Polarizable Frozen Density Embedding with External Orthogonalization
Author(s) -
Partha Pratim Pal,
Pengchong Liu,
Lasse Jensen
Publication year - 2019
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.9b00472
Subject(s) - embedding , dipole , density functional theory , orthogonalization , coupled cluster , operator (biology) , ground state , physics , statistical physics , molecule , quantum mechanics , molecular physics , computer science , chemistry , algorithm , biochemistry , repressor , artificial intelligence , transcription factor , gene
We report a polarizable subsystem density functional theory to describe electronic properties of molecules embedded on a metal cluster. Interaction between the molecule and metal cluster is described using frozen density embedding (FDE). Substituting the nonadditive kinetic potential (NAKP) by approximate functionals is circumvented by enforcing external orthogonality (EO) through a projection operator. The computationally expensive freeze/thaw (FT) cycles are bypassed by including a polarization term in the embedding operator. Furthermore, the combination of polarization and EO permits supermolecular basis set calculations, which was not possible for strongly interacting systems with existing kinetic energy functionals. To test the method, we described the ground state density of pyridine, water, and benzene on a silver cluster. Performing FT on top of EO results in exact density embedding for this category of systems and is thus used for benchmarking the method. We find that the density is reproduced to within 0.15e, and the dipole and quadrupole moments are within 18% of the reference points for subsystem separations ranging from bonding to noninteracting distances. Additionally, our formalism allows the flexibility of incorporating different density functionals to the molecular and the metallic subsystems reducing the overall computational cost.
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