Fragment-based 13C nuclear magnetic resonance chemical shift predictions in molecular crystals: An alternative to planewave methods

We assess the quality of fragment-based ab initio isotropic 13C chemical shift predictions for a collection of 25 molecular crystals with eight different density functionals. We explore the relative performance of cluster, two-body fragment, combined cluster/fragment, and the planewave gauge-includi...

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Bibliographic Details
Published in:The Journal of chemical physics 2015-09, Vol.143 (10)
Main Authors: Hartman, Joshua D., Monaco, Stephen, Schatschneider, Bohdan, Beran, Gregory J. O.
Format: Article
Language:English
Subjects:
Carbon 13
Chemical equilibrium
Clusters
Density
Electrostatic shielding
Fragmentation
Functionals
Magnetic shielding
Mathematical models
NMR
Nuclear magnetic resonance
Organic chemistry
Quality assessment
Statistical analysis
Tensors
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Summary:We assess the quality of fragment-based ab initio isotropic 13C chemical shift predictions for a collection of 25 molecular crystals with eight different density functionals. We explore the relative performance of cluster, two-body fragment, combined cluster/fragment, and the planewave gauge-including projector augmented wave (GIPAW) models relative to experiment. When electrostatic embedding is employed to capture many-body polarization effects, the simple and computationally inexpensive two-body fragment model predicts both isotropic 13C chemical shifts and the chemical shielding tensors as well as both cluster models and the GIPAW approach. Unlike the GIPAW approach, hybrid density functionals can be used readily in a fragment model, and all four hybrid functionals tested here (PBE0, B3LYP, B3PW91, and B97-2) predict chemical shifts in noticeably better agreement with experiment than the four generalized gradient approximation (GGA) functionals considered (PBE, OPBE, BLYP, and BP86). A set of recommended linear regression parameters for mapping between calculated chemical shieldings and observed chemical shifts are provided based on these benchmark calculations. Statistical cross-validation procedures are used to demonstrate the robustness of these fits.
ISSN:0021-9606
1089-7690
DOI:10.1063/1.4922649