Dataset Information

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 recurrent neural network (LSTM-RNN). The latter is suggested by the apparent similarity between natural languages and the mathematical structure of perturbative expansions of, in our case, excited-state energies perturbed by conformational fluctuations. We also use this model to explore the level of sensitivity of spectra to the coarse-grained representation back-mapping protocol. Our approach presents a tool uniquely suited for improving postsimulation analysis protocols, as well as, potentially, for including spectral data as input in the refinement of coarse-grained potentials.

SUBMITTER: Simine L

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

REPOSITORIES: biostudies-literature

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Publications

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

Simine Lena L Allen Thomas C TC Rossky Peter J PJ

Proceedings of the National Academy of Sciences of the United States of America 20200608 25

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 ...[more]

PMID: 32513725

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Dataset Information

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

Publications

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

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