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Design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach

Recent advances in machine learning have allowed us to quantify the parameters that are important for the fabrication of high efficient phosphorescent bottom emitting organic light emitting diodes (PhOLEDs). Herein, we have collected 304 blue PhOLED data from the literature along with their frontier...

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Main Authors:	Janai, Muhammad Asyraf, Woon, Kai Lin, Chan, Chee Seng
Format:	Article
Published:	Elsevier 2018
Subjects:	QA75 Electronic computers. Computer science QC Physics
Online Access:	http://eprints.um.edu.my/21755/ https://doi.org/10.1016/j.orgel.2018.09.029
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spelling	my.um.eprints.217552019-08-05T02:58:35Z http://eprints.um.edu.my/21755/ Design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach Janai, Muhammad Asyraf Woon, Kai Lin Chan, Chee Seng QA75 Electronic computers. Computer science QC Physics Recent advances in machine learning have allowed us to quantify the parameters that are important for the fabrication of high efficient phosphorescent bottom emitting organic light emitting diodes (PhOLEDs). Herein, we have collected 304 blue PhOLED data from the literature along with their frontier molecular orbital energy levels, triplet energies, efficiencies, device structures and layer thicknesses. Using these descriptors as the inputs and efficiency as the output, we showed that the random forest algorithm (a machine learning approach) provides significant improved predictive performance over linear regression analysis and other multivariate regression models such as extreme gradient boosting, adaptive boosting, gradient boosting and k-nearest neighbor. The triplet energy of the electron transporting layer was found to be the more critical feature. Complex correlations between various parameters on device efficiency generated by the random forest model are also presented. This study demonstrates the applicability of machine learning algorithm in extracting underlying complex correlations in blue PhOLEDs. Elsevier 2018 Article PeerReviewed Janai, Muhammad Asyraf and Woon, Kai Lin and Chan, Chee Seng (2018) Design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach. Organic Electronics, 63. pp. 257-266. ISSN 1566-1199 https://doi.org/10.1016/j.orgel.2018.09.029 doi:10.1016/j.orgel.2018.09.029
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	QA75 Electronic computers. Computer science QC Physics
spellingShingle	QA75 Electronic computers. Computer science QC Physics Janai, Muhammad Asyraf Woon, Kai Lin Chan, Chee Seng Design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach
description	Recent advances in machine learning have allowed us to quantify the parameters that are important for the fabrication of high efficient phosphorescent bottom emitting organic light emitting diodes (PhOLEDs). Herein, we have collected 304 blue PhOLED data from the literature along with their frontier molecular orbital energy levels, triplet energies, efficiencies, device structures and layer thicknesses. Using these descriptors as the inputs and efficiency as the output, we showed that the random forest algorithm (a machine learning approach) provides significant improved predictive performance over linear regression analysis and other multivariate regression models such as extreme gradient boosting, adaptive boosting, gradient boosting and k-nearest neighbor. The triplet energy of the electron transporting layer was found to be the more critical feature. Complex correlations between various parameters on device efficiency generated by the random forest model are also presented. This study demonstrates the applicability of machine learning algorithm in extracting underlying complex correlations in blue PhOLEDs.
format	Article
author	Janai, Muhammad Asyraf Woon, Kai Lin Chan, Chee Seng
author_facet	Janai, Muhammad Asyraf Woon, Kai Lin Chan, Chee Seng
author_sort	Janai, Muhammad Asyraf
title	Design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach
title_short	Design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach
title_full	Design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach
title_fullStr	Design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach
title_full_unstemmed	Design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach
title_sort	design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach
publisher	Elsevier
publishDate	2018
url	http://eprints.um.edu.my/21755/ https://doi.org/10.1016/j.orgel.2018.09.029
_version_	1643691651524198400
score	13.211869

Design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach

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