Machine-Learning Predictions of Rate Constants of Internal Conversion Using Electronic and Structural Descriptors

We present a machine learning approach to predict internal conversion rate constants (kIC) based on molecular electronic and structural descriptors. Our approach addresses the challenge of accurately estimating kIC between the first excited singlet state (S1) and the ground state (S0), which is an important deactivation channel of photophysical processes. Using a cost-efficient method that assumes excitation-energy transfer to the vibrations of the X–H bonds (X = C, N, O), we generated a data set for kIC of more than 5000 molecules, including porphyrins and [8]circulenes using parameters calculated at the time-dependent density functional theory (TDDFT) level. We have used both the CatBoost (CB) model and a neural network (NN) including transformer-based and graph-based architectures. Using the CB and NN models we obtained high precision with R2 values of about 0.99. The models successfully predict kIC even for new molecules when they are trained with energy-augmented data sets based on electronic and structural descriptors. We also introduce graph-based X–H bond descriptors to predict rate constants for various chemical classes. The calculated rate constants indicate the promising potential of these descriptors to predict kIC using machine learning to bypass computationally demanding quantum mechanical calculations to determine the photophysical properties.