Extracting Polaron Recombination from Electroluminescence in Organic Light‐Emitting Diodes by Artificial Intelligence
Jae‐Min Kim, Kyung Hyung Lee, Jun Yeob Lee
Abstract
Abstract 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 is significantly improved using a novel featurization method and input node optimization, achieving an R 2 value of 0.947. The optimized ML model successfully predicts 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.