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Benchmarking of density functionals for a soft but accurate prediction and assignment of 1 H and 13 C NMR chemical shifts in organic and biological molecules
Author(s) -
Benassi Enrico
Publication year - 2017
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.24521
Subject(s) - benchmarking , basis (linear algebra) , chemical shift , density functional theory , computer science , simple (philosophy) , range (aeronautics) , statistical physics , computational chemistry , chemistry , mathematics , physics , materials science , nuclear magnetic resonance , philosophy , geometry , epistemology , marketing , business , composite material
A number of programs and tools that simulate 1 H and 13 C nuclear magnetic resonance (NMR) chemical shifts using empirical approaches are available. These tools are user‐friendly, but they provide a very rough (and sometimes misleading) estimation of the NMR properties, especially for complex systems. Rigorous and reliable ways to predict and interpret NMR properties of simple and complex systems are available in many popular computational program packages. Nevertheless, experimentalists keep relying on these “unreliable” tools in their daily work because, to have a sufficiently high accuracy, these rigorous quantum mechanical methods need high levels of theory. An alternative, efficient, semi‐empirical approach has been proposed by Bally, Rablen, Tantillo, and coworkers. This idea consists of creating linear calibrations models, on the basis of the application of different combinations of functionals and basis sets. Following this approach, the predictive capability of a wider range of popular functionals was systematically investigated and tested. The NMR chemical shifts were computed in solvated phase at density functional theory level, using 30 different functionals coupled with three different triple–ζ basis sets. © 2016 Wiley Periodicals, Inc.