LAUSR

Quantum mechanical simulations of charge and exciton transfer in molecular organic materials are a key method to increase our understanding of organic semiconductors. Our goal is to build an efficient multi-scale model to predict charge transfer mobilities and exciton diffusion constants from non-adiabatic molecular dynamics simulations and Marcus-based Monte-Carlo approaches. In this work, we apply machine learning models to simulate charge and exciton propagation in organic semiconductors. We show that kernel ridge regression models can be trained to predict electronic and excitonic couplings from semi-empirical DFTB reference data with very good accuracy. In simulations, the models could reproduce hole mobilities along the anthracene crystal axes to within 8.5% of the DFTB reference and 34% of experimental results with only 1000 training data points. Using these models decreased the cost of exciton transfer simulations by one order of magnitude.