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Coarse-grained simulations of conjugated polymers have become a popular wayof investigating the device physics of organic photovoltaics. While UV-Visspectroscopy remains one of key experimental methods for the interrogation ofthese devices, a rigorous bridge between coarse-grained simulations andspectroscopy has never been established. Here we address this challenge bydeveloping a method that predicts spectra of conjugated polymers directly fromcoarse-grained representations while avoiding ad-hoc procedures such asback-mapping from coarse-grained to atomistic representations followed bycomputing the spectra using standard quantum chemistry methods. Our approach isbased on a generative deep learning model: the long-short-term memory recurrentneural network (LSTM-RNN) and it is suggested by the apparent similaritybetween natural languages and the mathematical structure of the perturbativeexpansions of the excited state energies due to small fluctuations of thepolymer conformation. We use this model to demonstrate a dangerous discrepancybetween the spectra obtained in the coarse-grained representation and after theback-mapping. This indicates that standard protocols may require additionalfine-tuning in order to become reliable and that our model presents a noveltool uniquely suited for improving the back-mapping protocols and for includingspectral data in the development of coarse-grained potentials.

Prediction of optical spectra of coarse-grained polymers as a sequence generation problem: the Recurrent Neural Networks solution