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Nat Mater. 2016 Oct;15(10):1120-7. doi: 10.1038/nmat4717. Epub 2016 Aug 8.

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

Author information

1
Department of Chemistry and Chemical Biology, 12 Oxford Street, Harvard University, Cambridge, Massachusetts 02138, USA.
2
John A. Paulson School of Engineering and Applied Sciences, 33 Oxford Street, Harvard University, Cambridge, Massachusetts 02138, USA.
3
Samsung Research America, 255 Main Street, Suite 702, Cambridge, Massachusetts 02142, USA.
4
Department of Electrical Engineering and Computer Science, 77 Massachusetts Avenue, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
5
Department of Materials Science and Engineering, 77 Massachusetts Avenue, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
6
Department of Chemistry, 77 Massachusetts Avenue, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
7
Samsung Advanced Institute of Technology, Samsung Electronics Co., Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16678, Korea.

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%.

PMID:
27500805
DOI:
10.1038/nmat4717

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