Cited by 1,060Organic Light-Emitting Diodes ResearchMachine Learning in Materials ScienceLuminescence and Fluorescent Materials

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

Gómez-Bombarelli, Rafael, Aguilera-Iparraguirre, Jorge, Hirzel, Timothy D., Duvenaud, David, Maclaurin, Dougal, Blood-Forsythe, Martin A., Chae, Hyun Sik, Einzinger, Markus, Ha, Dong-Gwang, Wu, Tony, Markopoulos, Georgios, Jeon, Soonok, Kang, Hosuk, Miyazaki, Hiroshi, Numata, Masaki, Kim, Sunghan, Huang, Wenliang, Hong, Seong Ik, Baldo, Marc, Adams, Ryan P., Aspuru-Guzik, Alán·2016·Nature Materials

DOI 10.1038/nmat4717

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Abstract

Virtual screening is becoming a ground-breaking tool for molecular discovery due to the exponential growth of available computer time and constant improvement of simulation and machine learning techniques. We report an integrated organic functional material design process that incorporates theoretical insight, quantum chemistry, cheminformatics, machine learning, industrial expertise, organic synthesis, molecular characterization, device fabrication and optoelectronic testing. After exploring a search space of 1.6 million molecules and screening over 400,000 of them using time-dependent density functional theory, we identified thousands of promising novel organic light-emitting diode molecules across the visible spectrum. Our team collaboratively selected the best candidates from this set. The experimentally determined external quantum efficiencies for these synthesized candidates were as large as 22%.

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Authors: Gómez-Bombarelli, R., Aguilera-Iparraguirre, J., Hirzel, T. D., Duvenaud, D., Maclaurin, D., Blood-Forsythe, M. A., Chae, H. S., Einzinger, M., Ha, D.-G., Wu, T., Markopoulos, G., Jeon, S., Kang, H., Miyazaki, H., Numata, M., Kim, S., Huang, W., Hong, S. I., Baldo, M., Adams, R. P., & Aspuru-Guzik, A.
Affiliations: Harvard University (US)
Massachusetts Institute of Technology (US)
Samsung (South Korea) (KR)
ORCID: Gómez-Bombarelli, Rafael
Maclaurin, Dougal
Blood-Forsythe, Martin A.
Einzinger, Markus
Wu, Tony
Miyazaki, Hiroshi
Kim, Sunghan
Huang, Wenliang
Baldo, Marc
Adams, Ryan P.
Aspuru-Guzik, Alán
Venue: Nature Materials
Publisher: Springer Science and Business Media LLC
Published: 2016-08-08
Volume: 15
Issue: 10
Pages: 1120-1127
ISSN: 1476-1122 (ISSN-L)
1476-4660
DOI: 10.1038/nmat4717
OpenAlex: W2478294658
Cited by: 1,060
References: 50
Citations by year: 2026: 80 · 2025: 104 · 2024: 116 · 2023: 127 · 2022: 109 · 2021: 147 · 2020: 118 · 2019: 105 · 2018: 90 · 2017: 54 · 2016: 9
FWCI: 39.62
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Materials (publishedVersion)
PubMed (publishedVersion)
Funders: U.S. Department of Energy (DEFG0207ER46474)
Samsung Advanced Institute of Technology
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Organic Light-Emitting Diodes Research
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Materials Science › Materials Chemistry › Luminescence and Fluorescent Materials
Keywords: organic light-emitting diodes, virtual screening, high-throughput screening, thermally activated delayed fluorescence, organic emitters, quantum chemistry, machine learning, external quantum efficiency, time-dependent density functional theory, cheminformatics

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

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