Renormalized Internally Contracted Multireference Coupled Cluster with Perturbative Triples

In this work, we combine the many-body formulation of the internally contracted multireference coupled cluster (ic-MRCC) method with Evangelista's multireference formulation of the driven similarity renormalization group (DSRG). The DSRG method can be viewed as a unitary multireference coupled cluster theory, which renormalizes the amplitudes based on a flow equation approach to eliminate numerical instabilities. We extend this approach by demonstrating that the unitary flow equation approach can be adapted for nonunitary transformations, rationalizing the renormalization of ic-MRCC amplitudes. We denote the new approach, the renormalized ic-MRCC (ric-MRCC) method. To achieve high accuracy with a reasonable computational cost, we introduce a new approximation to the Baker-Campbell-Hausdorff expansion. We fully consider the linear commutator while approximating the quadratic commutator, for which we neglect specific contractions involving amplitudes with active indices. Moreover, we introduce approximate perturbative triples to obtain the ric-MRCCSD[T] method. We demonstrate the accuracy of our approaches in comparison to advanced multireference methods for the potential energy curves of H<sub>8</sub>, F<sub>2</sub>, H<sub>2</sub>O, N<sub>2</sub>, and Cr<sub>2</sub>. Additionally, we show that ric-MRCCSD and ric-MRCSSD[T] match the accuracy of CCSD(T) for evaluating spectroscopic constants and of full configuration interaction energies for a set of small molecules.

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Publication Details

Journal
Journal of Chemical Theory and Computation
Published
2024-08-19
DOI
https://doi.org/10.1021/acs.jctc.4c00679
Citations
4
Primary Topic
Random Matrices and Applications
Type
article
Field-Weighted Citation Impact
2.84

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article

Renormalized Internally Contracted Multireference Coupled Cluster with Perturbative Triples

Robin Feldmann, Markus Reiher
4 citations
Journal of Chemical Theory and Computation
Random Matrices and Applications
2.84
article

Renormalized Internally Contracted Multireference Coupled Cluster with Perturbative Triples

Robin Feldmann, Markus Reiher
article en
4 citations

Abstract

In this work, we combine the many-body formulation of the internally contracted multireference coupled cluster (ic-MRCC) method with Evangelista's multireference formulation of the driven similarity renormalization group (DSRG). The DSRG method can be viewed as a unitary multireference coupled cluster theory, which renormalizes the amplitudes based on a flow equation approach to eliminate numerical instabilities. We extend this approach by demonstrating that the unitary flow equation approach can be adapted for nonunitary transformations, rationalizing the renormalization of ic-MRCC amplitudes. We denote the new approach, the renormalized ic-MRCC (ric-MRCC) method. To achieve high accuracy with a reasonable computational cost, we introduce a new approximation to the Baker-Campbell-Hausdorff expansion. We fully consider the linear commutator while approximating the quadratic commutator, for which we neglect specific contractions involving amplitudes with active indices. Moreover, we introduce approximate perturbative triples to obtain the ric-MRCCSD[T] method. We demonstrate the accuracy of our approaches in comparison to advanced multireference methods for the potential energy curves of H<sub>8</sub>, F<sub>2</sub>, H<sub>2</sub>O, N<sub>2</sub>, and Cr<sub>2</sub>. Additionally, we show that ric-MRCCSD and ric-MRCSSD[T] match the accuracy of CCSD(T) for evaluating spectroscopic constants and of full configuration interaction energies for a set of small molecules.

Journal of Chemical Theory and ComputationVol. 20(16)
ETH Zurich (CH)
National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Openalex Percentile: Top 9%
Random Matrices and Applications
2.84
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Renormalized Internally Contracted Multireference Coupled Cluster with Perturbative Triples — Robin Feldmann, Markus Reiher · Journal of Chemical Theory and Computation (2024) | TGRS Research Map | TGRS