DOI: 10.1039/c7sc03542j. Machine learning for quantum dynamics: deep learning of excitation energy transfer properties

Machine learning for quantum dynamics: deep learning of excitation energy transfer properties

10.1039/c7sc03542j

Crossref journal-article

Royal Society of Chemistry (RSC)

Chemical Science (292)

Abstract

Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics.

Bibliography

HÃ¤se, F., Kreisbeck, C., & Aspuru-Guzik, A. (2017). Machine learning for quantum dynamics: deep learning of excitation energy transfer properties. Chemical Science, 8(12), 8419â8426.

Authors 3

Florian Häse (first)
Christoph Kreisbeck (additional)
Alán Aspuru-Guzik (additional)

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Dates

Type	When
Created	7 years, 10 months ago (Oct. 23, 2017, 4:37 p.m.)
Deposited	1 year, 4 months ago (April 17, 2024, 3:59 p.m.)
Indexed	6 days, 8 hours ago (Aug. 23, 2025, 1:15 a.m.)
Issued	8 years, 7 months ago (Jan. 1, 2017)
Published	8 years, 7 months ago (Jan. 1, 2017)
Published Online	8 years, 7 months ago (Jan. 1, 2017)

Funders 2

National Science Foundation
Awards1
1. CHE-1464862
U.S. Department of Energy 10.13039/100000015
Region: Americas
gov (National government)
Labels8
1. Energy Department
2. Department of Energy
3. United States Department of Energy
4. ENERGY.GOV
5. US Department of Energy
6. USDOE
7. DOE
8. USADOE
Awards1
1. DE-SC0001088

BibTeX

@article{H_se_2017, title={Machine learning for quantum dynamics: deep learning of excitation energy transfer properties}, volume={8}, ISSN={2041-6539}, url={http://dx.doi.org/10.1039/c7sc03542j}, DOI={10.1039/c7sc03542j}, number={12}, journal={Chemical Science}, publisher={Royal Society of Chemistry (RSC)}, author={Häse, Florian and Kreisbeck, Christoph and Aspuru-Guzik, Alán}, year={2017}, pages={8419–8426} }

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