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\newcommand{\LCPQ}{Laboratoire de Chimie et Physique Quantiques (UMR 5626), Universit\'e de Toulouse, CNRS, UPS, France}
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\title{Configuration interaction with seniority number and excitation degree}
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\title{Configuration Interaction with Seniority Number and Excitation Degree}
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\author{F\'abris Kossoski}
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\email{fkossoski@irsamc.ups-tlse.fr}
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% Abstract
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\begin{abstract}
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%aimed at recovering both static and dynamic correlation,
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Here we propose a novel partitioning of the Hilbert space, hierarchy configuration interaction (hCI),
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We propose a novel partitioning of the Hilbert space, hierarchy configuration interaction (hCI),
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where the degree of excitation (with respect to a given reference) and the seniority number (number of unpaired electrons) are combined in a single hierarchy parameter.
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The key appealing feature of hCI is that it includes all classes of determinants that share the same scaling with the number of electrons and basis functions.
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In this way, it accounts for low-seniority high-excitation determinants lacking in excitation-based CI, while keeping the same computational scaling with system size.
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By surveying the dissociation of multiple molecular systems, we examined how fast hCI and their excitation-based and seniority-based parents converge as
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we step up towards the exact full CI limit.
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We found that the overall performance of hCI usually exceeds or at least parallels that of excitation-based CI.
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For small systems and basis sets, doubly-occupied CI (the first level of seniority-based CI) often remains the best option.
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However, for larger systems or basis sets, and for higher accuracy, seniority-based CI becomes impractical.
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However, some of its interesting features, particularly the small non-parallelity errors, are partially recovered with hCI, at only a polynomical cost.
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We have futher explored the role of optimizing the orbitals at several levels of CI.
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The key appealing feature of hCI is that each level of the hierarchy accounts for all classes of determinants that share the same scaling with the system size.
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%number of electrons and basis functions.
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%In this way, it accounts for low-seniority high-excitation determinants lacking in excitation-based CI, while keeping the same computational scaling with system size.
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By surveying the dissociation of multiple molecular systems, we found that the overall performance of hCI usually exceeds or at least parallels that of excitation-based CI.
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%By surveying the dissociation of multiple molecular systems, we examined how fast hCI and their excitation-based and seniority-based parents converge as we step up towards the exact full CI limit.
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%The overall performance of hCI usually exceeds or at least parallels that of excitation-based CI.
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%For small systems and basis sets, doubly-occupied CI (the first level of seniority-based CI) often remains the best option, but becomes impractical for larger systems or basis sets, and for higher accuracy.
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%However, for larger systems or basis sets, and for higher accuracy, seniority-based CI becomes impractical.
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%However, some of its interesting features, particularly the small non-parallelity errors, are partially recovered with hCI, at only a polynomial cost.
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%We have further explored the role of optimizing the orbitals at several levels of CI.
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For higher orders of hCI and excitation-based CI,
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the additional computational burden and other known issues related to orbital optimization usually do not compensate the marginal improvements often observed,
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when compared with results obtained with canonical Hartree-Fock orbitals.
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the additional computational burden related to orbital optimization usually do not compensate the marginal improvements compared with results obtained with Hartree-Fock orbitals.
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The exception is orbital-optimized CI with single excitations, a minimally correlated model displaying the qualitatively correct description of single bond breaking,
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at a very modest computational cost.
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%\bigskip
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@ -191,7 +191,7 @@ at the same time as static correlation, by moving down (increasing the seniority
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The second justification is computational.
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%Fig.~\ref{fig:scaling} also illustrates how the number of determinants within each block scales with the number of occupied orbitals $O$ and the number of virtual orbitals $V$.
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In the hCI class of methods, each next level of theory accomodates additional determinants from different excitation-seniority sectors (each block of Fig.~\ref{fig:allCI}).
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In the hCI class of methods, each level of theory accomodates additional determinants from different excitation-seniority sectors (each block of Fig.~\ref{fig:allCI}).
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The key realization behind hCI is that the number of additional determinants presents the same scaling with respect to $N$, for all excitation-seniority sectors entering at a given hierarchy $h$.
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%to $O$ and $V$, for all excitation-seniority sectors of a given hierarchy $h$.
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%This computational realization represents the second justification for the introduction of the hCI method.
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