Cubic scaling algorithms for RPA correlation using interpolative separable density fitting
Publication
, Journal Article
Lu, J; Thicke, K
Published in: Journal of Computational Physics
December 15, 2017
We present a new cubic scaling algorithm for the calculation of the RPA correlation energy. Our scheme splits up the dependence between the occupied and virtual orbitals in χ0 by use of Cauchy's integral formula. This introduces an additional integral to be carried out, for which we provide a geometrically convergent quadrature rule. Our scheme also uses the newly developed Interpolative Separable Density Fitting algorithm to further reduce the computational cost in a way analogous to that of the Resolution of Identity method.
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Published In
Journal of Computational Physics
DOI
EISSN
1090-2716
ISSN
0021-9991
Publication Date
December 15, 2017
Volume
351
Start / End Page
187 / 202
Related Subject Headings
- Applied Mathematics
- 51 Physical sciences
- 49 Mathematical sciences
- 40 Engineering
- 09 Engineering
- 02 Physical Sciences
- 01 Mathematical Sciences
Citation
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Chicago
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MLA
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Lu, J., & Thicke, K. (2017). Cubic scaling algorithms for RPA correlation using interpolative separable density fitting. Journal of Computational Physics, 351, 187–202. https://doi.org/10.1016/j.jcp.2017.09.012
Lu, J., and K. Thicke. “Cubic scaling algorithms for RPA correlation using interpolative separable density fitting.” Journal of Computational Physics 351 (December 15, 2017): 187–202. https://doi.org/10.1016/j.jcp.2017.09.012.
Lu J, Thicke K. Cubic scaling algorithms for RPA correlation using interpolative separable density fitting. Journal of Computational Physics. 2017 Dec 15;351:187–202.
Lu, J., and K. Thicke. “Cubic scaling algorithms for RPA correlation using interpolative separable density fitting.” Journal of Computational Physics, vol. 351, Dec. 2017, pp. 187–202. Scopus, doi:10.1016/j.jcp.2017.09.012.
Lu J, Thicke K. Cubic scaling algorithms for RPA correlation using interpolative separable density fitting. Journal of Computational Physics. 2017 Dec 15;351:187–202.
Published In
Journal of Computational Physics
DOI
EISSN
1090-2716
ISSN
0021-9991
Publication Date
December 15, 2017
Volume
351
Start / End Page
187 / 202
Related Subject Headings
- Applied Mathematics
- 51 Physical sciences
- 49 Mathematical sciences
- 40 Engineering
- 09 Engineering
- 02 Physical Sciences
- 01 Mathematical Sciences