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Approximate Self-Consistent Molecular Orbital Theory. I. Invariant Procedures

Pople, J. A., Santry, D. P., Segal, G. A.·1965·The Journal of Chemical Physics

Abstract

A general discussion of approximate methods for obtaining self-consistent molecular orbitals for all valence electrons of large molecules is presented. It is shown that the procedure of neglecting differential overlap in electron-interaction integrals (familiar in π-electron theory) without further adjustment may lead to results which are not invariant to simple transformations of the atomic orbital basis set such as rotation of axes or replacement of s, p orbitals by hybrids. The behavior of approximate methods in this context is examined in detail and two schemes are found which are invariant to transformations among atomic orbitals on a given atom. One of these (the simpler but more approximate) involves the complete neglect of differential overlap (CNDO) in all basis sets connected by such transformations. The other involves the neglect of diatomic differential overlap (NDDO) only, that is only products of orbitals on different atoms being neglected in the electron-repulsion integrals.

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Metadata

Authors
Pople, J. A., Santry, D. P., & Segal, G. A.
Venue
The Journal of Chemical Physics
Published
1965-11-15
Volume
43
Issue
10
Pages
S129-S135
ISSN
  • 0021-9606 (ISSN-L)
  • 1089-7690
OpenAlex
W1973899958
Cited by
1,721
References
0
FWCI
27.11
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Approximate Self-Consistent Molecular Orbital Theory. I. Invariant Procedures. https://app.cochat.ai/papers/10.1063/1.1701475
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Pople, J. A., Santry, D. P., & Segal, G. A. (1965). Approximate Self-Consistent Molecular Orbital Theory. I. Invariant Procedures. The Journal of Chemical Physics. https://doi.org/10.1063/1.1701475

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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.