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  Systematic High-Accuracy Prediction of Electron Affinities for Biological Quinones

Schulz, C. E., Dutta, A. K., Izsák, R., & Pantazis, D. A. (2018). Systematic High-Accuracy Prediction of Electron Affinities for Biological Quinones. Journal of Computational Chemistry, 39(29), 2439-2451. doi:10.1002/jcc.25570.

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 Creators:
Schulz, Christine E.1, 2, 3, Author              
Dutta, Achintya Kumar2, 4, Author
Izsák, Róbert2, 5, Author              
Pantazis, Dimitrios A.2, 3, Author              
Affiliations:
1Fakultät für Chemie und Biochemie, Ruhr‐Universität Bochum, Bochum, Germany, ou_persistent22              
2Max‐Planck‐Institut für Chemische Energiekonversion, Stiftstr. 34‐36, Mülheim an der Ruhr, Germany, ou_persistent22              
3Research Group Pantazis, Max-Planck-Institut für Kohlenforschung, Max Planck Society, ou_2541711              
4Department of Chemistry, Indian Institute of Technology Bombay, Mumbai 400076, India, ou_persistent22              
5Research Group Izsák, Max-Planck-Institut für Kohlenforschung, Max Planck Society, ou_2541707              

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Free keywords: quinones; electron affinities; coupled cluster; DLPNO methods; bacterial reaction centers
 Abstract: Quinones play vital roles as electron carriers in fundamental biological processes; therefore, the ability to accurately predict their electron affinities is crucial for understanding their properties and function. The increasing availability of cost‐effective implementations of correlated wave function methods for both closed‐shell and open‐shell systems offers an alternative to density functional theory approaches that have traditionally dominated the field despite their shortcomings. Here, we define a benchmark set of quinones with experimentally available electron affinities and evaluate a range of electronic structure methods, setting a target accuracy of 0.1 eV. Among wave function methods, we test various implementations of coupled cluster (CC) theory, including local pair natural orbital (LPNO) approaches to canonical and parameterized CCSD, the domain‐based DLPNO approximation, and the equations‐of‐motion approach for electron affinities, EA‐EOM‐CCSD. In addition, several variants of canonical, spin‐component‐scaled, orbital‐optimized, and explicitly correlated (F12) Møller–Plesset perturbation theory are benchmarked. Achieving systematically the target level of accuracy is challenging and a composite scheme that combines canonical CCSD(T) with large basis set LPNO‐based extrapolation of correlation energy proves to be the most accurate approach. Methods that offer comparable performance are the parameterized LPNO‐pCCSD, the DLPNO‐CCSD(T0), and the orbital optimized OO‐SCS‐MP2. Among DFT methods, viable practical alternatives are only the M06 and the double hybrids, but the latter should be employed with caution because of significant basis set sensitivity. A highly accurate yet cost‐effective DLPNO‐based coupled cluster approach is used to investigate the methoxy conformation effect on the electron affinities of ubiquinones found in photosynthetic bacterial reaction centers. © 2018 Wiley Periodicals, Inc.

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Language(s): eng - English
 Dates: 2018-04-212018-08-072018-11-05
 Publication Status: Published online
 Pages: 13
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1002/jcc.25570
 Degree: -

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Title: Journal of Computational Chemistry
  Abbreviation : J. Comput. Chem.
Source Genre: Journal
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Publ. Info: New York : Wiley
Pages: - Volume / Issue: 39 (29) Sequence Number: - Start / End Page: 2439 - 2451 Identifier: ISSN: 0192-8651
CoNE: https://pure.mpg.de/cone/journals/resource/954925489848