Your browser doesn't support javascript.
loading
Achieving Predictive Description of Molecular Conductance by Using a Range-Separated Hybrid Functional.
Yamada, Atsushi; Feng, Qingguo; Hoskins, Austin; Fenk, Kevin D; Dunietz, Barry D.
Afiliação
  • Yamada A; Department of Chemistry and Biochemistry, Kent State University , Kent, Ohio 44242, United States.
  • Feng Q; Department of Chemistry and Biochemistry, Kent State University , Kent, Ohio 44242, United States.
  • Hoskins A; Department of Chemistry and Biochemistry, Kent State University , Kent, Ohio 44242, United States.
  • Fenk KD; Department of Chemistry and Biochemistry, Kent State University , Kent, Ohio 44242, United States.
  • Dunietz BD; Department of Chemistry and Biochemistry, Kent State University , Kent, Ohio 44242, United States.
Nano Lett ; 16(10): 6092-6098, 2016 10 12.
Article em En | MEDLINE | ID: mdl-27636328
ABSTRACT
The conductance of molecular bridges tends to be overestimated by computational studies in comparison to measured values. While this well-established trend may be related to difficulties for achieving robust bridges, the employed computational scheme can also contribute to this tendency. In particular, caveats of the traditional functionals employed in first-principles-based calculations can lead to discrepancies reflected in exaggerated conductance. Here, we show that by employing a range-separated hybrid functional the calculated values are within the same order as the measured conductance for all four considered cases. On the other hand, with B3LYP, which is a widely used functional, the calculated values greatly overestimate the conductance (by about 1-2 orders of magnitude). The improved description of the conductance with a RSH functional builds on achieving a physically meaningful treatment of the quasi particles associated with the frontier orbitals.
Palavras-chave
Buscar no Google
Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2016 Tipo de documento: Article
Buscar no Google
Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2016 Tipo de documento: Article