How good can a simple artificial neural network predict the medium reorganization energy and the free energy gap from a steady-state fluorescence spectrum?

Medium reorganization energy, E-rm, and free energy gap, Delta G, are key characteristics in determining the photoinduced charge transfer rate. They are strongly related to the width and position of the steady-state fluorescence and absorption spectra of organic dyes in solvents. Steady-state spectroscopy is one of the most standard techniques for studying organic dyes. Here, we present a simple machine learning model for cost-effective prediction the medium reorganization energy and the free energy gap from the steady-state fluorescence spectra. Fluorescence spectra with corresponding reference values of physical quantities are calculated using an explicit mathematical expression that addresses electronic-vibrational transitions, intramolecular and solvent reorganizations. An approach to the separate formation of data sets for training and validation is proposed. Gaussian noise is used in training to improve predictions. This study shows that E-rm and Delta G can be predicted with the mean absolute error similar to 0.024 eV and similar to 0.009 eV, respectively. Robustness testing is performed on the experimental spectra of coumarin-153, coumarin-307, and coumarin-522B in a series of solvents.

Automated summary

Medium reorganization energy and free energy gap are key characteristics in determining the photoinduced charge transfer rate. In this study, a simple machine learning model is presented to predict these characteristics from steady-state fluorescence spectra. The results show that the model can predict these characteristics with small errors, and it has been tested for robustness.