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Vertical Valence Ionization Potential Benchmarks from Equation-of-Motion Coupled Cluster Theory and QTP Functionals

Title data

Ranasinghe, Duminda S. ; Margraf, Johannes T. ; Perera, Ajith ; Bartlett, Rodney J.:
Vertical Valence Ionization Potential Benchmarks from Equation-of-Motion Coupled Cluster Theory and QTP Functionals.
In: The Journal of Chemical Physics. Vol. 150 (2019) Issue 7 . - 074108.
ISSN 0021-9606
DOI: https://doi.org/10.1063/1.5084728

Abstract in another language

The ionization potential (IP) of a molecule quantifies the energy required to remove an electron from the system. As such, it is a fundamental quantity in the context of redox chemistry, charge transfer, and molecular electronics. The accurate theoretical prediction of this property is therefore highly desirable for virtual materials design. Furthermore, vertical IPs are of interest in the development of many-body Green’s function methods like the GW formalism, as well as density functionals and semiempirical methods. In this contribution, we report over 1468 vertical valence IPs calculated with the IP variant of equation-of-motion coupled cluster theory with singles and doubles (IP-EOM-CCSD) covering 155 molecules. The purpose of this is two-fold: First, the quality of the predicted IPs is compared with respect to experiments and higher-order coupled cluster theory. This confirms the overall high accuracy and robustness of this method, with some outliers which are discussed in detail. Second, a large set of consistent theoretical reference values for vertical valence IPs are generated. This addresses a lack of reliable reference data for lower-lying valence IPs, where experimental data are often unavailable or of dubious quality. The benchmark set is then used to assess the quality of the eigenvalues predicted by different density functional approximations (via Bartlett’s IP-eigenvalue theorem) and the extended Koopmans’ theorem approach. The QTP family of functionals are found to be remarkably accurate, low-cost alternatives to IP-EOM-CCSD.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik - Univ.-Prof. Dr. Johannes Theo Margraf
Result of work at the UBT: No
DDC Subjects: 500 Science > 540 Chemistry
Date Deposited: 13 Nov 2023 12:29
Last Modified: 13 Nov 2023 12:29
URI: https://eref.uni-bayreuth.de/id/eprint/87669