The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.
Semiempirical quantum–chemical methods
Open accessAbstract
The semiempirical methods of quantum chemistry are reviewed, with emphasis on established neglect of diatomic differential overlap‐based methods (MNDO, AM 1, PM 3) and on the more recent orthogonalization‐corrected methods (OM 1, OM 2, OM 3). After a brief historical overview, the methodology is presented in nontechnical terms, covering the underlying concepts, parameterization strategies, and computational aspects, as well as linear scaling and hybrid approaches. The application section addresses selected recent benchmarks and surveys ground‐state and excited‐state studies, including recent OM 2‐based excited‐state dynamics investigations. This article is categorized under: Electronic Structure Theory > Semiempirical Electronic Structure Methods
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Cites
Grimme, S., Antony, J., Ehrlich, S., & Krieg, H. (2010) · The Journal of Chemical Physics · doi:10.1063/1.3382344 · Cited by 59,686
Semiempirical GGA‐type density functional constructed with a long‐range dispersion correction
Grimme, S. (2006) · Journal of Computational Chemistry · doi:10.1002/jcc.20495 · Cited by 31,038
Dewar, M. J. S., Zoebisch, E. G., Healy, E. F., & Stewart, J. J. P. (1985) · Journal of the American Chemical Society · doi:10.1021/ja00299a024 · Cited by 13,074
Optimization of parameters for semiempirical methods I. Method
Stewart, J. J. P. (1989) · Journal of Computational Chemistry · doi:10.1002/jcc.540100208 · Cited by 7,633
Ground states of molecules. 38. The MNDO method. Approximations and parameters
Dewar, M. J. S., & Thiel, W. (1977) · Journal of the American Chemical Society · doi:10.1021/ja00457a004 · Cited by 5,128
Self-citationAn Extended Hückel Theory. I. Hydrocarbons
Hoffmann, R. (1963) · The Journal of Chemical Physics · doi:10.1063/1.1734456 · Cited by 4,622
Warshel, A., & Levitt, M. (1976) · Journal of Molecular Biology · doi:10.1016/0022-2836(76)90311-9 · Cited by 4,479
Elstner, M., Porezag, D., Jungnickel, G., Elsner, J., Haugk, M., Frauenheim, T., Suhai, S., & Seifert, G. (1998) · Physical Review B · doi:10.1103/physrevb.58.7260 · Cited by 4,124
Molecular dynamics with electronic transitions
Tully, J. C. (1990) · The Journal of Chemical Physics · doi:10.1063/1.459170 · Cited by 4,000
Optimization of parameters for semiempirical methods II. Applications
Stewart, J. J. P. (1989) · Journal of Computational Chemistry · doi:10.1002/jcc.540100209 · Cited by 3,748
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Metadata
- Authors
- Thiel, W.
- Venue
- WIREs Computational Molecular Science
- Publisher
- Wiley
- Published
- 2013-07-19
- Volume
- 4
- Issue
- 2
- Pages
- 145-157
- ISSN
- 1759-0876 (ISSN-L)
- 1759-0884
- OpenAlex
- W2099805477
- Cited by
- 353
- References
- 88
- FWCI
- 8.91
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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