput phase-field simulations and machine learning to address the computational bottlenecks associated with large-scale simulations as well as identify features necessary for microstructure control in polycrystals. A random forest (RF) regression model was developed to predict grain sizes based on five Phase-field model parameters, achieving an average prediction error of 0.72 nm for the training data and 1.44 nm for the test data. The importance of the input parameters is analyzed using the SHapley Additive exPlanations (SHAP) approach which reveals that diffusivity, volume fraction, and particle diameter are the most important parameters in determining the final grain size. These findings will allow us to select the best second-phase particles, optimize grain size distributions and thus design microstructures with the desired properties. The developed method is a highly versatile and generalizable approach that can be used to assess the combined effects of individual features in the presence of multiple variables.

To fill knowledge gaps between anion sublattice models and real lithium compounds in terms of the influence of Li-anion interaction on Li ion diffusion, herein, molecular dynamics calculations were applied to systematically study impacts of anion charge and lattice volume on Li ion diffusion in lithium battery materials. Regardless of different Li occupation patterns, Li sublattice type, anion sublattice types and Li ion diffusion channels, there is a universal physical picture of anion charge dependent activation energy (Ea) for Li ion diffusion in lithium compounds, and that is the more negative anion charges increase Ea for Li ion diffusion and then decrease them. While large lattice volumes not always reduce Ea for Li ion migration, and even increase Ea, distinguished from the traditional understandings. These physical pictures of anion charge and lattice volume dependent Ea guide us to apply strains and choose appropriate elements for doping or substituting lithium compounds to reduce Ea for fast Li ion diffusion.

In the study of lattices structures, both experiments and numerical simulations are often conducted with small samples. Using combined ensemble and volume averaging, this work introduces a method to extract a macroscopic constitutive response of a lattice material from numerical simulations performed in periodic domains. The domain size needed to obtain statistically accurate results is investigated. Similar to molecular dynamics, the concept of the virial stress is introduced after homogenized equations are derived using the ensemble averaging method. Under static conditions, the virial stress is shown to agree with the volume averaged solid stress.Using the homogenization method, constitutive relations for this stress can be obtained from systems with uniform strains. Application of such obtained constitutive relations to more general cases results in an error proportional to the square of the ratio between the lattice length scale and the macroscopic length scale. Taking advantage of this property, numerical simulations are performed in systems with a uniform gradient of the average velocity. The volume average method is then used to accelerate convergence when studying lattices with defects. To avoid the artificial numerical time scale from the size of a representative volume element divided by the wave speed, a numerical scheme is developed to enforce a spatially uniform velocity gradient within the computational domain while allowing fluctuations of the velocity or displacement to develop naturally. To account for probability distribution of lattice defects, the stress is calculated as the ensemble-volume averaged value. For dynamic systems, energy dissipation properties are also studied.

Several graphene nanoribbon forms have been obtained from the fusion of different molecular precursors over the last decade. These include a rich set of examples in which heteroatoms substitute carbon at precisely defined sites. Considering this scenario, we employed first-principles calculations to study the electronic and transport properties of coronene-based nanoribbons with boron and nitrogen heteroatoms. We demonstrate that B/N substitution induces marked changes in the electronic properties of these structures in comparison to the full-carbon counterpart. For instance, the systems change from semiconductor to metallic due to levels introduced by the heteroatoms. In addition, non-trivial spin-polarized distributions emerge in selected cases, resulting in systems with a high potential for insertion in spintronic applications. This is further investigated in nanojunctions composed of N-substituted electrodes.

Molecular dynamics simulations are used to investigate the thermal stability of Al-Ni core–shell nanoparticles (NP) with different core sizes and shell thicknesses. Our study reveals that a distinct two-stage melting occurs during the continuous heating of bimetallic NPs. Unlike previous studies for single NP, where melting start from the outer surface and gradually encompass the core of the material, our result clearly indicates the interface dominated melting phenomena. It is evidenced in our analysis through microstructure, coordination number, and Lindemann index, that this interface dominated phenomena does not alter with decreased shell thickness. We estimate that the interfacial misfit, and bond energy orders (Ni-Al, Al-Al, and Al-Al) are the sources of such premelting phenomena to be nucleated at the interface. This study provides a fundamental perspective on the melting behavior of bimetallic nanoparticles and can be extended towards multimetallic NPs at the atomic level.