Literature DB >> 16343021

Density functional theory study of the geometry, energetics, and reconstruction process of Si111 surfaces.

Santiago D Solares1, Siddharth Dasgupta, Peter A Schultz, Yong-Hoon Kim, Charles B Musgrave, William A Goddard.   

Abstract

We report the structures and energies from first principles density functional calculations of 12 different reconstructed (111) surfaces of silicon, including the 3x3 to 9x9 dimer-adatom-stacking fault (DAS) structures. These calculations used the Perdew-Burke-Ernzerhof generalized gradient approximation of density functional theory and Gaussian basis functions. We considered fully periodic slabs of various thicknesses. We find that the most stable surface is the DAS 7x7 structure, with a surface energy of 1.044 eV/1x1 cell (1310 dyn/cm). To analyze the origins of the stability of these systems and to predict energetics for more complex, less-ordered systems, we develop a model in which the surface energy is partitioned into contributions from seven different types of atom environments. This analysis is used to predict the surface energy of larger DAS structures (including their asymptotic behavior for very large unit cells) and to study the energetics of the sequential size change (SSC) model proposed by Shimada and Tochihara for the observed dynamical reconstruction of the Si(111) 1x1 structure. We obtain an energy barrier at the 2x2 cell size and confirm that the 7x7 regular stage of the SSC model (corresponding to the DAS 7x7 reconstruction) provides the highest energy reduction per unit cell with respect to the unreconstructed Si111 1x1 surface.

Entities:  

Year:  2005        PMID: 16343021     DOI: 10.1021/la052029s

Source DB:  PubMed          Journal:  Langmuir        ISSN: 0743-7463            Impact factor:   3.882


  2 in total

1.  Nanoscale effects in the characterization of viscoelastic materials with atomic force microscopy: coupling of a quasi-three-dimensional standard linear solid model with in-plane surface interactions.

Authors:  Santiago D Solares
Journal:  Beilstein J Nanotechnol       Date:  2016-04-15       Impact factor: 3.649

2.  Machine learning unifies the modeling of materials and molecules.

Authors:  Albert P Bartók; Sandip De; Carl Poelking; Noam Bernstein; James R Kermode; Gábor Csányi; Michele Ceriotti
Journal:  Sci Adv       Date:  2017-12-13       Impact factor: 14.136

  2 in total

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