Machine-Learning Framework for Excitation Energies of Chromophores in Polarizable Environments

Excited states of embedded chromophores are highly influenced by their interaction with the environment. Herein, we present a machine-learning (ML) framework capable of predicting the different environmental contributions to excitation energies of chromophores in a polarizable embedding. Our ML models are built in a hierarchical structure to capture both the effect of ground-state polarization and the response of the polarizable environment to the electronic transition. With the use of the right descriptors, the models trained on the quantum mechanics/molecular mechanics (QM/MM) calculations in a nonpolarizable environment are able to successfully predict the effects of a polarizable environment on excitation energies. The ML models are applied to three chromophores present in light-harvesting complexes (chlorophyll a, chlorophyll b, and lutein) and are used to reproduce the excitonic structure of a multichromophoric system unseen in the training set to a level of accuracy offered by a polarizable QM/MM calculation, while taking a fraction of its time.