  Coarse-grained simulations of conjugated polymers have become a popular way
of investigating the device physics of organic photovoltaics. While UV-Vis
spectroscopy remains one of key experimental methods for the interrogation of
these devices, a rigorous bridge between coarse-grained simulations and
spectroscopy has never been established. Here we address this challenge by
developing a method that predicts spectra of conjugated polymers directly from
coarse-grained representations while avoiding ad-hoc procedures such as
back-mapping from coarse-grained to atomistic representations followed by
computing the spectra using standard quantum chemistry methods. Our approach is
based on a generative deep learning model: the long-short-term memory recurrent
neural network (LSTM-RNN) and it is suggested by the apparent similarity
between natural languages and the mathematical structure of the perturbative
expansions of the excited state energies due to small fluctuations of the
polymer conformation. We use this model to demonstrate a dangerous discrepancy
between the spectra obtained in the coarse-grained representation and after the
back-mapping. This indicates that standard protocols may require additional
fine-tuning in order to become reliable and that our model presents a novel
tool uniquely suited for improving the back-mapping protocols and for including
spectral data in the development of coarse-grained potentials.
