Electronic structure at coarse-grained resolutions from supervised machine learning
Abstract
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:
-
[1];
[2];
- Argonne National Lab. (ANL), Lemont, IL (United States); Univ. of Chicago, Chicago, IL (United States)
- Univ. of Chicago, Chicago, IL (United States)
- Argonne National Lab. (ANL), Lemont, IL (United States)
- Publication Date:
- Research Org.:
- Argonne National Laboratory (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)
- OSTI Identifier:
- 1506239
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Science Advances
- Additional Journal Information:
- Journal Volume: 5; Journal Issue: 3; Journal ID: ISSN 2375-2548
- Publisher:
- AAAS
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 36 MATERIALS SCIENCE; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
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., 2019.
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. https://doi.org/10.1126/sciadv.aav1190
Jackson, Nicholas E., Bowen, Alec S., Antony, Lucas W., Webb, Michael A., Vishwanath, Venkatram, and de Pablo, Juan J. Fri .
"Electronic structure at coarse-grained resolutions from supervised machine learning". United States. https://doi.org/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 = {Fri Mar 22 00:00:00 EDT 2019},
month = {Fri Mar 22 00:00:00 EDT 2019}
}
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Works referencing / citing this record:
Machine learning predictions of electronic couplings for charge transport calculations of P3HT
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