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

Nature Materials volume 15, pages 1120–1127 (2016)Cite this article

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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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Figure 1: Discovery pipeline.
Figure 2: Experiment–theory calibration.
Figure 3: Effectiveness of machine learning.
Figure 4: Candidate statistics and voting tool.
Figure 5: Lead candidates and optoelectronic characterization.

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Acknowledgements

The authors acknowledge Samsung Advanced Institute of Technology for funding and Lumtec Inc. for custom synthesis of candidate materials. The assistance of Samsung for synthesis and characterization of lead compounds is acknowledged. M.A.B.-F. acknowledges support from the DOE Office of Science Graduate Fellowship. M.E. and T.W. were supported by the US Department of Energy, Office of Basic Energy Sciences (Award No. DE-FG02-07ER46474). G.M. thanks the German Academic Exchange Service (DAAD) for a postdoctoral fellowship. The authors acknowledge the use of the Harvard FAS Odyssey Cluster and support from FAS Research Computing.

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Authors and Affiliations

  1. Department of Chemistry and Chemical Biology, 12 Oxford Street, Harvard University, Cambridge, Massachusetts 02138, USA

    Rafael Gómez-Bombarelli, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Martin A. Blood-Forsythe & Alán Aspuru-Guzik

  2. John A. Paulson School of Engineering and Applied Sciences, 33 Oxford Street, Harvard University, Cambridge, Massachusetts 02138, USA

    David Duvenaud, Dougal Maclaurin & Ryan P. Adams

  3. Samsung Research America, 255 Main Street, Suite 702, Cambridge, Massachusetts 02142, USA

    Hyun Sik Chae & Seong Ik Hong

  4. Department of Electrical Engineering and Computer Science, 77 Massachusetts Avenue, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

    Markus Einzinger, Tony Wu & Marc Baldo

  5. Department of Materials Science and Engineering, 77 Massachusetts Avenue, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

    Dong-Gwang Ha

  6. Department of Chemistry, 77 Massachusetts Avenue, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

    Georgios Markopoulos & Wenliang Huang

  7. Samsung Advanced Institute of Technology, Samsung Electronics Co., Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Korea

    Soonok Jeon, Hosuk Kang, Hiroshi Miyazaki, Masaki Numata & Sunghan Kim

Authors
  1. Rafael Gómez-Bombarelli
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Contributions

A.A.-G., M.B. and R.P.A. conceived the project. T.D.H. designed and wrote the custom computer code for molecular screening, with contributions from R.G.-B. and J.A.-I. R.G.-B. and J.A.-I. designed the molecules, with contributions from A.A.-G., H.M., M.N. and H.S.C. R.G.-B. and J.A.-I. performed calculations and analysed theoretical predictions. M.A.B.-F. carried out the experimental calibration of the theoretical methods. D.M., D.D. and R.P.A. applied machine learning to the computational predictions. H.S.C. and G.M. assessed synthetic feasibility of molecular candidates, with contributions from W.H., S.J., H.M., M.N. and S.K. R.G.-B., J.A.-I., T.D.H., H.S.C., M.A.B.-F., G.M., D.M., D.D., S.H., S.J., H.M., M.N., S.K., R.P.A., M.B. and A.A.-G. selected the molecules for characterization. S.J. synthesized J1-2 and L1. S.J., H.S.C., T.W., D.-G.H. and M.E. collected and analysed spectroscopic data. D.-G.H., M.E. and T.W. manufactured and tested devices for F1, J1, J2 and L1, with contributions from H.K. R.G.-B., J.A.-I. and T.D.H. wrote the first version of the manuscript. All authors contributed to the discussion, writing and editing of the manuscript. A.A.-G. and R.P.A. supervised the computational chemistry study. R.P.A. and A.A.-G. supervised the machine learning approach. M.B. supervised the device fabrication.

Corresponding author

Correspondence to Alán Aspuru-Guzik.

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The authors declare no competing financial interests.

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Gómez-Bombarelli, R., Aguilera-Iparraguirre, J., Hirzel, T. et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nature Mater 15, 1120–1127 (2016). https://doi.org/10.1038/nmat4717

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