Deep Learning Prediction of Triplet–Triplet Annihilation Parameters in Blue Fluorescent Organic Light‐Emitting Diodes

Abstract

The triplet–triplet annihilation (TTA) ratio and the rate coefficient (kTT) of TTA are key factors in estimating the contribution of triplet excitons to radiative singlet excitons in fluorescent TTA organic light‐emitting diodes. In this study, we implemented deep learning models to predict key factors from transient electroluminescence (trEL) data using new numerical equations. A new TTA model was developed that considers both polaron and exciton dynamics, enabling the distinction between prompt and delayed singlet decays with a fundamental understanding of the mechanism. In addition, deep learning models for predicting the kinetic coefficients and TTA ratio were established. After comprehensive optimization inspired by photophysics, we achieved determination coefficient values of 0.992 and 0.999 in the prediction of kTT and TTA ratio, respectively, indicating a nearly perfect prediction. The contribution of each kinetic parameter of polaron and exciton dynamics to the trEL curve was discussed using various deep learning models.This article is protected by copyright. All rights reserved