Predicting optical spectra for optoelectronic polymers using coarse-grained models and recurrent neural networks.

ABSTRACT: Coarse-grained modeling of conjugated polymers has become an increasingly popular route to investigate the physics of organic optoelectronic materials. While ultraviolet (UV)-vis spectroscopy remains one of the key experimental methods for the interrogation of these materials, a rigorous bridge between simulated coarse-grained structures and spectroscopy has not been established. Here, we address this challenge by developing a method that can predict spectra of conjugated polymers directly from coarse-grained representations while avoiding repetitive procedures such as ad hoc back-mapping from coarse-grained to atomistic representations followed by spectral computation using quantum chemistry. Our approach is based on a generative deep-learning model: the long-short-term memory recu

SUBMITTER: Simine L 

PROVIDER: S-EPMC7322026 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature