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

09 March 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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 nine 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 obtain E0,0 as calculating it directly is highly demanding and its accuracy depends strongly on the DFT functional employed. We have found that approximating E0,0 with 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 predict E0,0 with 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 ML for E0,0. 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.


reduction potential
excitation energy

Supplementary materials

Supplementary Material
Relation between E0,0 and Eabs Analysis of the solvation energies Ground state redox potentials when both shift and scaling corrections are applied to the predictions MAEs of the different shifts applied to the predicted ground state potentials The MAEs for the shifts in Fig. S7 Excited state redox potentials without the use of any adjustment Excited state redox potentials using the E0,0 = 0.91×Eabs approximation Excited state redox potentials using the E0,0 = 0.91×Eabs approximation and the universal ground state potential shift of 0.3 V Solvents used for the molecule types Reference data used for the ground and excited state potentials Analysis of the machine learning model

Supplementary weblinks


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