Ab initio predictions of chemical shifts
and electric
field gradient (EFG) tensor components are frequently used to help
interpret solid-state nuclear magnetic resonance (NMR) experiments.
Typically, these predictions employ density functional theory (DFT)
with generalized gradient approximation (GGA) functionals, though
hybrid functionals have been shown to improve accuracy relative to
experiment. Here, the performance of a dozen models beyond the GGA
approximation are examined for the prediction of solid-state NMR observables,
including meta-GGA, hybrid, and double-hybrid density functionals
and second-order Møller–Plesset perturbation theory (MP2).
These models are tested on organic molecular crystal data sets containing
169 experimental 13C and 15N chemical shifts
and 114 17O and 14N EFG tensor components. To
make these calculations affordable, gauge-including projector augmented
wave (GIPAW) Perdew–Burke–Ernzerhof (PBE) calculations
with periodic boundary conditions are combined with a local intramolecular
correction computed at the higher level of theory. Within the context
of typical NMR property calculations performed on a static, DFT-optimized
crystal structure, the benchmarking finds that the double-hybrid DFT
functionals produce errors versus experiment that are no smaller than
those of hybrid functionals in the best cases, and they can be larger.
MP2 errors versus experiment are even bigger. Overall, no practical
advantages are found for using any of the tested double-hybrid functionals
or MP2 to predict experimental solid-state NMR chemical shifts and
EFG tensor components for routine organic crystals, especially given
the higher computational cost of those methods. This finding likely
reflects error cancellation benefiting the hybrid functionals. Improving
the accuracy of the predicted chemical shifts and EFG tensors relative
to experiment would probably require more robust treatments of the
crystal structures, their dynamics, and other factors.