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JETP Letters

, Volume 95, Issue 11, pp 581–585 | Cite as

Consistent LDA’ + DMFT—an unambiguous way to avoid double counting problem: NiO test

  • I. A. Nekrasov
  • V. S. Pavlov
  • M. V. Sadovskii
Methods of Theoretical Physics

Abstract

We present a consistent way of treating a double counting problem unavoidably arising within the LDA + DMFT combined approach to realistic calculations of electronic structure of strongly correlated systems. The main obstacle here is the absence of systematic (e.g., diagrammatic) way to express LDA (local density approximation) contribution to exchange correlation energy appearing in the density functional theory. It is not clear then, which part of interaction entering DMFT (dynamical mean-field theory) is already taken into account through LDA calculations. Because of that, up to now there is no accepted unique expression for the double counting correction in LDA + DMFT. To avoid this problem we propose here the consistent LDA’ + DMFT approach, where LDA exchange correlation contribution is explicitly excluded for correlated states (bands) during self-consistent band structure calculations. What is left out of Coulomb interaction for those strongly correlated states (bands) is its non-local part, which is not included in DMFT, and the local Hartreelike contribution. Then the double counting correction is uniquely reduced to the local Hartree contribution. Correlations for strongly correlated states are then directly accounted for via the standard DMFT. We further test the consistent LDA’ + DMFT scheme and compare it with conventional LDA + DMFT calculating the electronic structure of NiO. Opposite to the conventional LDA + DMFT our consistent LDA’ + DMFT approach unambiguously produces the insulating band structure in agreement with experiments.

Keywords

JETP Letter Local Density Approximation Double Counting Band Structure Calculation Hubbard Band 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Pleiades Publishing, Ltd. 2012

Authors and Affiliations

  • I. A. Nekrasov
    • 1
  • V. S. Pavlov
    • 1
  • M. V. Sadovskii
    • 1
    • 2
  1. 1.Institute of ElectrophysicsUral Branch, Russian Academy of SciencesYekaterinburgRussia
  2. 2.Institute of Metal PhysicsUral Branch, Russian Academy of SciencesYekaterinburgRussia

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