Journal: Computational Materials Science

Abbreviation

Comput. Mater. Sci.

Publisher

Elsevier

Journal Volumes

ISSN

0927-0256
1879-0801

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Publications 1 - 10 of 19
  • Collective phenomena and states in traffic and self-driven many-particle systems
    Item type: Conference Paper
    Helbing, Dirk (2004)
    Computational Materials Science
  • Preface
    Item type: Other Journal Item
    Karakasidis, Theodoros; Kalliadasis, Serafim; Koumoutsakos, Petros; et al. (2019)
    Computational Materials Science
  • An effective constitutive model for polycrystalline ferroelectric ceramics: Theoretical framework and numerical examples
    Item type: Journal Article
    Tan, Wei Lin; Kochmann, Dennis M. (2017)
    Computational Materials Science
  • Stochastic 3D modeling of complex three-phase microstructures in SOFC-electrodes with completely connected phases
    Item type: Journal Article
    Neumann, Matthias; Staněk, Jakub; Pecho, Omar M.; et al. (2016)
    Computational Materials Science
  • An atomic-scale insight into the effects of hydrogen microalloying on the glass-forming ability and ductility of Zr-based bulk metallic glasses
    Item type: Journal Article
    Mahjoub, Reza; Laws, Kevin J.; Hamilton, Nicholas E.; et al. (2016)
    Computational Materials Science
  • Investigation of progressive failure in composites by combined simulated and experimental photoelasticity
    Item type: Journal Article
    Deuschle, H. Matthias; Wittel, Falk K.; Gerhard, Henry; et al. (2006)
    Computational Materials Science
  • Machine learning of twin/matrix interfaces from local stress field
    Item type: Journal Article
    Troncoso, Javier F.; Hu, Yang; della Ventura, Nicolò Maria; et al. (2023)
    Computational Materials Science
    Twinning is an important deformation mode in plastically deformed hexagonal close-packed materials. The extremely high twin growth rates at the nanoscale make atomistic simulations an attractive method for investigating the role of individual twin/matrix interfaces such as twin boundaries and basal-prismatic interfaces in twin growth kinetics. Unfortunately, there is no single framework that allows researchers to differentiate such interfaces automatically, neither in experimental in-situ transmission electron microscopy analysis images nor in atomistic simulations. Moreover, the presence of alloying elements introduces substantial noise to local atomic environments, making it nearly impossible to identify which atoms belong to which interface. Here, with the help of advanced machine learning methods, we provide a proof-of-concept way of using the local stress field distribution as an indicator for the presence of interfaces and for determining their types. We apply such an analysis to the growth of twin embryos in Mg-10 at.% Al alloys under constant stress and constant strain conditions, corresponding to two extremes of high and low strain rates, respectively. We discover that the kinetics of such growth is driven by high-energy basal-prismatic interfaces, in line with our experimental observations for pure Mg.
  • Designing two-dimensional metamaterials of controlled static and dynamic properties
    Item type: Journal Article
    Karathanasopoulos, Nikolaos; Reda, Hilal; Ganghoffer, Jean-francois (2017)
    Computational Materials Science
  • A meshless multiscale approach to modeling severe plastic deformation of metals: Application to ECAE of pure copper
    Item type: Journal Article
    Kumar, Siddhant; Tutcuoglu, Abbas D.; Hollenweger, Yannick; et al. (2020)
    Computational Materials Science
  • A fast atomistic approach to finite-temperature surface elasticity of crystalline solids
    Item type: Journal Article
    Saxena, Shashank; Spinola, Miguel; Gupta, Prateek; et al. (2022)
    Computational Materials Science
    Surface energies and surface elasticity largely affect the mechanical response of nanostructures as well as the physical phenomena associated with surfaces such as evaporation and adsorption. Studying surface energies at finite temperatures is therefore of immense interest for nanoscale applications. However, calculating surface energies and derived quantities from atomistic ensembles is usually limited to zero temperature or involves cumbersome thermodynamic integration techniques at finite temperature. Here, we illustrate a computational technique to identify the energy and elastic properties of surfaces of solids at non-zero temperature based on a Gaussian phase packets (GPP) approach (which in the isothermal limit coincides with a maximum-entropy formulation). Using this technique, we investigate the effect of temperature on the surface properties of different crystal faces for six pure metals – copper, nickel, aluminium, iron, tungsten and vanadium – thus covering both FCC and BCC lattice structures. While the obtained surface energies and stresses usually show a decreasing trend with increasing temperature, the elastic constants do not show such a consistent trend across the different materials and are quite sensitive to temperature changes. Validation is performed by comparing the obtained surface energy densities of selected BCC and FCC materials to those calculated via molecular dynamics.
Publications 1 - 10 of 19