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Complexity reduction in large quantum systems: fragment identification and population analysis via a local optimized minimal basis.

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Title: Complexity reduction in large quantum systems: fragment identification and population analysis via a local optimized minimal basis.
Authors: Mohr, S
Masella, M
Ratcliff, LE
Genovese, L
Item Type: Journal Article
Abstract: We present, within Kohn-Sham density functional theory calculations, a quantitative method to identify and assess the partitioning of a large quantum-mechanical system into fragments. We then show how within this framework simple generalizations of other well-known population analyses can be used to extract, from first-principles, reliable electrostatic multipoles for the identified fragments. Our approach reduces arbitrariness in the fragmentation procedure and enables the possibility to assess quantitatively whether the corresponding fragment multipoles can be interpreted as observable quantities associated with a system moiety. By applying our formalism within the code BigDFT, we show that the use of a minimal set of in situ-optimized basis functions allows at the same time a proper fragment definition and an accurate description of the electronic structure.
Issue Date: 12-Sep-2017
Date of Acceptance: 21-Jul-2017
URI: http://hdl.handle.net/10044/1/61618
DOI: https://dx.doi.org/10.1021/acs.jctc.7b00291
ISSN: 1549-9618
Publisher: American Chemical Society
Start Page: 4079
End Page: 4088
Journal / Book Title: Journal of Chemical Theory and Computation
Volume: 13
Issue: 9
Copyright Statement: © 2017 American Chemical Society. This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Theory and Computation, after peer review and technical editing by the publisher. To access the final edited and published work see https://dx.doi.org/10.1021/acs.jctc.7b00291
Keywords: 0307 Theoretical And Computational Chemistry
Chemical Physics
Publication Status: Published
Conference Place: United States
Appears in Collections:Materials
Faculty of Engineering