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Dipole moments of molecules with multi-reference character from optimally tuned range-separated density functional theory.

Mojtaba Alipour
Published in: Journal of computational chemistry (2018)
Dipole moment is the first nonzero moment of the charge density of neutral systems. If a density functional theory (DFT) method is able to yield accurate dipole moments, it should first provide an accurate geometry and then predict a reliable charge distribution for that geometry. In this respect, recent literatures have revealed that most DFT approximations work considerably better for single-reference molecules with respect to multi-reference ones, as may be expected from this fact that DFT utilizes a single configuration state function as reference function to represent the density. Putting together, it seems that as compared to the single-reference systems, situation is slightly more involved in the case of dipole moment calculations of multi-reference molecules. Effort to address this latter issue constitutes the cornerstone of the present investigation. To this end, we rely on a different approach where the new optimally (nonempirically) tuned range-separated hybrid density functionals (OT-RSHs) without invoking any empirical fitting are proposed for predicting the dipole moments of multi-reference molecules containing both main-group elements and transition metals. We have scanned the controlling factors of OT-RSHs like short- and long-range exchange contributions and range-separation parameter with the aim of deriving the best performing models for the purpose. The obtained results unveil that, as compared to the standard range-separated density functionals, our newly developed OT-RSHs not only give an improved description on the dipole moments of the molecules with multi-reference character but also the quality of their predictions is better than other conventional and recently proposed DFT approximations. © 2018 Wiley Periodicals, Inc.
Keyphrases
  • density functional theory
  • molecular dynamics
  • high resolution
  • risk assessment
  • climate change