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Molecular Physics
An International Journal at the Interface Between Chemistry and Physics
Volume 118, 2020 - Issue 19-20: Special Issue of Molecular Physics in Honour of Jürgen Gauss
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Research Articles

Accelerating convergence of equation-of-motion coupled-cluster computations using the semi-stochastic CC(P;Q) formalism

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Article: e1817592 | Received 08 Apr 2020, Accepted 26 Aug 2020, Published online: 17 Sep 2020
 

Abstract

The recently proposed approach to excited electronic states, in which the deterministic equation-of-motion coupled-cluster (EOMCC) framework is merged with stochastic configuration interaction Quantum Monte Carlo (CIQMC) computations [J.E. Deustua et al., J. Chem. Phys. 150, 111101 (2019)], is combined with the noniterative energy corrections derived from the CC(P;Q) formalism. By examining vertical excitations in CH+ at the equilibrium and stretched geometries and adiabatic excitations in CH and CNC, we demonstrate that the resulting semi-stochastic CC(P;Q) methodology converges target high-level energetics, represented in this study by the EOMCC method with singles, doubles, and triples, in the early stages of CIQMC propagations.

GRAPHICAL ABSTRACT

This article is part of the following collections:
Longuet-Higgins Early Career Researcher Prize

Acknowledgments

We dedicate this paper to Professor Jürgen Gauss on the occasion of his 60th birthday. One of us (Piotr Piecuch) would like to thank Drs. Janus Eriksen, Stella Stopkowicz, and Thomas Jagau and Professor Trygve Helgaker for inviting him to submit an article for the Special Issue of Molecular Physics in honour of Professor Jürgen Gauss.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work has been supported by the U.S. Department of Energy (Grant number DE-FG02-01ER15228 awarded to P.P.), National Science Foundation (Grant number CHE-1763371 awarded to P.P.), and Phase I and II Software Fellowships awarded to J.E.D. by the Molecular Sciences Software Institute funded by the National Science Foundation (grant number ACI-1547580).

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