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Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach
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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“It may be necessary to validate the ML-interpolated and DFT-level predictions with experiment to ensure that predictions are realistic and materials are synthesizable, as was done for organic light-emitting diodes (50) or MOFs for wet flue gas capture (51).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [50]
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“One such example is the design of blue emitters for organic light-emitting diode devices accomplished by virtual screening of half a million molecules .172”
From QSAR without borders (2020) · cites this paper as [172]
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“This approach led the successful discovery of three lead candidate compounds with state-of-the-art performance,172 exemplifying the promise of closed loop discovery.”
From QSAR without borders (2020) · cites this paper as [172]
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Metadata
- 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
- Venue
- Nature Materials
- Published
- 2016-08-08
- Volume
- 15
- Issue
- 10
- Pages
- 1120-1127
- ISSN
- 1476-1122 (ISSN-L)
- 1476-4660
- 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
- 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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