The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Sparse maps—A systematic infrastructure for reduced-scaling electronic structure methods. II. Linear scaling domain based pair natural orbital coupled cluster theory
Abstract
Domain based local pair natural orbital coupled cluster theory with single-, double-, and perturbative triple excitations (DLPNO-CCSD(T)) is a highly efficient local correlation method. It is known to be accurate and robust and can be used in a black box fashion in order to obtain coupled cluster quality total energies for large molecules with several hundred atoms. While previous implementations showed near linear scaling up to a few hundred atoms, several nonlinear scaling steps limited the applicability of the method for very large systems. In this work, these limitations are overcome and a linear scaling DLPNO-CCSD(T) method for closed shell systems is reported. The new implementation is based on the concept of sparse maps that was introduced in Part I of this series [P. Pinski, C. Riplinger, E. F. Valeev, and F. Neese, J. Chem. Phys. 143, 034108 (2015)]. Using the sparse map infrastructure, all essential computational steps (integral transformation and storage, initial guess, pair natural orbital construction, amplitude iterations, triples correction) are achieved in a linear scaling fashion. In addition, a number of additional algorithmic improvements are reported that lead to significant speedups of the method. The new, linear-scaling DLPNO-CCSD(T) implementation typically is 7 times faster than the previous implementation and consumes 4 times less disk space for large three-dimensional systems. For linear systems, the performance gains and memory savings are substantially larger. Calculations with more than 20 000 basis functions and 1000 atoms are reported in this work. In all cases, the time required for the coupled cluster step is comparable to or lower than for the preceding Hartree-Fock calculation, even if this is carried out with the efficient resolution-of-the-identity and chain-of-spheres approximations. The new implementation even reduces the error in absolute correlation energies by about a factor of two, compared to the already accurate previous implementation.
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.
Integrity signals
Registry
Cites
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.
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
Coupled-cluster theory in quantum chemistry
Bartlett, R. J., & Musiał, M. (2007) · Reviews of Modern Physics · doi:10.1103/revmodphys.79.291 · Cited by 3,343
Cited by
Software update: The ORCA program system—Version 5.0
Neese, F. (2022) · WIREs Computational Molecular Science · doi:10.1002/wcms.1606 · Cited by 4,963
Self-citationGoerigk, L., Hansen, A., Bauer, C., Ehrlich, S., Najibi, A., & Grimme, S. (2017) · Physical Chemistry Chemical Physics · doi:10.1039/c7cp04913g · Cited by 1,894
Best‐Practice DFT Protocols for Basic Molecular Computational Chemistry**
Bursch, M., Mewes, J., Hansen, A., & Grimme, S. (2022) · Angewandte Chemie International Edition · doi:10.1002/anie.202205735 · Cited by 890
B97-3c: A revised low-cost variant of the B97-D density functional method
Brandenburg, J. G., Bannwarth, C., Hansen, A., & Grimme, S. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5012601 · Cited by 760
Saitow, M., Becker, U., Riplinger, C., Valeev, E. F., & Neese, F. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4981521 · Cited by 423
Self-citation
Metadata
- Authors
- Riplinger, C., Pinski, P., Becker, U., Valeev, E. F., & Neese, F.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2016-01-11
- Volume
- 144
- Issue
- 2
- Pages
- 024109-024109
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2234552091
- Cited by
- 1,146
- References
- 1
- FWCI
- 37.31
- 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: Sparse maps—A systematic infrastructure for reduced-scaling electronic structure methods. II. Linear scaling domain based pair natural orbital coupled cluster theory. https://app.cochat.ai/papers/10.1063/1.4939030
- The paper
- Riplinger, C., Pinski, P., Becker, U., Valeev, E. F., & Neese, F. (2016). Sparse maps—A systematic infrastructure for reduced-scaling electronic structure methods. II. Linear scaling domain based pair natural orbital coupled cluster theory. The Journal of Chemical Physics. https://doi.org/10.1063/1.4939030
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.