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%.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
Cites
(2014) · Cited by 33,419
(2010) · International Conference on Machine Learning · Cited by 13,995
Extended-Connectivity Fingerprints
Rogers, D. O. W., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,925
Highly efficient phosphorescent emission from organic electroluminescent devices
Baldo, M. A., O’Brien, D. F., You, Y., Shoustikov, A., Sibley, S. P., Thompson, M. E., & Forrest, S. R. (1998) · Nature · doi:10.1038/25954 · Cited by 7,213
Self-citationPractical Bayesian Optimization of Machine Learning Algorithms
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691
Self-citationAdvances in molecular quantum chemistry contained in the Q-Chem 4 program package
Shao, Y., Gan, Z., Epifanovsky, E., Gilbert, A. T. B., Wormit, M., Kußmann, J., Lange, A. W., Behn, A., Deng, J., Feng, X., et al. (2014) · Molecular Physics · doi:10.1080/00268976.2014.952696 · Cited by 3,007
Single-Reference ab Initio Methods for the Calculation of Excited States of Large Molecules
Dreuw, A., & Head‐Gordon, M. (2005) · Chemical Reviews · doi:10.1021/cr0505627 · Cited by 2,765
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
Tao, Y., Yuan, K., Chen, T., Xu, P., Li, H., Chen, R., Zheng, C., Zhang, L., & Huang, W. (2014) · Advanced Materials · doi:10.1002/adma.201402532 · Cited by 2,011
Self-citationErtl, P., & Schuffenhauer, A. (2009) · Journal of Cheminformatics · doi:10.1186/1758-2946-1-8 · Cited by 1,939
Zhang, Q., Li, J., Shizu, K., Huang, S., Hirata, S., Miyazaki, H., & Adachi, C. (2012) · Journal of the American Chemical Society · doi:10.1021/ja306538w · Cited by 1,609
Self-citationA metal-free organic–inorganic aqueous flow battery
Huskinson, B., Marshak, M. P., Suh, C., Er, S., Gerhardt, M. R., Galvin, C. J., Chen, X., Aspuru‐Guzik, A., Gordon, R. G., & Aziz, M. J. (2014) · Nature · doi:10.1038/nature12909 · Cited by 1,578
Self-citationVirtual screening of chemical libraries
Shoichet, B. K. (2004) · Nature · doi:10.1038/nature03197 · Cited by 1,425
Yersin, H., Rausch, A. F., Czerwieniec, R., Hofbeck, T., & Fischer, T. (2011) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2011.01.042 · Cited by 1,357
Highly efficient blue electroluminescence based on thermally activated delayed fluorescence
Hirata, S., Sakai, Y., Masui, K., Tanaka, H., Lee, S. Y., Nomura, H., Nakamura, N., Yasumatsu, M., Nakanotani, H., Zhang, Q., et al. (2014) · Nature Materials · doi:10.1038/nmat4154 · Cited by 1,328
Self-citationEndo, A., Sato, K., Yoshimura, K., Kai, T., Kawada, A., Miyazaki, H., & Adachi, C. (2011) · Applied Physics Letters · doi:10.1063/1.3558906 · Cited by 1,213
Self-citationDias, F. B., Bourdakos, K. N., Jankus, V., Moss, K. C., Kamtekar, K. T., Bhalla, V., Santos, J., Bryce, M. R., & Monkman, A. P. (2013) · Advanced Materials · doi:10.1002/adma.201300753 · Cited by 1,005
Ufimtsev, I. S., & Martı́nez, T. J. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct9003004 · Cited by 924
Einstein coefficients, cross sections, f values, dipole moments, and all that
Hilborn, R. C. (1982) · American Journal of Physics · doi:10.1119/1.12937 · Cited by 846
Hachmann, J., Olivares‐Amaya, R., Atahan-Evrenk, Ş., Amador‐Bedolla, C., Sánchez‐Carrera, R. S., Gold‐Parker, A., Vogt-Maranto, L., Brockway, A. M., & Aspuru‐Guzik, A. (2011) · The Journal of Physical Chemistry Letters · doi:10.1021/jz200866s · Cited by 668
Self-citationEstimation of the size of drug-like chemical space based on GDB-17 data
Polishchuk, P. G., Madzhidov, T. I., & Varnek, A. (2013) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-013-9672-4 · Cited by 619
Approaches for fabricating high efficiency organic light emitting diodes
Jou, J., Kumar, S., Agrawal, A., Li, T., & Sahoo, S. (2015) · Journal of Materials Chemistry C · doi:10.1039/c4tc02495h · Cited by 612
Zhang, Q., Tsang, D. P., Kuwabara, H., Hatae, Y., Li, B., Takahashi, T., Lee, S. Y., Yasuda, T., & Adachi, C. (2015) · Advanced Materials · doi:10.1002/adma.201405474 · Cited by 580
Wang, H., Xie, L., Peng, Q., Meng, L., Wang, Y., Yi, Y., & Wang, P. (2014) · Advanced Materials · doi:10.1002/adma.201401393 · Cited by 544
Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches
Geppert, H., & Bajorath, J. (2007) · Drug Discovery Today · doi:10.1016/j.drudis.2007.01.011 · Cited by 470
Computational design of molecules for an all-quinone redox flow battery
Er, S., Suh, C., Marshak, M. P., & Aspuru‐Guzik, A. (2014) · Chemical Science · doi:10.1039/c4sc03030c · Cited by 431
Self-citationCarrete, J., Li, W., Mingo, N., Wang, S., & Stefano Curtarolo, A. (2014) · Physical Review X · doi:10.5281/zenodo.10562670 · Cited by 385
Carrete, J., Li, W., Mingo, N., et al. (2014) · Physical Review X · doi:10.5281/zenodo.10562671 · Cited by 381
Multi-task Neural Networks for QSAR PredictionsPreprint
Dahl, G. E., Jaitly, N., & Salakhutdinov, R. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.1231 · Cited by 352
Gritsenko, O. V., & Baerends, E. J. (2004) · The Journal of Chemical Physics · doi:10.1063/1.1759320 · Cited by 347
Computational Prediction for Singlet- and Triplet-Transition Energies of Charge-Transfer Compounds
Huang, S., Zhang, Q., Shiota, Y., Nakagawa, T., Kuwabara, K., Yoshizawa, K., & Adachi, C. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400415r · Cited by 323
Jankus, V., Data, P., Graves, D., McGuinness, C., Santos, J., Bryce, M. R., Dias, F. B., & Monkman, A. P. (2014) · Advanced Functional Materials · doi:10.1002/adfm.201400948 · Cited by 317
What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery
Pyzer‐Knapp, E. O., Suh, C., Gómez‐Bombarelli, R., Aguilera‐Iparraguirre, J., & Aspuru‐Guzik, A. (2015) · Annual Review of Materials Research · doi:10.1146/annurev-matsci-070214-020823 · Cited by 307
Self-citationBringing the MMFF force field to the RDKit: implementation and validation
Tosco, P., Stiefl, N., & Landrum, G. A. (2014) · Journal of Cheminformatics · doi:10.1186/s13321-014-0037-3 · Cited by 275
A search model for topological insulators with high-throughput robustness descriptors
Yang, K., Setyawan, W., Wang, S., Nardelli, M. B., & Stefano Curtarolo, A. (2012) · Nature Materials · doi:10.1038/nmat3332 · Cited by 274
Hachmann, J., Olivares‐Amaya, R., Jinich, A., Appleton, A. L., Blood-Forsythe, M. A., Seress, L. R., Román-Salgado, C., Trepte, K., Atahan-Evrenk, Ş., Er, S., et al. (2013) · Energy & Environmental Science · doi:10.1039/c3ee42756k · Cited by 246
Self-citationOn Predicting the Excited-State Properties of Thermally Activated Delayed Fluorescence Emitters
Penfold, T. J. (2015) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.5b03530 · Cited by 193
Strategy for Designing Electron Donors for Thermally Activated Delayed Fluorescence Emitters
Shizu, K., Tanaka, H., Uejima, M., Sato, T., Tanaka, K., Kaji, H., & Adachi, C. (2014) · The Journal of Physical Chemistry C · doi:10.1021/jp511061t · Cited by 168
Lee, J.-Y., Shizu, K., Tanaka, H., Nakanotani, H., Yasuda, T., Kaji, H., & Adachi, C. (2015) · Journal of Materials Chemistry C · doi:10.1039/c4tc02530j · Cited by 168
Moral, M., Muccioli, L., Son, W., Olivier, Y., & Sancho-Garcı́a, J. C. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500957s · Cited by 144
Baleizão, C., & Berberan-Santos, M. N. (2007) · The Journal of Chemical Physics · doi:10.1063/1.2734974 · Cited by 117
Lee, D. R., Hwang, S., Jeon, S. K., Lee, C. W., & Lee, J. Y. (2015) · Chemical Communications · doi:10.1039/c5cc01940k · Cited by 98
Self-citationSimulated evolution of fluorophores for light emitting diodes
Shu, Y., & Levine, B. G. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4914294 · Cited by 78
Transition Metal and Rare Earth Compounds
Yersin, H. (2004) · Topics in current chemistry · doi:10.1007/b83770 · Cited by 68
Sagara, Y., Shizu, K., Tanaka, H., Miyazaki, H., Goushi, K., Kaji, H., & Adachi, C. (2014) · Chemistry Letters · doi:10.1246/cl.141054 · Cited by 61
Self-citationTanaka, H., Shizu, K., Lee, J., & Adachi, C. (2015) · The Journal of Physical Chemistry C · doi:10.1021/jp510751n · Cited by 58
Transition Metal and Rare Earth Compounds
Bray, K. L., Yersin, H., Bray, K. L., & Donges, D. (2000) · Topics in current chemistry · doi:10.1007/3-540-44447-5 · Cited by 35
Combinatorial Design of Copolymer Donor Materials for Bulk Heterojunction Solar Cells
Shin, Y., Liu, J., Quigley, J. J., Luo, H., & Lin, X. (2014) · ACS Nano · doi:10.1021/nn5014066 · Cited by 25
Zhang, X., Shen, W., Zhang, D., Zheng, Y.-Z., He, R., & Li, M. (2015) · RSC Advances · doi:10.1039/c5ra04929f · Cited by 19
Modeling Choices for Virtual Screening Hit Identification
Bergeron, C., Krein, M. P., Moore, G., Breneman, C. M., & Bennett, K. P. (2011) · Molecular Informatics · doi:10.1002/minf.201100092 · Cited by 4
Cited by
Sorted by citations
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
Self-citation
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
