The accurate prediction of excited state properties is
a key element
of rational photocatalyst design. This involves the prediction of
ground and excited state redox potentials, for which an accurate description
of electronic structures is needed. Even with highly sophisticated
computational approaches, however, a number of difficulties arise
from the complexity of excited state redox potentials, as they require
the calculation of the corresponding ground state redox potentials
and the estimation of the 0–0 transition energies (E0,0). In this study, we have systematically
evaluated the performance of DFT methods for these quantities on a
set of 37 organic photocatalysts representing 9 different chromophore
scaffolds. We have found that the ground state redox potentials can
be predicted with reasonable accuracy that can be further improved
by rationally minimizing the systematic underestimations. The challenging
part is to obtainE0,0, as calculating
it directly is highly demanding and its accuracy depends strongly
on the DFT functional employed. We have found that approximatingE0,0with appropriately scaled vertical absorption
energies offers the best compromise between accuracy and computational
effort. An even more accurate and cost-effective approach, however,
is to predictE0,0with machine learning
and avoid the use of DFT for excited state calculations. Indeed, the
best excited state redox potential predictions are achieved with the
combination of M062X for ground state redox potentials and machine
learning (ML) forE0,0. With this protocol,
the excited state redox potential windows of the photocatalyst frameworks
could be adequately predicted. This shows the potential of combining
DFT with ML in the computational design of photocatalysts with preferred
photochemical properties.