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Subspace Density Matrix Functional Embedding Theory: Theory, Implementation, and Applications to Molecular Systems
Author(s) -
Xing Zhang,
Emily A. Carter
Publication year - 2018
Publication title -
journal of chemical theory and computation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.001
H-Index - 185
eISSN - 1549-9626
pISSN - 1549-9618
DOI - 10.1021/acs.jctc.8b00990
Subject(s) - embedding , subspace topology , density functional theory , wave function , matrix (chemical analysis) , density matrix , decomposition , computer science , matrix decomposition , function (biology) , algorithm , physics , quantum mechanics , chemistry , artificial intelligence , eigenvalues and eigenvectors , organic chemistry , chromatography , evolutionary biology , quantum , biology
We introduce the subspace density matrix functional embedding theory (sDMFET), in which optimization of the nonlocal embedding potential and subsequent embedded correlated wave function calculations are carried out within a truncated subspace determined by a Schmidt decomposition. As compared to the original density matrix functional embedding theory [K. Yu and E. A. Carter, Proceedings of the National Academy of Sciences 2017 , 114 , E10861 ], the computational cost of sDMFET is significantly reduced while the accuracy is preserved. We perform test calculations for both covalently and noncovalently bound molecular systems to demonstrate the feasibility of our theory.

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