In Advanced materials (Deerfield Beach, Fla.)
Direct exploring the electroluminescence (EL) of organic light-emitting diodes (OLEDs) is a challenge due to the complicated processes of polarons, excitons, and their interactions. This study demonstrated the extraction of the polaron dynamics from transient EL by predicting the recombination coefficient via artificial intelligence, overcoming multivariable kinetics problems. The performance of a machine learning (ML) model trained by various EL decay curves was significantly improved using a novel featurization method and input node optimization, achieving an R2 value of 0.947. The optimized ML model successfully predicted the recombination coefficients of actual OLEDs based on an exciplex-forming cohost, enabling the quantitative understanding of the overall polaron behavior under various electrical excitation conditions. This article is protected by copyright. All rights reserved.
Kim Jae-Min, Lee Kyung Hyung, Lee Jun Yeob
2023-Feb-14
device architecture, machine learning, organic light-emitting diodes, polaron dynamics, polaron recombination
