A deep learning model for inferring the reverse intersystem crossing rate of TADF organic light-emitting diodes, overcoming the uncertainty of recombination dynamics

Abstract

Polaron recombination and reverse intersystem crossing (RISC) are crucial processes related to the performance of thermally activated delayed fluorescence (TADF) organic light-emitting diodes (OLEDs). In this study, we developed a tandem deep neural network (DNN) model to predict the RISC rate from the transient electroluminescence behavior of TADF OLEDs via step-by-step analysis of both recombination and exciton dynamics. Based on the recombination rate results of the first tandem model, we designed an algorithm in which the second model was automatically selected from among the pretrained candidate models to infer the RISC rate. With comprehensive optimization, a tandem DNN model with a determination coefficient value of 0.985 was realized, overcoming the uncertainty of polaron recombination dynamics. The practical application of the developed model was demonstrated by fabricating a state-of-the-art TADF OLED.