Understanding the charge dynamics in organic light-emitting diodes using convolutional neural network-Reference-Cited by-同舟云学术

Understanding the charge dynamics in organic light-emitting diodes using convolutional neural network

Published:2022 Issue:10 Volume:9 Page:2551-2563
ISSN:2051-6347
Container-title:Materials Horizons
language:en
Short-container-title:Mater. Horiz.

Author:

Kim Jae-Min¹^ORCID,Lim Junseop¹,Lee Jun Yeob¹^ORCID

Affiliation:

1. School of Chemical Engineering, Sungkyunkwan University, Suwon Campus, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of Korea

Abstract

A fast analysis of the charge dynamics of organic light-emitting diodes using a convolutional neural network (CNN) was demonstrated. The electrical properties of organic layers were predicted by the CNN model with 2D modulus spectra as a fingerprint.

Funder

National Research Foundation of Korea

Publisher

Royal Society of Chemistry (RSC)

Subject

Electrical and Electronic Engineering,Process Chemistry and Technology,Mechanics of Materials,General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2022/MH/D2MH00373B

Reference74 articles.

1. Organic electroluminescent diodes

2. Toward Ultra‐Efficient OLEDs: Approaches Based on Low Refractive Index Materials

3. Novel optoelectronic technique for direct tracking of ultrafast triplet excitons in polymeric semiconductor