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Theory of electron affinities of small molecules
Abstract
In this paper, the equations-of-motion method, which has been successfully applied to the prediction of electronic excitation energies, is used to derive a physically clear and computationally tractable theory of molecular electron affinities. The contributions to the calculated electron affinities made by the ion-neutral correlation energy difference and by the Hartree-Fock energy change can be computed separately in this theory. In addition, the change in the correlation energy of the parent molecule's electrons, which is caused by adding an ``extra'' electron, can be quantitatively assessed. The physical content of this theory is discussed in considerable detail, and a connection is made with the many-body Green Function theory. The technique is shown to have important advantages over the variational wavefunction approach, the most attractive feature being the small size of the matrices occurring in computational applications.
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Citations
Cited by
Coupled-cluster theory in quantum chemistry
Bartlett, R. J., & Musiał, M. (2007) · Reviews of Modern Physics · doi:10.1103/revmodphys.79.291 · Cited by 3,343
Ortiz, J. V. (1996) · The Journal of Chemical Physics · doi:10.1063/1.471468 · Cited by 314
Metadata
- Authors
- Simons, J., & Smith, W. D.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 1973-06-01
- Volume
- 58
- Issue
- 11
- Pages
- 4899-4907
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W1998550138
- Cited by
- 287
- References
- 0
- FWCI
- 13.45
- Type
- journal article
- Language
- en
- Open access
- closed
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- Simons, J., & Smith, W. D. (1973). Theory of electron affinities of small molecules. The Journal of Chemical Physics. https://doi.org/10.1063/1.1679074
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.