arXiv · 1007.2271
Combining DFT and Many-Body Methods to Understand Correlated Materials
Abstract
The electronic and magnetic properties of many strongly-correlated systems are controlled by a limited number of states, located near the Fermi level and well isolated from the rest of the spectrum. This opens a formal way for combining the methods of first-principles electronic structure calculations, based on the density-functional theory (DFT), with many-body models, formulated in the restricted Hilbert space of states close to the Fermi level. The core of this project is the so-called "realistic modeling" or the construction of the model many-body Hamiltonians entirely from the first principles. Such a construction should be able to go beyond the conventional local-density approximation (LDA), which typically supplements the density-functional theory, and incorporate the physics of Coulomb correlations. It should also provide a transparent physical picture for the low-energy properties of strongly correlated materials. In this review article, we will outline the basic ideas of such a realistic modeling. The entire procedure will be illustrated on the series of examples, including the distorted transition-metal perovskite oxides, the compounds with the inversion symmetry breaking caused by the defects, and the alkali hyperoxide KO2, which can be regarded as an analog of strongly-correlated systems where the localized electrons reside on the molecular orbitals of the O2- dimer. In order to illustrate abilities of the realistic modeling, we will also consider solutions of the obtained low-energy models for a number of systems, and argue that it can be used as a powerful tool for the exploration and understanding of properties of strongly correlated materials.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
I. V. Solovyev. 2010-07-14. Combining DFT and Many-Body Methods to Understand Correlated Materials. https://doi.org/10.1088/0953-8984%2F20%2F29%2F293201
Cite the original work for its findings. Save a collection to share your selection of sources.