The Tg and Td data in this article are also placed on this website. A screenshot of the website homepage interface is shown in Fig. . More details can be found by visiting the website. We will continue updating the dataset and optimal model on the website in order to more accurately predict Tg and Td of OLED materials. to support the data-driven ML models. With the dataset and the combined descriptors, the optimal LightGBM models offer satisfactory accuracy for the prediction of Td and Tg, with higher accuracy than other six classic ML models (SVM, PLS, LASSO, KRR, kNN, and RF ML models). The models are further validated by two types of out-ofsample prediction (including recently reported host and guest materials as well as organic ETMs and HTMs), exhibiting good robustness and universality. Finally, the experimental validation of a high thermal stability OLED material further confirms the reliability of our models and practical application potential. In addition, we constructed a website including all the data and the optimal ML models in order to provide a simple and quick tool for estimating these two important properties for unknown compounds.