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Booming progress has been made in both the molecular design concept and the fundamental electroluminescence (EL) mechanism of thermally activated delayed fluorescence (TADF)-based organic light-emitting diodes (OLEDs) in recent years. One of the requirements for TADF-based OLEDs having high external quantum efficiency (EQE) is the favorable energy level alignment between the host and the guest to promote the energy transfer and improve the carrier balance. However, strategies to optimize the TADF-based OLED performance by selecting suitable host–guest systems in the light-emitting layer are far from enough. In this work, we investigated guest–host systems through the use of two machine-learning approaches (feature-based and similarity-based algorithms) from our recent effort for the optimization of TADF-based OLEDs. The Random Forest (RF) algorithm based on the features of electronic …