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Cited by 10,536

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

An all-electron numerical method for solving the local density functional for polyatomic molecules

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Delley, B.·1990·The Journal of Chemical Physics

Abstract

A method for accurate and efficient local density functional calculations (LDF) on molecules is described and presented with results. The method, Dmol for short, uses fast convergent three-dimensional numerical integrations to calculate the matrix elements occurring in the Ritz variation method. The flexibility of the integration technique opens the way to use the most efficient variational basis sets. A practical choice of numerical basis sets is shown with a built-in capability to reach the LDF dissociation limit exactly. Dmol includes also an efficient, exact approach for calculating the electrostatic potential. Results on small molecules illustrate present accuracy and error properties of the method. Computational effort for this method grows to leading order with the cube of the molecule size. Except for the solution of an algebraic eigenvalue problem the method can be refined to quadratic growth for large molecules.

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Metadata

Authors
Delley, B.
Venue
The Journal of Chemical Physics
Published
1990-01-01
Volume
92
Issue
1
Pages
508-517
ISSN
  • 0021-9606 (ISSN-L)
  • 1089-7690
OpenAlex
W1965752441
Cited by
10,536
References
1
FWCI
21.44
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: An all-electron numerical method for solving the local density functional for polyatomic molecules. https://app.cochat.ai/papers/10.1063/1.458452
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Delley, B. (1990). An all-electron numerical method for solving the local density functional for polyatomic molecules. The Journal of Chemical Physics. https://doi.org/10.1063/1.458452

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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.