The glass transition temperature (Tg) and decomposition temperature (Td, corresponding to 5% weight loss) are the two most important thermal properties of OLED materials, and exert significant influence on the performance of OLED devices . The OLED devices irreversibly deteriorate when heated above their Tg . High Tg and Td values can reduce heat-induced morphology changes, thus enhancing the stability of device performance . The experimental Tg and Td values of the OLED materials are generally measured by differential scanning calorimetry (DSC) and thermal gravimetric analysis (TGA). However, before DSC and TGA determination, OLED materials need to be purified by column chromatography or sublimation , which are complicated and time-consuming. Thus, the development of a quick and accurate method to predict Tg and Td is highly desirable. It is generally acknowledged that the thermal stability of OLED materials is closely related to their molecular structures . However, the relationship between molecular structure and the thermal stability such as Tg and Td is complex and has not been elucidated. Machine learning (ML), a key technique used in artificial intelligence, can map the complex relationshiop underlying a large amount of data. ML has been succesfully applied in the fields of medicinal chemistry, environmental risk assessment, organic synthesis, and materials science . To the best of our knowledge, only two previous studies have used machine learning methods to predict the Tg of OLED materials , and there is a significant lack of Td predictions. In 2003, Yin et al. performed a quantitative structureproperty relationship (QSPR) model to predict the Tg of 88 OLED molecules with MAE = 17.9 K by using a multilinear regression (MLR) method . In 2013, Silva et al. developed a QSPR model to predict the Tg of 66 OLED materials with R 2 = 0.96, MAE = 0.97 K by using support vector machines (SVM) . These two studies appear to make highly accurate predictions based on a small amount of data (fewer than 100 molecules).