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Density-Functional Methods for Excited States

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Attention for Chapter 630: Ensemble DFT Approach to Excited States of Strongly Correlated Molecular Systems
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Chapter title
Ensemble DFT Approach to Excited States of Strongly Correlated Molecular Systems
Chapter number 630
Book title
Density-Functional Methods for Excited States
Published in
Topics in current chemistry, January 2015
DOI 10.1007/128_2015_630
Pubmed ID
Book ISBNs
978-3-31-922080-2, 978-3-31-922081-9
Authors

Michael Filatov

Abstract

Ensemble density functional theory (DFT) is a novel time-independent formalism for obtaining excitation energies of many-body fermionic systems. A considerable advantage of ensemble DFT over the more common Kohn-Sham (KS) DFT and time-dependent DFT formalisms is that it enables one to account for strong non-dynamic electron correlation in the ground and excited states of molecular systems in a transparent and accurate fashion. Despite its positive aspects, ensemble DFT has not so far found its way into the repertoire of methods of modern computational chemistry, probably because of the perceived lack of practically affordable implementations of the theory. The spin-restricted ensemble-referenced KS (REKS) method is perhaps the first computationally feasible implementation of the ideas behind ensemble DFT which enables one to describe accurately electronic transitions in a wide class of molecular systems, including strongly correlated molecules (biradicals, molecules undergoing bond breaking/formation), extended π-conjugated systems, donor-acceptor charge transfer adducts, etc.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 29%
Professor 2 12%
Student > Postgraduate 2 12%
Student > Master 2 12%
Student > Doctoral Student 1 6%
Other 4 24%
Unknown 1 6%
Readers by discipline Count As %
Chemistry 12 71%
Physics and Astronomy 1 6%
Computer Science 1 6%
Social Sciences 1 6%
Engineering 1 6%
Other 0 0%
Unknown 1 6%