Electronic structure at coarse-grained resolutions from supervised machine learning-Reference-Cited by-同舟云学术

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Electronic structure at coarse-grained resolutions from supervised machine learning

Published:2019-03 Issue:3 Volume:5 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Jackson Nicholas E.¹²^ORCID,Bowen Alec S.²,Antony Lucas W.²,Webb Michael A.²^ORCID,Vishwanath Venkatram³,de Pablo Juan J.¹²

Affiliation:

1. Institute for Molecular Engineering, Argonne National Laboratory, Lemont, IL 60439, USA.

2. Institute for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA.

3. Argonne Leadership Computing Facility, Argonne National Laboratory, Lemont, IL 60439, USA.

Abstract

Machine learning–enhanced molecular simulation opens a pathway to multiscale prediction for organic electronics.

Funder

U.S. Department of Energy

Argonne National Laboratory, Office of Science

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference48 articles.

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2. 25th Anniversary Article: Organic Field-Effect Transistors: The Path Beyond Amorphous Silicon

3. Charge Transport in Organic Semiconductors

4. Electron transfer—From isolated molecules to biomolecules;Bixon M.;Adv. Chem. Phys.,1999

5. Modeling charge transport in organic photovoltaic materials;Nelson J.;Acc. Chem. Res.,2009

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