Key Role of Density Functional Approximation in Predicting M–N–C Catalyst Activities for Oxygen Reduction

Metal–nitrogen–carbon (M–N–C)
motifs
present intriguing structural and electronic properties for a number
of applications, including as oxygen reduction catalysts. However,
computational investigations of M–N–C-catalyzed reactions
must grapple with their complex electronic structures. In the present
study, we evaluate the impact of the density functional approximation
on calculated M–N–C catalyst activities for oxygen reduction.
Using metalloporphyrins as model catalysts, we find a significant
split between pure (GGA) and hybrid functionals, with hybrid functionals,
in particular B3LYP, showing greater agreement with DLPNO-CCSD(T)
reaction energies. Notably, double-hybrids offered no noticeable improvement
over the much more computationally efficient B3LYP and PBE0. Other
discrepancies between functionals, as well as an in-depth analysis
of ground state spin and geometry, are also considered in this work.
Finally, both hybrid and double-hybrid functionals greatly reduced
the gas phase errors associated with the main group molecules in the
oxygen reduction reaction relative to GGA calculations, leading us
to question the application of widely used empirical corrections to
O2.