Machine learning Frenkel Hamiltonian parameters to accelerate simulations of exciton dynamics-Reference-Cited by-同舟云学术

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Machine learning Frenkel Hamiltonian parameters to accelerate simulations of exciton dynamics

Published:2020-08-21 Issue:7 Volume:153 Page:074111
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Farahvash Ardavan¹^ORCID,Lee Chee-Kong²^ORCID,Sun Qiming²^ORCID,Shi Liang³^ORCID,Willard Adam P.¹^ORCID

Affiliation:

1. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

2. Tencent America, Palo Alto, California 94306, USA

3. Department of Chemistry and Chemical Biology, University of California, Merced, California 95343, USA

Funder

U.S. Department of Energy

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/am-pdf/10.1063/5.0016009

Reference71 articles.

1. Organic photovoltaics

2. Organic photovoltaics: technology and market

3. White light emission from blends of blue-emitting organic molecules: A general route to the white organic light-emitting diode?

4. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

5. On the design of molecular excitonic circuits for quantum computing: the universal quantum gates

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