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Title: Complexity Reduction in Large Quantum Systems: Fragment Identification and Population Analysis via a Local Optimized Minimal Basis

Journal Article · · Journal of Chemical Theory and Computation
ORCiD logo [1];  [2];  [3];  [4]
  1. Barcelona Supercomputing Center (BSC), Barcelona (Spain)
  2. Institut de Biologie et de Technologie de Saclay, Gif-sur-Yvette Cedex (France)
  3. Argonne National Lab. (ANL), Argonne, IL (United States)
  4. Univ. Grenoble Alpes, Grenoble (France); CEA, Grenoble (France)
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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 introduce a simple and efficient formalism (which can be written as generalization of other well-known population analyses) to extract, from first principles, electrostatic multipoles for these fragments. The corresponding fragment multipoles can in this way be seen as reliable (pseudo-) observables. By applying our formalism within the code BigDFT, we show that the usage of a minimal set of in-situ optimized basis functions is of utmost importance for having at the same time a proper fragment definition and an accurate description of the electronic structure. With this approach it becomes possible to simplify the modeling of environmental fragments by a set of multipoles, without notable loss of precision in the description of the active quantum mechanical region. Furthermore, this leads to a considerable reduction of the degrees of freedom by an effective coarsegraining approach, eventually also paving the way towards efficient QM/QM and QM/MM methods coupling together different levels of accuracy.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
European Commission, Community Research and Development Information Service (CORDIS), EXTended Model of Organic Semiconductors (ExtMOS); Energy Oriented Centre of Excellence (EoCoE); USDOE Office of Science (SC), Basic Energy Sciences (BES); Argonne National Laboratory, Argonne Leadership Computing Facility; MaX Centre of Excellence
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1400406
Journal Information:
Journal of Chemical Theory and Computation, Vol. 13, Issue 9; ISSN 1549-9618
Publisher:
American Chemical SocietyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 15 works
Citation information provided by
Web of Science

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