Non-parametrized functionals with empirical dispersion corrections: A happy match?

The performances of two parametrized functionals (namely B3LYP and B2PYLP) have been compared with those of two non-parametrized functionals (PBE0 and PBE0-DH) on a relatively large benchmark set when three different types of dispersion corrections are applied [namely the D2, D3 and D3(BJ) models]. Globally, the MAD computed using non-parametrized functionals decreases when adding dispersion terms although the accuracy not necessarily increases with the complexity of the model of dispersion correction used. In particular, the D2 correction is found to improve the performances of both PBE0 and PBE0-DH, while no systematic improvement is observed going from D2 to D3 or D3(BJ) corrections. Indeed when including dispersion, the number of sets for which PBE0-DH is the best performing functional decreases at the benefit of B2PLYP. Overall, our results clearly show that inclusion of dispersion corrections is more beneficial to parametrized double-hybrid functionals than to non-parametrized ones. The same conclusions globally hold for the corresponding global hybrids, showing that the marriage between non-parametrized functionals and empirical corrections may be a difficult deal.