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• W3207300352 endingPage "110943" @default.
• W3207300352 startingPage "110943" @default.
• W3207300352 abstract "We propose here a method to generate random networked amorphous structure using only readily available short-range properties like bond lengths, bond angles and connectivity of the constituents. This method is a variant of Monte-Carlo (MC) method wherein the basic constituents of an amorphous network i.e. rigid polyhedral units are connected randomly obeying certain steric constraints. The algorithm is designed to reproduce the medium-range order universally observed in glasses. The method somewhat resembles the reverse MC (RMC) method where a random move of an atom inside a box is accepted or rejected depending upon whether it decreases or increases the deviation from the experimentally observed features. However unlike RMC, this method does not demand large experimental sets of scattering data which in most cases is a priori not available for glasses. It rather relies on the stochasticity of MC method to produce glassy structures. The algorithm is first validated against SiO2 glass structure by comparing with the available structures from other methods and experimental data. The method is then extended for developing more complex Iron Phosphate Glass (IPG) structures and a comparison with existing models of IPG developed using quench-from-melt scheme implemented in classical Molecular Dynamics (MD) reveals that the method is extensible to complex glasses also. This study addresses the often-neglected issue of non-availability of correct starting structures in simulating glasses using MD or Density Functional Theory (DFT)." @default.
• W3207300352 created "2021-10-25" @default.
• W3207300352 creator A5000273063 @default.
• W3207300352 creator A5023567541 @default.
• W3207300352 date "2022-02-01" @default.
• W3207300352 modified "2023-10-18" @default.
• W3207300352 title "Developing atomistic glass models using potential-free Monte Carlo method: From simple to complex structures" @default.
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• W3207300352 doi "https://doi.org/10.1016/j.commatsci.2021.110943" @default.
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