Published November 2020 | Version public
Journal Article

Hybrid functionals with system‐dependent parameters: Conceptual foundations and methodological developments

  • 1. ROR icon Peking University
  • 2. ROR icon Beijing National Laboratory for Molecular Sciences
  • 3. ROR icon California Institute of Technology

Abstract

Approximate density‐functional theory (DFT) has become the major workhorse of modern computational chemistry and materials science, but the most widely used DFT approaches, local‐density approximation (LDA) and generalized gradient approximation (GGA), suffer from some fundamental deficiencies, including, in particular, the band gap problem. As a relatively cheap way to overcome the difficulty confronted by LDA/GGA, hybrid functional methods have attracted tremendous interest, first in molecular quantum chemistry, and more recently also in computational materials science. While early hybrid functionals use fixed parameters that are determined either by fitting some standard experimental database or based on theoretical arguments, recent studies have clearly indicated that the hybridization parameters carry on the physical significance and therefore should be system‐dependent. Developing theoretical methods to evaluate those parameters in a first‐principles manner has become one of the most active frontiers in theoretical chemistry community, and various schemes have been proposed. In this article, we aim at giving a systematic overview on the main theoretical concepts underlying various strategies and review major methodological developments in the recent years.

Additional Information

© 2020 Wiley Periodicals LLC. Version of Record online: 16 April 2020; Manuscript accepted: 16 March 2020; Manuscript revised: 13 March 2020; Manuscript received: 15 January 2020. Funding information: National Key Research and Development Program of China, Grant/Award Number: 2016YFB0701100; National Natural Science Foundation of China, Grant/Award Numbers: 21673005, 21621061.

Additional details

Identifiers

Eprint ID
102682
Resolver ID
CaltechAUTHORS:20200421-080205471

Funding

National Key Research and Development Program of China
2016YFB0701100
National Natural Science Foundation of China
21673005
National Natural Science Foundation of China
21621061

Dates

Created
2020-04-21
Created from EPrint's datestamp field
Updated
2021-11-16
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