The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
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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In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Citations
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Extended-Connectivity Fingerprints
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Highly efficient phosphorescent emission from organic electroluminescent devices
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Self-citationPractical Bayesian Optimization of Machine Learning Algorithms
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Self-citationAdvances in molecular quantum chemistry contained in the Q-Chem 4 program package
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Single-Reference ab Initio Methods for the Calculation of Excited States of Large Molecules
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Efficient blue organic light-emitting diodes employing thermally activated delayed fluorescence
Zhang, Q., Li, B., Huang, S., Nomura, H., Tanaka, H., & Adachi, C. (2014) · Nature Photonics · doi:10.1038/nphoton.2014.12 · Cited by 2,443
The high-throughput highway to computational materials design
Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013) · Nature Materials · doi:10.1038/nmat3568 · Cited by 2,053
Thermally Activated Delayed Fluorescence Materials Towards the Breakthrough of Organoelectronics
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Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106
Self-citationInverse molecular design using machine learning: Generative models for matter engineering
Sánchez-Lengeling, B., & Aspuru‐Guzik, A. (2018) · Science · doi:10.1126/science.aat2663 · Cited by 2,177
Self-citationScientific discovery in the age of artificial intelligence.
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.
Gómez‐Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera‐Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru‐Guzik, A. (2018) · Cambridge University Engineering Department Publications Database · doi:10.17863/cam.34134 · Cited by 1,813
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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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