Selecting suitable descriptors is crucial for Tg and Td prediction tasks. We started with the choice of molecular fingerprints. A potential challenge exists due to the multifold molecular features involved in the thermal stability of OLED materials, because a single molecular fingerprint does not cover all of these features. However, combining different molecular fingerprints may solve this problem. Table and Table show the training and testing results of different Tg and Td prediction ML models with different fingerprints as inputs. Joint fingerprints including CDK fingerprints (1024 bits), CDK extended fingerprints (1024 bits), and substructure fingerprints count (307 bits) show the best performance, implying that the representation of molecular structures by the molecular backbone and functional groups is potentially better for Tg and Td prediction than the use of other fingerprints. Therefore, the three molecular fingerprints (CDK, CDK extended, substructure count, 2355 bits) were combined as an input, denoted SC_2CDK.