Key Factors Governing the External Quantum Efficiency of Thermally Activated Delayed Fluorescence Organic Light-Emitting Devices: Evidence from Machine Learning

Haochen Shi; Wenzhu Jing; Wu Liu; Yaoyao Li; Zhaojun Li; Bo Qiao; Suling Zhao; Zheng Xu; Dandan Song

doi:10.1021/acsomega.1c06820

Key Factors Governing the External Quantum Efficiency of Thermally Activated Delayed Fluorescence Organic Light-Emitting Devices: Evidence from Machine Learning

ACS Omega. 2022 Feb 22;7(9):7893-7900. doi: 10.1021/acsomega.1c06820. eCollection 2022 Mar 8.

Authors

Haochen Shi^{1

2}, Wenzhu Jing^{1

2}, Wu Liu^{1

2}, Yaoyao Li^{1

2}, Zhaojun Li^{1

2}, Bo Qiao^{1

2}, Suling Zhao^{1

2}, Zheng Xu^{1

2}, Dandan Song^{1

2}

Affiliations

¹ Key Laboratory of Luminescence and Optical Information, Beijing Jiaotong University, Ministry of Education, Beijing 100044, China.
² Institute of Optoelectronics Technology, Beijing Jiaotong University, Beijing 100044, China.

Abstract

Thermally activated delayed fluorescence (TADF) materials enable organic light-emitting devices (OLEDs) to exhibit high external quantum efficiency (EQE), as they can fully utilize singlets and triplets. Despite the high theoretical limit in EQE of TADF OLEDs, the reported values of EQE in the literature vary a lot. Hence, it is critical to quantify the effects of the factors on device EQE based on data-driven approaches. Herein, we use machine learning (ML) algorithms to map the relationship between the material/device structural factors and the EQE. We established the dataset from a variety of experimental reports. Four algorithms are employed, among which the neural network performs best in predicting the EQE. The root-mean-square errors are 1.96 and 3.39% for the training and test sets. Based on the correlation and the feature importance studies, key factors governing the device EQE are screened out. These results provide essential guidance for material screening and experimental device optimization of TADF OLEDs.