首页期刊导航|Computational Materials Science
期刊信息/Journal information
Computational Materials Science
Elsevier Science Publishers B.V.
Computational Materials Science

Elsevier Science Publishers B.V.

0927-0256

Computational Materials Science/Journal Computational Materials ScienceISTPSCIEI
正式出版
收录年代

    Fracture and strength of single-atom-thick hexagonal materials

    Le, Minh-Quy
    17页
    查看更多>>摘要：The fracture toughness has been theoretically and experimentally estimated for few of various two-dimensional (2D) materials. Relationship between the fracture toughness and other mechanical properties of 2D hexagonal materials is extremely needed for a quick estimation in engineering applications, as well as for a better comprehension of their fracture properties. The present work investigates through molecular dynamics simu-lations at room temperature the mode-I fracture of 25 single-atom-thick hexagonal materials with the buckling height-bond length ratio in the range from 0 through-0.6, including 6 planar sheets (graphene, boronitrene, SiC, GeC, AlN and InN) and 19 buckled sheets (silicene, InP, SiGe, SnSi, SnGe, SnO, GeO, SiO, blue-P, arsenene, GeS, SnS, antimonene, bismuthene, SiTe, SnSe, SiSe, GeTe and SnTe). Fracture mechanism is considered. With this large data set and based on dimensional analysis, an empirical formula is established to estimate the mode-I fracture toughness from the elastic modulus, intrinsic tensile strength, bond length and buckling height of a single-atom-thick hexagonal material with reasonable accuracy. This simple formula provides quick estimation of the fracture toughness and is useful for engineering applications.
      原文链接:
    • NSTL
    • Elsevier

    Supervised deep learning prediction of the formation enthalpy of complex phases using a DFT database: The sigma-phase as an example

    Crivello, Jean-ClaudeJoubert, Jean-MarcSokolovska, Nataliya
    7页
    查看更多>>摘要：Machine learning (ML) methods are becoming the state-of-the-art in numerous domains, including material sciences. In this manuscript, we demonstrate how ML can be used to efficiently predict several properties in solid-state chemistry applications, in particular, to estimate the heat of formation of a given complex crystallographic phase (here, the sigma-phase, tP30, D8(b)). Based on an independent and unprecedented large first principles dataset containing about 10,000 sigma-compounds with n = 14 different elements, we used a supervised learning approach to predict all the similar to 500,000 possible configurations. From a random set of similar to 1000 samples, predictions are given within a mean absolute error of 23 meV at(-1) (similar to 2 kJ mol(-1)) on the heat of formation and similar to 0.06 angstrom on the tetragonal cell parameters. We show that deep neural network regression results in a significant improvement in the accuracy of the predicted output compared to traditional regression techniques. We also integrated descriptors having physical nature (atomic radius, number of valence electrons), and we observe that they improve the model precision. We conclude from our numerical experiments that the learning database composed of the binary-compositions only, plays a major role in predicting the higher degree system configurations. Our results open a broad avenue to efficient high-throughput investigations of the combinatorial binary computations for multicomponent complex intermetallic phase prediction.
      原文链接:
    • NSTL
    • Elsevier

    Intrinsic disorder of dangling OH-bonds in the first water layer on noble metal surfaces

    Zhao, DiLiu, FengDuan, XiangmeiSun, Deyan...
    6页
    查看更多>>摘要：Using the first-principles calculation, we have systematically investigated the orientation of dangling OH-bonds in the first adsorbed water layer on close packed surface of noble metals (gold, platinum and palladium). We find that, the distribution of up and down dangling OH-bonds can strongly change the adsorption stability. A specific distribution of dangling OH-bonds is always indispensable in various adsorption patterns for its stability. The inplane arrangement of water molecules also has important influence on the orientation of dangling OH-bonds. More importantly, the disorder in the orientation (up or down) of dangling OH manifests a kind of zerotemperature residual entropy in some absorption structures. In essence, the residual entropy arises from the competition between water-water and water-metal interactions, which is different from the counterpart in ice crystals.
      原文链接:
    • NSTL
    • Elsevier

    The behaviour of Boron Carbide under shock compression conditions: MD simulation results

    Cekil, Huseyin C.Ozdemir, Metin
    14页
    查看更多>>摘要：Boron Carbide (B4C) is investigated by molecular dynamics simulations to examine its mechanical behaviour on the dynamics loading. Atomistic shock compression simulations are carried out in [0001] and [10 (1) over bar0] impact directions for that purpose. Interaction between atoms is defined with reactive force field (reaxFF). Hugoniot curves are obtained and Hugoniot elastic limits (HEL) are determined for both directions. HEL point occurs at about 17 and 24 GPa in [0001] and [10 (1) over bar0] directions, respectively. Resistance of material to structural deformations is higher for [1010] direction compared to [0001]. Three wave fronts develop in shock wave profile in the material. The shock velocity U-s- particle velocity Up relations in plastic region has bilinear nature. Amorphous state is observed above impact speeds 2.0 and 3.0 km/s, [0001] and [10 (1) over bar0] impact directions, respectively. Buckling of C-B-C chains and lattice rotation occur before amorphization, the degree of both of which depends on impact direction and these are considered the causes of deformation. The changes in the structural order of B4C is investigated using radial distribution function (RDF) analysis. Our numerical results compare favourably with available experimental results.
      原文链接:
    • NSTL
    • Elsevier

    Reactive molecular dynamics simulations of nickel-based heterometallic catalysts for hydrogen evolution in an alkaline KOH solution

    Oyinbo, Sunday TemitopeJen, Tien-Chien
    10页
    查看更多>>摘要：In recent years, significant attention has been paid to the possible use of hydrogen (H2) as a renewable energy source. The formation of H2, in particular, is appealing by Ni-based-catalyzed activity in an alkaline potassium hydroxide (KOH) solution at room temperatures. However, a broader understanding of the reactions at the catalyst surface needs to be sought at the atomic level. In this research, a comparative and systematic study of nickel-based heterometallic catalysts in an alkaline potassium hydroxide (KOH) solution for H2 generation with the aid of ReaxFF potential is performed using reactive molecular dynamics (RMD) simulations. The interface composition of nickel catalyst was systematically modulated to include transition metals such as iron, platinum, and their oxides. All nickel-based transition metals and their oxides are equally active while influencing the catalytic reaction. Ni-Fe and Ni-Pt impose major promoting effects on the Ni-based catalyst, with an improvement in the generation rate of output of H2 when compared to the Ni-Fe-Pt heterometallic catalyst. On the other hand, only a marginal improvement in the catalytic effectiveness of the Ni-based catalyst is evident with Ni-Fe-O and Ni-Pt-O catalyst. The molecular proportion of the metal/(Ni + oxide) was varied within the catalyst to investigate the impact of metal combination and concentration on the alloy catalytic performance. This structural variation also explained the role of each second metal and their oxide involved in an alkaline KOH electron exchange process. The second metal promoting effects are mainly summarized in terms of the ability to serve as an electrophilic site for enhanced OH- group absorption, a large area of active surface, amorphous characteristic of the alloy catalyst, and interaction of the electrons with the Ni active metal.
      原文链接:
    • NSTL
    • Elsevier

    Leaching model of radionuclides in metal-organic framework particles

    Li, YulanHu, ShenyangHilty, Floyd W.Montgomery, Robert...
    10页
    查看更多>>摘要：Metal-organic frameworks (MOFs) have been used to sequester radionuclides and seal them inside of porous scaffolds using postsynthetic modification procedures. Experiments show that certain Zr-MOF with different capping linkers significantly affects the radionuclide release kinetics. In this work, we developed a leaching model of radionuclides in Zr-MOF particles. The model assumes that uranyl species occupy two energetically favored sites: the metal node and the MOF pores. For a given overall concentration of uranyl species, the partitions of uranyl species at the metal nodes and within the pores are determined by their chemical potentials. The model also considers the effect of particle surface and concentration on chemical potentials and diffusivity. The effects of spatial and structural dependent chemical potentials and diffusivity as well as particle sizes on leaching kinetics are investigated with the model. Predicted and measured uranyl leaching kinetics in Zr-MOF particles under batch experiments are compared. The results demonstrate the model's capability for exploring the mechanisms of leaching and provide guidance for material design.
      原文链接:
    • NSTL
    • Elsevier

    A Buckingham interatomic potential for thallium oxide (Tl2O): Application to the case of thallium tellurite glasses

    RaghvenderBouzid, AssilHamani, DavidThomas, Philippe...
    7页
    查看更多>>摘要：An interatomic potential for simulating the structural properties of thallium (I) oxide (with oxidation state +1) based compounds has been developed by fitting to the experimental crystalline structures of alpha-Tl2Te2O5, Tl2Te3O7 and Tl2TeO3 simultaneously. The obtained potentials are subsequently verified and validated by optimizing additional Tl(I)-O based compounds, leading to a good agreement of the lattice constants with experimental data. Amorphous (TlO0.5)(x) - (TeO2)(1-x) systems where then produced by classical molecular dynamics and their structural properties compared to experimental measurements.
      原文链接:
    • NSTL
    • Elsevier

    Shock-induced spallation in single-crystalline tantalum at elevated temperatures through molecular dynamics modeling

    Wang, YuntianZeng, XiangguoYang, XinXu, Taolong...
    20页
    查看更多>>摘要：The effects of initial temperature on shock-induced spalling behavior and damage evolution of single-crystal Tantalum were investigated using molecular dynamics simulation. The wave profiles show that shock pressure and temperature increment rise as initial temperature increases, which can be explained utilizing the RankineHugoniot relationship. It is found that strain rate and the initial temperature has a substantial effect on the spall strength. The spall strength will decrease with initial temperature increases, and the competition between the strain rate hardening and temperature softening effect on spall strength is discussed. The radial distribution function analysis reveal that the classical spallation occurs under shock velocity 1.5 km/s and micro-spalling state with material partially melted or melted happened in higher shock velocity. The simulations show that shock-induced spalling of Tantalum is characterized by void nucleation, growth, and coalescence. The void evolution characteristics in classical spallation and micro-spalling are discussed. Furthermore, it is found that the initial temperature has a dramatic effect on the void evolution. The total voids number increases as the initial temperature rises. The characteristics of the free surface velocity profile are also disscussed.
      原文链接:
    • NSTL
    • Elsevier

    Low lattice thermal conductivity and its role in the remarkable thermoelectric performance of newly predicted SiS2 and SiSe2 monolayers

    Bera, JayantaBetal, AtanuSingh, ZimmiGandi, Appala Naidu...
    9页
    查看更多>>摘要：For high-efficiency thermoelectric power conversion, not only improvement of existing materials properties but also prediction and synthesis of new thermoelectric materials are needed. Here, we have carried out a systematic investigation on the thermoelectric performance of newly predicted two dimensional (2D) semiconducting SiS2 and SiSe2 monolayers using density functional theory (DFT) and solving the Boltzmann transport equations (BTEs) for electrons and phonons. Our computed value of lattice thermal conductivity (k(ph)) in SiSe2 monolayer is ultralow, which results in a high thermoelectric figure of merit (zT) value of 0.86 (0.83) for p-type (n-type) at 900 K in SiSe2 monolayer. While in SiS2 monolayer, zT value are 0.77 (p-type) and 0.71 (n-type) at 900 K. The values of k(ph) are attributed to low group velocity, strong anharmonicity and phonon-phonon coupling of acoustic and low-frequency optical branches, leading to larger scattering, smaller mean free path, and shorter lifetime of phonons. It is also found that p-type doping is more effective than n-type doping to get optimal power factor (PF) and zT. Our findings suggest that newly predicted semiconducting SiSe2 and SiS2 monolayers can be very promising thermoelectric materials for the fabrication of high-efficiency thermoelectric power generators to convert waste heat into electricity.
      原文链接:
    • NSTL
    • Elsevier

    Accurate prediction of band gap of materials using stacking machine learning model

    Wang, TengZhang, KefeiThe, JesseYu, Hesheng...
    11页
    查看更多>>摘要：The prediction of the band gap of semiconductor materials using machine learning has gradually progressed in recent years. However, the performance of such prediction still needs further optimization. This work applies the stacking approach, which fuses the output of multiple baseline models, to further enhance the performance of band gap regression. Ten baseline models are optimized to predict the band gap of materials. Afterwards, the output of models with relatively better performance is used as the input features of the stacking approach. This research employed a benchmark dataset containing 3896 inorganic compounds, with 136 dimensions, and a newly established complex database (E-AFLOW), containing 21,534 compounds with 206 dimensions, to prove the effectiveness of different models. The trained stacking model based on the E-AFLOW database is then applied to determine the band gaps of different new compounds. The results demonstrate that the stacking model has the highest R-2 value, at 0.920, in benchmark dataset and a value of 0.917 in the E-AFLOW dataset, with 5-flod cross validation. For the E-AFLOW dataset, the improvement percentage of RMSE, MAE, MAPE, and R-2 of the stacking model to GBDT, XGB, RF, and LGB input baseline models are between 3.06%-17.54%, 8.12%-33.25%, 7.69%-33.33%, and 0.66%-4.44%, respectively. In real applications, the trained stacking model based on the E-AFLOW dataset can predict the band gaps of 78.57% of new materials within +/- 8.00% of observed measurements. The minimum deviation between the predicted and observed values is -0.02%, and the maximum is 14.27%. These results convincingly demonstrate the excellent performance of stacking approach in band gap regression.
      原文链接:
    • NSTL
    • Elsevier