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Title: Electronic structure at coarse-grained resolutions from supervised machine learning

Computational studies aimed at understanding conformationally dependent electronic structure in soft materials require a combination of classical and quantum-mechanical simulations, for which the sampling of conformational space can be particularly demanding. Coarse-grained (CG) models provide a means of accessing relevant time scales, but CG configurations must be back-mapped into atomistic representations to perform quantum-chemical calculations, which is computationally intensive and inconsistent with the spatial resolution of the CG models. A machine learning approach, denoted as artificial neural network electronic coarse graining (ANN-ECG), is presented here in which the conformationally dependent electronic structure of a molecule is mapped directly to CG pseudo-atom configurations. By averaging over decimated degrees of freedom, ANN-ECG accelerates simulations by eliminating backmapping and repeated quantum-chemical calculations. The approach is accurate, consistent with the CG spatial resolution, and can be used to identify computationally optimal CG resolutions.
Authors:
ORCiD logo [1] ;  [2] ;  [2] ; ORCiD logo [2] ;  [3] ;  [1]
  1. Argonne National Lab. (ANL), Lemont, IL (United States); Univ. of Chicago, Chicago, IL (United States)
  2. Univ. of Chicago, Chicago, IL (United States)
  3. Argonne National Lab. (ANL), Lemont, IL (United States)
Publication Date:
Grant/Contract Number:
AC02-06CH11357
Type:
Accepted Manuscript
Journal Name:
Science Advances
Additional Journal Information:
Journal Volume: 5; Journal Issue: 3; Journal ID: ISSN 2375-2548
Publisher:
AAAS
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division; Midwest Integrated Center for Computational Materials (MICCoM)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 36 MATERIALS SCIENCE; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
OSTI Identifier:
1506239

Jackson, Nicholas E., Bowen, Alec S., Antony, Lucas W., Webb, Michael A., Vishwanath, Venkatram, and de Pablo, Juan J.. Electronic structure at coarse-grained resolutions from supervised machine learning. United States: N. p., Web. doi:10.1126/sciadv.aav1190.
Jackson, Nicholas E., Bowen, Alec S., Antony, Lucas W., Webb, Michael A., Vishwanath, Venkatram, & de Pablo, Juan J.. Electronic structure at coarse-grained resolutions from supervised machine learning. United States. doi:10.1126/sciadv.aav1190.
Jackson, Nicholas E., Bowen, Alec S., Antony, Lucas W., Webb, Michael A., Vishwanath, Venkatram, and de Pablo, Juan J.. 2019. "Electronic structure at coarse-grained resolutions from supervised machine learning". United States. doi:10.1126/sciadv.aav1190. https://www.osti.gov/servlets/purl/1506239.
@article{osti_1506239,
title = {Electronic structure at coarse-grained resolutions from supervised machine learning},
author = {Jackson, Nicholas E. and Bowen, Alec S. and Antony, Lucas W. and Webb, Michael A. and Vishwanath, Venkatram and de Pablo, Juan J.},
abstractNote = {Computational studies aimed at understanding conformationally dependent electronic structure in soft materials require a combination of classical and quantum-mechanical simulations, for which the sampling of conformational space can be particularly demanding. Coarse-grained (CG) models provide a means of accessing relevant time scales, but CG configurations must be back-mapped into atomistic representations to perform quantum-chemical calculations, which is computationally intensive and inconsistent with the spatial resolution of the CG models. A machine learning approach, denoted as artificial neural network electronic coarse graining (ANN-ECG), is presented here in which the conformationally dependent electronic structure of a molecule is mapped directly to CG pseudo-atom configurations. By averaging over decimated degrees of freedom, ANN-ECG accelerates simulations by eliminating backmapping and repeated quantum-chemical calculations. The approach is accurate, consistent with the CG spatial resolution, and can be used to identify computationally optimal CG resolutions.},
doi = {10.1126/sciadv.aav1190},
journal = {Science Advances},
number = 3,
volume = 5,
place = {United States},
year = {2019},
month = {3}
}

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