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Abstract:
Thermally activated delayed fluorescence (TADF) chromophores have attracted significant attention because they can harvest singlets and triplets in organic light-emitting diodes (OLEDs), resulting in high external quantum efficiency (EQE). This work aims to use a data-driven machine-learning model to predict the relationship between EQE and essential features of TADF-based OLEDs. The study uses a set of experimental data and applies various machine-learning models to analyze the relationship between EQE and the features of the device. The Random Forest model is found to have the highest accuracy for predicting EQE for non-doped TADF emitters. The importance of feature selection and its correlation with EQE is also analyzed, providing insight into how to select the best machine-learning model for rapid material screening and device optimization for non-doped TADF materials.
