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

Nature Materials

Volumn 15, Issue 10, 2016, Pages 1120-1127

  Gómez Bombarelli, Rafael ^a   Aguilera Iparraguirre, Jorge ^a   Hirzel, Timothy D ^a   Duvenaud, David ^b   Maclaurin, Dougal ^b   Blood Forsythe, Martin A ^a   Chae, Hyun Sik ^c   Einzinger, Markus ^d   Ha, Dong Gwang ^e   Wu, Tony ^d   Markopoulos, Georgios ^f   Jeon, Soonok ^g   Kang, Hosuk ^g   Miyazaki, Hiroshi ^g   Numata, Masaki ^g   Kim, Sunghan ^g   Huang, Wenliang ^f   Hong, Seong Ik ^c   Baldo, Marc ^d   Adams, Ryan P ^b   Aspuru Guzik, Alán ^a   more..

^a HARVARD UNIVERSITY   (United States)

^b HARVARD UNIVERSITY   (United States)

^c SAMSUNG RESEARCH AMERICA   (United States)

^d Department of Electrical Engineering and Computer Science   (United States)

^e USA   (United States)

^f MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^g SAMSUNG Electronics   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DENSITY FUNCTIONAL THEORY; ELECTRONIC EQUIPMENT; FUNCTIONAL MATERIALS; LEARNING SYSTEMS; LIGHT EMITTING DIODES; MOLECULES; QUANTUM CHEMISTRY; QUANTUM EFFICIENCY;

CONSTANT IMPROVEMENT; EXPERIMENTAL APPROACHES; EXTERNAL QUANTUM EFFICIENCY; MACHINE LEARNING TECHNIQUES; MOLECULAR CHARACTERIZATION; MOLECULAR ORGANIC LIGHT-EMITTING DIODES; ORGANIC FUNCTIONAL MATERIALS; TIME DEPENDENT DENSITY FUNCTIONAL THEORY;

ORGANIC LIGHT EMITTING DIODES (OLED);

EID: 84981173474     PISSN: 14761122     EISSN: 14764660     Source Type: Journal    
DOI: 10.1038/nmat4717     Document Type: Article

References (51)

1. Polishchuk, P. G., Madzhidov, T. I. & Varnek, A. Estimation of the size of drug-like chemical space based on GDB-17 data. J. Comput. Aided Mol. Des. 27, 675-679 (2013).
2. Shoichet, B. K. Virtual screening of chemical libraries. Nature 432, 862-865 (2004).
3. Curtarolo, S. et al. The high-throughput highway to computational materials design. Nature Mater. 12, 191-201 (2013).
4. Pyzer-Knapp, E. O., Suh, C., Gómez-Bombarelli, R., Aguilera-Iparraguirre, J. & Aspuru-Guzik, A.What is high-throughput virtual screening? A perspective from organic materials discovery. Annu. Rev. Mater. Res. 45, 195-216 (2015).
5. Yang, K., Setyawan,W.,Wang, S., Buongiorno Nardelli, M. & Curtarolo, S. A search model for topological insulators with high-throughput robustness descriptors. Nature Mater. 11, 614-619 (2012).
6. Carrete, J., Li,W., Mingo, N.,Wang, S. & Curtarolo, S. Finding unprecedentedly low-thermal-conductivity half-Heusler semiconductors via high-throughput materials modeling. Phys. Rev. X 4, 011019 (2014).
7. Huskinson, B. et al. A metal-free organic-inorganic aqueous flow battery. Nature 505, 195-198 (2014).
8. Er, S., Suh, C., Marshak, M. P. & Aspuru-Guzik, A. Computational design of molecules for an all-quinone redox flow battery. Chem. Sci. 6, 885-893 (2015).
9. Hachmann, J. et al. The Harvard Clean Energy Project: large-scale computational screening and design of organic photovoltaics on the world community grid. J. Phys. Chem. Lett. 2, 2241-2251 (2011).
10. Shin, Y., Liu, J., Quigley, J. J., Luo, H. & Lin, X. Combinatorial design of copolymer donor materials for bulk heterojunction solar cells. ACS Nano 8, 6089-6096 (2014).
11. Hachmann, J. et al. Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry the Harvard Clean Energy Project. Energy Environ. Sci. 7, 698-704 (2014).
12. Baldo, M. A. et al. Highly efficient phosphorescent emission from organic electroluminescent devices. Nature 395, 151-154 (1998).
13. Yersin, H. Transition Metal and Rare Earth Compounds 1-26 (Springer, 2004).
14. Jou, J.-H., Kumar, S., Agrawal, A., Li, T.-H. & Sahoo, S. Approaches for fabricating high efficiency organic light emitting diodes. J. Mater. Chem. C 3, 2974-3002 (2015).
15. Tao, Y. et al. Thermally activated delayed fluorescence materials towards the breakthrough of organoelectronics. Adv. Mater. 26, 7931-7958 (2014).
16. Endo, A. et al. Efficient up-conversion of triplet excitons into a singlet state and its application for organic light emitting diodes. Appl. Phys. Lett. 98, 083302 (2011).
17. Zhang, Q. et al. Design of efficient thermally activated delayed fluorescence materials for pure blue organic light emitting diodes. J. Am. Chem. Soc. 134, 14706-14709 (2012).
18. Zhang, Q. et al. Efficient blue organic light-emitting diodes employing thermally activated delayed fluorescence. Nature Photon. 8, 326-332 (2014).
19. Yersin, H., Rausch, A. F., Czerwieniec, R., Hofbeck, T. & Fischer, T. The triplet state of organo-transition metal compounds. Triplet harvesting and singlet harvesting for efficient OLEDs. Coord. Chem. Rev. 255, 2622-2652 (2011).
20. Dias, F. B. et al. Triplet harvesting with 100% efficiency by way of thermally activated delayed fluorescence in charge transfer OLED emitters. Adv. Mater. 25, 3707-3714 (2013).
21. Jankus, V. et al. Highly efficient TADF OLEDs: How the emitter-host interaction controls both the excited state species and electrical properties of the devices to achieve near 100% triplet harvesting and high efficiency. Adv. Funct. Mater. 24, 6178-6186 (2014).
22. Zhang, Q. et al. Nearly 100% internal quantum efficiency in undoped electroluminescent devices employing pure organic emitters. Adv. Mater. 27, 2096-2100 (2015).
23. Tanaka, H., Shizu, K., Lee, J. & Adachi, C. Effect of atom substitution in chalcogenodiazole-containing thermally activated delayed fluorescence emitters on radiationless transition. J. Phys. Chem. C 119, 2948-2955 (2015).
24. Lee, J. et al. Controlled emission colors and singlet-triplet energy gaps of dihydrophenazine-based thermally activated delayed fluorescence emitters. J. Mater. Chem. C 3, 2175-2181 (2015).
25. Wang, H. et al. Novel thermally activated delayed fluorescence materials-thioxanthone derivatives and their applications for highly efficient OLEDs. Adv. Mater. 26, 5198-5204 (2014).
26. Lee, D. R., Hwang, S.-H., Jeon, S. K., Lee, C.W. & Lee, J. Y. Benzofurocarbazole and benzothienocarbazole as donors for improved quantum efficiency in blue thermally activated delayed fluorescent devices. Chem. Commun. 51, 8105-8107 (2015).
27. Shizu, K. et al. Strategy for designing electron donors for thermally activated delayed fluorescence emitters. J. Phys. Chem. C 119, 1291-1297 (2015).
28. Sagara, Y. et al. Highly efficient thermally activated delayed fluorescence emitters with a small singlet-triplet energy gap and large oscillator strength. Chem. Lett. 44, 360-362 (2015).
29. Shu, Y. & Levine, B. G. Simulated evolution of fluorophores for light emitting diodes. J. Chem. Phys. 142, 104104 (2015).
30. Zhang, X. et al. Theoretical investigation of dihydroacridine and diphenylsulphone derivatives as thermally activated delayed fluorescence emitters for organic light-emitting diodes. RSC Adv. 5, 51586-51591 (2015).
31. RDKit: open source cheminformatics software http://www.rdkit.org (accessed 23 June 2015).
32. Ertl, P. & Schuffenhauer, A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminform. 1, 8 (2009).
33. Baleizão, C. & Berberan-Santos, M. N. Thermally activated delayed fluorescence as a cycling process between excited singlet and triplet states: application to the fullerenes. J. Chem. Phys. 126, 204510 (2007).
34. Hilborn, R. C. Einstein coefficients, cross sections, f values, dipole moments, and all that. Am. J. Phys. 50, 982-986 (1982).
35. Dreuw, A. & Head-Gordon, M. Single-reference ab initio methods for the calculation of excited states of large molecules. Chem. Rev. 105, 4009-4037 (2005).
36. Gritsenko, O. & Baerends, E. J. Asymptotic correction of the exchange-correlation kernel of time-dependent density functional theory for long-range charge-transfer excitations. J. Chem. Phys. 121, 655-660 (2004).
37. Huang, S. et al. Computational prediction for singlet-And triplet-transition energies of charge-transfer compounds. J. Chem. Theory Comput. 9, 3872-3877 (2013).
38. Penfold, T. J. On predicting the excited-state properties of thermally activated delayed fluorescence emitters. J. Phys. Chem. C 119, 13535-13544 (2015).
39. Moral, M.,Muccioli, L., Son,W-J., Olivier, Y. & Sancho-García, J. C. Theoretical rationalization of the singlet-triplet gap in OLEDs materials: impact of charge-transfer character. J. Chem. Theory Comput. 11, 168-177 (2015).
40. Bergeron, C., Krein, M.,Moore, G., Breneman, C. M. & Bennett, K. P.Modeling choices for virtual screening hit identification.Mol. Inform. 30, 765-777 (2011).
41. Eckert, H. & Bajorath, J. Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches. Drug Discov. Today 12, 225-233 (2007).
42. Dahl, G. E., Jaitly, N. & Salakhutdinov, R. Multi-task neural networks for QSAR predictions. Preprint at http://arXiv.org/abs/1406.1231 (2014).
43. Rogers, D. & Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model. 50, 742-754 (2010).
44. Nair, V. & Hinton, G. E. Rectified linear units improve restricted Boltzmann machines. Proc. 27th International Conference on Machine Learning (ICML-10) 807-814 (2010).
45. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929-1958 (2014).
46. Snoek, J., Larochelle, H. & Adams, R. P. Practical Bayesian optimization of machine learning algorithms. Adv. Neural Inf. Process. Syst. 25, 2951-2959 (2012).
47. Maclaurin, D., Duvenaud, D. & Johnson, M. J. HIPS/autograd GitHub https://github.com/HIPS/autograd (accessed 29 October 2015).
48. Hirata, S. et al. Highly efficient blue electroluminescence based on thermally activated delayed fluorescence. Nature Mater. 14, 330-336 (2015).
49. Tosco, P., Stiefl, N. & Landrum, G. Bringing the MMFF force field to the RDKit: implementation and validation. J. Cheminformat. 6, 37 (2014).
50. Shao, Y. et al. Advances in molecular quantum chemistry contained in the Q-Chem 4 program package. Mol. Phys. 113, 184-215 (2015).
51. Ufimtsev, I. S. & Martinez, T. J. Quantum chemistry on graphical processing units. 3. Analytical energy gradients, geometry optimization, and first principles molecular dynamics. J. Chem. Theory Comput. 5, 2619-2628 (2009).