Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,505

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Gaussian-1 theory: A general procedure for prediction of molecular energies

Pople, John A., Head-Gordon, Martin, Fox, Douglas J., Raghavachari, Krishnan, Curtiss, Larry A.·1989·The Journal of Chemical Physics

Abstract

A general procedure is developed for the computation of the total energies of molecules at their equilibrium geometries. Ab initio molecular orbital theory is used to calculate electronic energies by a composite method, utilizing large basis sets (including diffuse-sp, double-d and f-polarization functions) and treating electron correlation by Mo/ller–Plesset perturbation theory and by quadratic configuration interaction. The theory is also used to compute zero-point vibrational energy corrections. Total atomization energies for a set of 31 molecules are found to agree with experimental thermochemical data to an accuracy greater than 2 kcal mol−1 in most cases. Similar agreement is achieved for ionization energies, electron and proton affinities. Residual errors are assessed for the total energies of neutral atoms.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1063/1.456415

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 1 references

Cited by

See all 1,505

Metadata

Authors
Pople, J. A., Head-Gordon, M., Fox, D. J., Raghavachari, K., & Curtiss, L. A.
Venue
The Journal of Chemical Physics
Published
1989-05-15
Volume
90
Issue
10
Pages
5622-5629
ISSN
  • 0021-9606 (ISSN-L)
  • 1089-7690
OpenAlex
W2004333877
Cited by
1,505
References
1
FWCI
21.18
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Gaussian-1 theory: A general procedure for prediction of molecular energies. https://app.cochat.ai/papers/10.1063/1.456415
The paper
Pople, J. A., Head-Gordon, M., Fox, D. J., Raghavachari, K., & Curtiss, L. A. (1989). Gaussian-1 theory: A general procedure for prediction of molecular energies. The Journal of Chemical Physics. https://doi.org/10.1063/1.456415

Data sources

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.