Prediction of Redox Power for Photocatalysts: Synergistic Combination of DFT and Machine Learning.

ABSTRACT: 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 (E_0,0). 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 ca

SUBMITTER: Feher PP 

PROVIDER: S-EPMC10339717 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature