Machine Learning Correlating Photovoltaics and Electroluminescence of Quantum Dot Light-Emitting Diodes

Min Yang; Hui Bao; Xiangmin Hu; Shipei Sun; Menglin Li; Yiran Yan; Wenjun Hou; Weiran Cao; Hang Liu; Shuangpeng Wang; Haizheng Zhong

doi:10.1021/acsphotonics.4c00413

Machine Learning Correlating Photovoltaics and Electroluminescence of Quantum Dot Light-Emitting Diodes

Min Yang, Hui Bao, Xiangmin Hu^*, Shipei Sun, Menglin Li, Yiran Yan, Wenjun Hou, Weiran Cao, Hang Liu, Shuangpeng Wang, Haizheng Zhong^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

Thus far, no reports have been made on the correlation between photovoltaics and electroluminescence in light-emitting diodes. With machine learning assistance, we here illustrate the relationship between photovoltaics and electroluminescence of quantum dot light-emitting diodes (QLEDs) by analyzing the measurements of over 200 devices, including J-V-L, photovoltaics, and time-resolved electroluminescence (TREL) test. By applying a decision tree analysis of 17 extracted features of photovoltaics test and TREL curves, we clarify the key features of open-circuit voltage (V_oc) and short-circuit current (I_sc) under varied illuminated light intensities that correlate with maximum external quantum efficiency (EQE_max) of QLED devices. These photovoltaic features are discussed from the perspective of carrier injection and recombination. In addition, the exciton formation rate (r) derived from TREL curves also affects the EQE_max. The machine learning assisted methodology is also able to predict EQE_max of the QLED with a coefficient of determination of 0.78 with an artificial neural network model.

Original language	English
Pages (from-to)	2131-2137
Number of pages	7
Journal	ACS Photonics
Volume	11
Issue number	5
DOIs	https://doi.org/10.1021/acsphotonics.4c00413
Publication status	Published - 15 May 2024
Externally published	Yes

Keywords

electroluminescence
external quantum efficiency
machine learning
photovoltaics
QLED

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1021/acsphotonics.4c00413

Cite this

@article{55658e6a831949b08482428ff257b51a,

title = "Machine Learning Correlating Photovoltaics and Electroluminescence of Quantum Dot Light-Emitting Diodes",

abstract = "Thus far, no reports have been made on the correlation between photovoltaics and electroluminescence in light-emitting diodes. With machine learning assistance, we here illustrate the relationship between photovoltaics and electroluminescence of quantum dot light-emitting diodes (QLEDs) by analyzing the measurements of over 200 devices, including J-V-L, photovoltaics, and time-resolved electroluminescence (TREL) test. By applying a decision tree analysis of 17 extracted features of photovoltaics test and TREL curves, we clarify the key features of open-circuit voltage (Voc) and short-circuit current (Isc) under varied illuminated light intensities that correlate with maximum external quantum efficiency (EQEmax) of QLED devices. These photovoltaic features are discussed from the perspective of carrier injection and recombination. In addition, the exciton formation rate (r) derived from TREL curves also affects the EQEmax. The machine learning assisted methodology is also able to predict EQEmax of the QLED with a coefficient of determination of 0.78 with an artificial neural network model.",

keywords = "electroluminescence, external quantum efficiency, machine learning, photovoltaics, QLED",

author = "Min Yang and Hui Bao and Xiangmin Hu and Shipei Sun and Menglin Li and Yiran Yan and Wenjun Hou and Weiran Cao and Hang Liu and Shuangpeng Wang and Haizheng Zhong",

note = "Publisher Copyright: {\textcopyright} 2024 American Chemical Society.",

year = "2024",

month = may,

day = "15",

doi = "10.1021/acsphotonics.4c00413",

language = "English",

volume = "11",

pages = "2131--2137",

journal = "ACS Photonics",

issn = "2330-4022",

publisher = "American Chemical Society",

number = "5",

}

TY - JOUR

T1 - Machine Learning Correlating Photovoltaics and Electroluminescence of Quantum Dot Light-Emitting Diodes

AU - Yang, Min

AU - Bao, Hui

AU - Hu, Xiangmin

AU - Sun, Shipei

AU - Li, Menglin

AU - Yan, Yiran

AU - Hou, Wenjun

AU - Cao, Weiran

AU - Liu, Hang

AU - Wang, Shuangpeng

AU - Zhong, Haizheng

PY - 2024/5/15

Y1 - 2024/5/15

N2 - Thus far, no reports have been made on the correlation between photovoltaics and electroluminescence in light-emitting diodes. With machine learning assistance, we here illustrate the relationship between photovoltaics and electroluminescence of quantum dot light-emitting diodes (QLEDs) by analyzing the measurements of over 200 devices, including J-V-L, photovoltaics, and time-resolved electroluminescence (TREL) test. By applying a decision tree analysis of 17 extracted features of photovoltaics test and TREL curves, we clarify the key features of open-circuit voltage (Voc) and short-circuit current (Isc) under varied illuminated light intensities that correlate with maximum external quantum efficiency (EQEmax) of QLED devices. These photovoltaic features are discussed from the perspective of carrier injection and recombination. In addition, the exciton formation rate (r) derived from TREL curves also affects the EQEmax. The machine learning assisted methodology is also able to predict EQEmax of the QLED with a coefficient of determination of 0.78 with an artificial neural network model.

AB - Thus far, no reports have been made on the correlation between photovoltaics and electroluminescence in light-emitting diodes. With machine learning assistance, we here illustrate the relationship between photovoltaics and electroluminescence of quantum dot light-emitting diodes (QLEDs) by analyzing the measurements of over 200 devices, including J-V-L, photovoltaics, and time-resolved electroluminescence (TREL) test. By applying a decision tree analysis of 17 extracted features of photovoltaics test and TREL curves, we clarify the key features of open-circuit voltage (Voc) and short-circuit current (Isc) under varied illuminated light intensities that correlate with maximum external quantum efficiency (EQEmax) of QLED devices. These photovoltaic features are discussed from the perspective of carrier injection and recombination. In addition, the exciton formation rate (r) derived from TREL curves also affects the EQEmax. The machine learning assisted methodology is also able to predict EQEmax of the QLED with a coefficient of determination of 0.78 with an artificial neural network model.

KW - electroluminescence

KW - external quantum efficiency

KW - machine learning

KW - photovoltaics

KW - QLED

UR - http://www.scopus.com/inward/record.url?scp=85192500333&partnerID=8YFLogxK

U2 - 10.1021/acsphotonics.4c00413

DO - 10.1021/acsphotonics.4c00413

M3 - Article

AN - SCOPUS:85192500333

SN - 2330-4022

VL - 11

SP - 2131

EP - 2137

JO - ACS Photonics

JF - ACS Photonics

IS - 5

ER -

Machine Learning Correlating Photovoltaics and Electroluminescence of Quantum Dot Light-Emitting Diodes

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this