Improving Rydberg Excitations within Time-Dependent Density Functional Theory with Generalized Gradient Approximations: The Exchange-Enhancement-for-Large-Gradient Scheme
Time-dependent density functional theory (TDDFT) with conventional local and hybrid functionals such as the local and hybrid generalized gradient approximations (GGA) seriously underestimates the excitation energies of Rydberg states, which limits its usefulness for applications such as spectroscopy and photochemistry. We present here a scheme that modifies the exchange-enhancement factor to improve GGA functionals for Rydberg excitations within the TDDFT framework while retaining their accuracy for valence excitations and for the thermochemical energetics calculated by ground-state density functional theory. The scheme is applied to a popular hybrid GGA functional and tested on data sets of valence and Rydberg excitations and atomization energies, and the results are encouraging. The scheme is simple and flexible. It can be used to correct existing functionals, and it can also be used as a strategy for the development of new functionals.
- Authors:
-
[1]
; [1]
- Univ. of Minnesota, Minneapolis, MN (United States). Dept. of Chemistry. Chemical Theory Center. Supercomputing Inst.
- Publication Date:
- Grant/Contract Number:
- SC0008666
- Type:
- Published Article
- Journal Name:
- Journal of Chemical Theory and Computation
- Additional Journal Information:
- Journal Volume: 11; Journal Issue: 7; Journal ID: ISSN 1549-9618
- Publisher:
- American Chemical Society
- Research Org:
- Univ. of Minnesota, Minneapolis, MN (United States)
- Sponsoring Org:
- USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
- OSTI Identifier:
- 1252036
- Alternate Identifier(s):
- OSTI ID: 1455126
Li, Shaohong L., and Truhlar, Donald G.. Improving Rydberg Excitations within Time-Dependent Density Functional Theory with Generalized Gradient Approximations: The Exchange-Enhancement-for-Large-Gradient Scheme. United States: N. p.,
Web. doi:10.1021/acs.jctc.5b00369.
Li, Shaohong L., & Truhlar, Donald G.. Improving Rydberg Excitations within Time-Dependent Density Functional Theory with Generalized Gradient Approximations: The Exchange-Enhancement-for-Large-Gradient Scheme. United States. doi:10.1021/acs.jctc.5b00369.
Li, Shaohong L., and Truhlar, Donald G.. 2015.
"Improving Rydberg Excitations within Time-Dependent Density Functional Theory with Generalized Gradient Approximations: The Exchange-Enhancement-for-Large-Gradient Scheme". United States.
doi:10.1021/acs.jctc.5b00369.
@article{osti_1252036,
title = {Improving Rydberg Excitations within Time-Dependent Density Functional Theory with Generalized Gradient Approximations: The Exchange-Enhancement-for-Large-Gradient Scheme},
author = {Li, Shaohong L. and Truhlar, Donald G.},
abstractNote = {Time-dependent density functional theory (TDDFT) with conventional local and hybrid functionals such as the local and hybrid generalized gradient approximations (GGA) seriously underestimates the excitation energies of Rydberg states, which limits its usefulness for applications such as spectroscopy and photochemistry. We present here a scheme that modifies the exchange-enhancement factor to improve GGA functionals for Rydberg excitations within the TDDFT framework while retaining their accuracy for valence excitations and for the thermochemical energetics calculated by ground-state density functional theory. The scheme is applied to a popular hybrid GGA functional and tested on data sets of valence and Rydberg excitations and atomization energies, and the results are encouraging. The scheme is simple and flexible. It can be used to correct existing functionals, and it can also be used as a strategy for the development of new functionals.},
doi = {10.1021/acs.jctc.5b00369},
journal = {Journal of Chemical Theory and Computation},
number = 7,
volume = 11,
place = {United States},
year = {2015},
month = {5}
}