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Title: Efficient Multiscale Optoelectronic Prediction for Conjugated Polymers

Abstract

Conjugated polymers represent a high-potential material class due to the tunability of their optoelectronic properties via straightforward processing protocols. However, to in silico tailor these optoelectronic properties, one must compute the conformationally dependent electronic structure over mesoscopic length and time scales. This task represents a challenging multiscale computational problem in which quantum chemistry and atomistic or coarse-grained molecular dynamics must be integrated. Recently, we introduced a methodology, called electronic coarse graining (ECG), that utilizes artificial neural networks to compute ab initio quality electronic structures using only the reduced degrees of freedom of coarse-grained models. Here, we adapt ECG to variable-molecular weight conjugated oligomers by casting the problem in the framework of sequence detection. A machine-learning (ML) architecture employing one-dimensional convolutional neural networks and long short-term memory networks is utilized to compute ground-state orbital energies, charge density distributions, and optical spectra solely from the coarse-grained model's configurational degrees of freedom. Robust molecular weight transferability for ECG is established via a A-ML approach that leverages model electronic Hamiltonians for ground and excited-state property determination. The accuracy and transferability of the ECG methodology opens the door for scalable optoelectronic property prediction in conjugated polymers directly from coarse-grained degrees of freedom.

Authors:
ORCiD logo [1];  [2]; ORCiD logo [1]
  1. Argonne National Lab. (ANL), Argonne, IL (United States). Materials Science Division; Univ. of Illinois, Chicago, IL (United States)
  2. Univ. of Illinois, Chicago, IL (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1601973
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
Macromolecules
Additional Journal Information:
Journal Volume: 53; Journal Issue: 1; Journal ID: ISSN 0024-9297
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; Electronic structure; Hamiltonians; Electrical properties; Conjugated polymers; Energy

Citation Formats

Jackson, Nicholas E., Bowen, Alec S., and de Pablo, Juan J. Efficient Multiscale Optoelectronic Prediction for Conjugated Polymers. United States: N. p., 2019. Web. https://doi.org/10.1021/acs.macromol.9b02020.
Jackson, Nicholas E., Bowen, Alec S., & de Pablo, Juan J. Efficient Multiscale Optoelectronic Prediction for Conjugated Polymers. United States. https://doi.org/10.1021/acs.macromol.9b02020
Jackson, Nicholas E., Bowen, Alec S., and de Pablo, Juan J. Thu . "Efficient Multiscale Optoelectronic Prediction for Conjugated Polymers". United States. https://doi.org/10.1021/acs.macromol.9b02020. https://www.osti.gov/servlets/purl/1601973.
@article{osti_1601973,
title = {Efficient Multiscale Optoelectronic Prediction for Conjugated Polymers},
author = {Jackson, Nicholas E. and Bowen, Alec S. and de Pablo, Juan J.},
abstractNote = {Conjugated polymers represent a high-potential material class due to the tunability of their optoelectronic properties via straightforward processing protocols. However, to in silico tailor these optoelectronic properties, one must compute the conformationally dependent electronic structure over mesoscopic length and time scales. This task represents a challenging multiscale computational problem in which quantum chemistry and atomistic or coarse-grained molecular dynamics must be integrated. Recently, we introduced a methodology, called electronic coarse graining (ECG), that utilizes artificial neural networks to compute ab initio quality electronic structures using only the reduced degrees of freedom of coarse-grained models. Here, we adapt ECG to variable-molecular weight conjugated oligomers by casting the problem in the framework of sequence detection. A machine-learning (ML) architecture employing one-dimensional convolutional neural networks and long short-term memory networks is utilized to compute ground-state orbital energies, charge density distributions, and optical spectra solely from the coarse-grained model's configurational degrees of freedom. Robust molecular weight transferability for ECG is established via a A-ML approach that leverages model electronic Hamiltonians for ground and excited-state property determination. The accuracy and transferability of the ECG methodology opens the door for scalable optoelectronic property prediction in conjugated polymers directly from coarse-grained degrees of freedom.},
doi = {10.1021/acs.macromol.9b02020},
journal = {Macromolecules},
number = 1,
volume = 53,
place = {United States},
year = {2019},
month = {12}
}

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