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.
John, C.; Cignoni. E.; Cupellini, L. & Mennucci. B.
Machine-Learning Framework for Excitation Energies of Chromophores in Polarizable Environments
Journal of Chemical Information and Modeling (2026)
Read the full paper here: https://pubs.acs.org/doi/full/10.1021/acs.jcim.5c02424
