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  Comparison of many‐particle representations for selected‐CI I: A tree based approach

Chilkuri, V. G., & Neese, F. (2021). Comparison of many‐particle representations for selected‐CI I: A tree based approach. Journal of Computational Chemistry, 42(14), 982-1005. doi:10.1002/jcc.26518.

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 Creators:
Chilkuri, Vijay Gopal1, Author           
Neese, Frank1, Author           
Affiliations:
1Research Department Neese, Max-Planck-Institut für Kohlenforschung, Max Planck Society, ou_2541710              

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Free keywords: configuration interaction; configuration interaction algorithms; graphical unitary group adaptation; selected CI; spin adaptation
 Abstract: The full configuration interaction (FCI) method is only applicable to small molecules with few electrons in moderate size basis sets. One of the main alternatives to obtain approximate FCI energies for bigger molecules and larger basis sets is selected CI. However, due to: (a) the lack of a well‐defined structure in a selected CI Hamiltonian, (b) the potentially large number of electrons together with c) potentially large orbital spaces, a computationally and memory efficient algorithm is difficult to construct. In the present series of papers, we describe our attempts to address these issues by exploring tree‐based approaches. At the same time, we devote special attention to the issue of obtaining eigenfunctions of the total spin squared operator since this is of particular importance in tackling magnetic properties of complex open shell systems. Dedicated algorithms are designed to tackle the CI problem in terms of determinant, configuration (CFG) and configuration state function many‐particle bases by effective use of the tree representation. In this paper we describe the underlying logic of our algorithm design and discuss the advantages and disadvantages of the different many particle bases. We demonstrate by the use of small examples how the use of the tree simplifies many key algorithms required for the design of an efficient selected CI program. Our selected CI algorithm, called the iterative configuration expansion, is presented in the penultimate part. Finally, we discuss the limitations and scaling characteristics of the present approach.

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Language(s): eng - English
 Dates: 2020-11-062021-03-022021-05-30
 Publication Status: Published online
 Pages: 24
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1002/jcc.26518
 Degree: -

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Title: Journal of Computational Chemistry
  Abbreviation : J. Comput. Chem.
Source Genre: Journal
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Publ. Info: New York : Wiley
Pages: - Volume / Issue: 42 (14) Sequence Number: - Start / End Page: 982 - 1005 Identifier: ISSN: 0192-8651
CoNE: https://pure.mpg.de/cone/journals/resource/954925489848