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doi = {10.1016/j.amc.2014.02.051}
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doi = {10.1016/j.amc.2014.02.051}
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}
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}
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@article{villa_2011,
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author = {Villa, M. and Senent, M. L. and Dominguez-Gomez, R. and Alvarez-Bajo, O. and Carvajal, M.},
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title = {{CCSD(T) Study of Dimethyl-Ether Infrared and Raman Spectra}},
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journal = {J. Phys. Chem. A},
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volume = {115},
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number = {46},
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pages = {13573--13580},
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year = {2011},
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month = nov,
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issn = {1089-5639},
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publisher = {American Chemical Society},
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doi = {10.1021/jp2062223}
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}
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i@article{watson_2016,
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author = {Watson, Peter D. and Yong, Hai-wang and Lapere, Kim M. L. and Kettner, Marcus and McKinley, Allan J. and Wild, Duncan A.},
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title = {{Anion photoelectron spectroscopy and CCSD(T) calculations of the Cl{-}{$\cdots$}N2 complex}},
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journal = {Chem. Phys. Lett.},
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volume = {654},
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pages = {119--124},
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year = {2016},
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month = jun,
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issn = {0009-2614},
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publisher = {North-Holland},
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doi = {10.1016/j.cplett.2016.04.073}
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}
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@article{dontgen_2015,
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author = {D{\"{o}}ntgen, Malte and Przybylski-Freund, Marie-Dominique and Kr{\"{o}}ger, Leif C. and Kopp, Wassja A. and Ismail, Ahmed E. and Leonhard, Kai},
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title = {{Automated Discovery of Reaction Pathways, Rate Constants, and Transition States Using Reactive Molecular Dynamics Simulations}},
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journal = {J. Chem. Theory Comput.},
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volume = {11},
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number = {6},
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pages = {2517--2524},
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year = {2015},
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month = jun,
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issn = {1549-9618},
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publisher = {American Chemical Society},
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doi = {10.1021/acs.jctc.5b00201}
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}
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@article{zhang_2019,
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author = {Zhang, Igor Ying and Gr{\"{u}}neis, Andreas},
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title = {{Coupled Cluster Theory in Materials Science}},
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journal = {Front. Mater.},
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volume = {6},
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pages = {432749},
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year = {2019},
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month = jun,
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issn = {2296-8016},
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publisher = {Frontiers},
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doi = {10.3389/fmats.2019.00123}
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}
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@article{stanton_1997,
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author = {Stanton, John F.},
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title = {{Why CCSD(T) works: a different perspective}},
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journal = {Chem. Phys. Lett.},
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volume = {281},
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number = {1},
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pages = {130--134},
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year = {1997},
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month = dec,
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issn = {0009-2614},
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publisher = {North-Holland},
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doi = {10.1016/S0009-2614(97)01144-5}
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}
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@article{castaneda_2012,
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author = {Casta{\~{n}}eda, Romina and Iuga, Cristina and {\'{A}}lvarez-Idaboy, J. Ra{\'{u}}l and Vivier-Bunge, Annik},
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title = {{Rate Constants and Branching Ratios in the Oxidation of Aliphatic Aldehydes by OH Radicals under Atmospheric Conditions}},
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journal = {J. Mex. Chem. Soc.},
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volume = {56},
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number = {3},
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pages = {316--324},
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year = {2012},
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month = sep,
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issn = {1870-249X},
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publisher = {Sociedad Qu{\'{\i}}mica de M{\'{e}}xico A.C.},
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url = {https://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1870-249X2012000300014}
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}
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@article{du_2020,
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author = {Du, Benni and Zhang, Weichao},
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title = {{Theoretical Insight into the Reaction Mechanism and Kinetics for the Criegee Intermediate of anti-PhCHOO with SO2}},
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journal = {Molecules},
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volume = {25},
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number = {13},
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pages = {3041},
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year = {2020},
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month = jul,
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issn = {1420-3049},
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publisher = {Multidisciplinary Digital Publishing Institute},
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doi = {10.3390/molecules25133041}
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}
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@article{mallick_2020,
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author = {Mallick, Subhasish and Roy, Bonasree and Kumar, Pradeep},
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title = {{A comparison of DLPNO-CCSD(T) and CCSD(T) method for the determination of the energetics of hydrogen atom transfer reactions}},
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journal = {Comput. Theor. Chem.},
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volume = {1187},
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pages = {112934},
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year = {2020},
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month = oct,
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issn = {2210-271X},
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publisher = {Elsevier},
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doi = {10.1016/j.comptc.2020.112934}
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}
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@article{vilarrubias_2020,
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author = {Pere Vilarrubias},
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title = {Electronic spectroscopy of some small anions containing S, N and O using CR-EOM-CCSD(T) method},
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journal = {Molecular Physics},
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volume = {118},
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number = {24},
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pages = {e1797915},
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year = {2020},
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publisher = {Taylor & Francis},
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doi = {10.1080/00268976.2020.1797915},
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URL = {https://doi.org/10.1080/00268976.2020.1797915},
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eprint = {https://doi.org/10.1080/00268976.2020.1797915}
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}
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@ -108,15 +108,16 @@ Coupled cluster (CC) theory is a powerful quantum mechanical approach widely use
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It offers a systematic and rigorous framework for accurate predictions of molecular properties and reactions by accounting for electron correlation effects beyond the mean-field approximation.
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It offers a systematic and rigorous framework for accurate predictions of molecular properties and reactions by accounting for electron correlation effects beyond the mean-field approximation.
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Among the various variants of the CC method, the CCSD(T) method stands as the gold standard of quantum chemistry.
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Among the various variants of the CC method, the CCSD(T) method stands as the gold standard of quantum chemistry.
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CCSD(T), which incorporates singles, doubles, and a perturbative treatment of triples, has demonstrated exceptional accuracy and reliability, making it one of the preferred choices for benchmark calculations and highly accurate predictions.
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CCSD(T), which incorporates singles, doubles, and a perturbative treatment of triples, has demonstrated exceptional accuracy and reliability, making it one of the preferred choices for benchmark calculations and highly accurate predictions.
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The CCSD(T) method has found successful applications in a diverse range of areas, including spectroscopy, reaction kinetics, and materials design, and has played a pivotal role in advancing our understanding of complex chemical phenomena.
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The CCSD(T) method has found successful applications in a diverse range of areas, including spectroscopy,\cite{villa_2011,watson_2016,vilarrubias_2020} reaction kinetics,\cite{dontgen_2015,castaneda_2012} and materials design,\cite{zhang_2019} and has played a pivotal role in advancing our understanding of complex chemical phenomena.
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In the context of CC theory, perturbative triples represent an important contribution to the accuracy of electronic structure calculations.
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In the context of CC theory, perturbative triples represent an important contribution to the accuracy of electronic structure calculations.\cite{stanton_1997}
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However, the computational cost associated with their calculation can be prohibitively high, especially for large molecular systems.
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However, the computational cost associated with their calculation can be prohibitively high, especially for large molecular systems.
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The CCSD(T) method, which includes the perturbative treatment of triples, is known to have a computational scaling of $\order{N^7}$, where $N$ represents the system size.
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The CCSD(T) method, which includes the perturbative treatment of triples, is known to have a computational scaling of $\order{N^7}$, where $N$ represents the system size.
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This scaling can rapidly become impractical, posing significant challenges in terms of computational resources and time requirements.
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This scaling can rapidly become impractical, posing significant challenges in terms of computational resources and time requirements.
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To address this computational bottleneck, our goal is to develop a novel semi-stochastic algorithm that brings back the computational time to a level comparable to that of the CCSD method, which has a scaling of $\order{N^6}$, while ensuring well-controlled approximations.
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To address this computational bottleneck, our goal is to develop a novel semi-stochastic algorithm that brings back the computational time to a level comparable to that of the CCSD method, which has a scaling of $\order{N^6}$, while ensuring well-controlled approximations.
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Our algorithm strikes a balance between computational efficiency and accuracy, making calculations for larger systems more feasible without compromising precision.
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Our algorithm strikes a balance between computational efficiency and
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accuracy, making calculations for larger basis sets more feasible without compromising precision.
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By incorporating stochastic sampling techniques, our approach provides an alternative avenue for approximating perturbative triples, relieving the computational burden inherent in traditional deterministic methods. This not only reduces the computational time to a more favorable level but also preserves the parallelism capabilities of CC calculations, ensuring efficient utilization of computational resources.
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By incorporating stochastic sampling techniques, our approach provides an alternative avenue for approximating perturbative triples, relieving the computational burden inherent in traditional deterministic methods. This not only reduces the computational time to a more favorable level but also preserves the parallelism capabilities of CC calculations, ensuring efficient utilization of computational resources.
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In the following sections of this paper, we will provide a brief introduction to the computation of perturbative triples in coupled cluster theory. We will explain the principles of our semi-stochastic algorithm, outlining its key features and advantages. Additionally, we will present implementation details, discussing the technical aspects and considerations involved in the algorithm's practical realization. To demonstrate the effectiveness and applicability of our approach, we finally present illustrative examples that showcase the algorithm's performance and compare it with the conventional algorithm.
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In the following sections of this paper, we will provide a brief introduction to the computation of perturbative triples in coupled cluster theory. We will explain the principles of our semi-stochastic algorithm, outlining its key features and advantages. Additionally, we will present implementation details, discussing the technical aspects and considerations involved in the algorithm's practical realization. To demonstrate the effectiveness and applicability of our approach, we finally present illustrative examples that showcase the algorithm's performance and compare it with the conventional algorithm.
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2748
triples.org
2748
triples.org
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