Deep learning for development of organic optoelectronic devices: efficient prescreening of hosts and emitters in deep-blue fluorescent OLEDs

Minseok Jeong, Joonyoung F. Joung, Jinhyo Hwang, Minhi Han, Chang Woo Koh, Dong Hoon Choi, Sungnam Park

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies, which are key factors in optoelectronic devices, must be accurately estimated for newly designed materials. Here, we developed a deep learning (DL) model that was trained with an experimental database containing the HOMO and LUMO energies of 3026 organic molecules in solvents or solids and was capable of predicting the HOMO and LUMO energies of molecules with the mean absolute errors of 0.058 eV. Additionally, we demonstrated that our DL model was efficiently used to virtually screen optimal host and emitter molecules for organic light-emitting diodes (OLEDs). Deep-blue fluorescent OLEDs, which were fabricated with emitter and host molecules selected via DL prediction, exhibited narrow emission (bandwidth = 36 nm) at 412 nm and an external quantum efficiency of 6.58%. Our DL-assisted virtual screening method can be further applied to the development of component materials in optoelectronics.

Original languageEnglish
Article number147
Journalnpj Computational Materials
Volume8
Issue number1
DOIs
Publication statusPublished - 2022 Dec

Bibliographical note

Funding Information:
This study was supported by grants from the National Research Foundation of Korea (NRF) funded by the Korean government (No. 2019R1A6A1A11044070 and 2022R1A2C1003627) and LG Display.

Publisher Copyright:
© 2022, The Author(s).

ASJC Scopus subject areas

  • Modelling and Simulation
  • General Materials Science
  • Mechanics of Materials
  • Computer Science Applications

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