期刊
JOURNAL OF COMPUTATIONAL CHEMISTRY
卷 40, 期 32, 页码 2793-2800出版社
WILEY
DOI: 10.1002/jcc.26037
关键词
FFLUX; QTAIM; QCT; electron correlation; Moller Plesset
资金
- EPSRC [EP/K005472]
- Rutherford Fund Strategic Partner Award [2014/21241-9]
- Brazilian government's FAPESP [2014/21241-9]
- Brazilian government's CAPES [2014/21241-9]
- EPSRC [EP/K005472/1] Funding Source: UKRI
Recently, the quantum topological energy partitioning method called interacting quantum atoms (IQA) has been extended to MPn (n = 2, 3, 4) wave functions. This enables the extraction of chemical insight related to dynamic electron correlation. The large computational expense of the IQA-MPn approach is compensated by the advantages that IQA offers compared to older nontopological energy decomposition schemes. This expense is problematic in the construction of a machine learning training set to create kriging models for topological atoms. However, the algorithm presented here markedly accelerates the calculation of atomically partitioned electron correlation energies. Then again, the algorithm cannot calculate pairwise interatomic energies because it applies analytical integrals over whole space (rather than over atomic volumes). However, these pairwise energies are not needed in the quantum topological force field FFLUX, which only uses the energy of an atom interacting with all remaining atoms of the system that it is part of. Thus, it is now feasible to generate accurate and sizeable training sets at MPn level of theory. (c) 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.
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