Organic light-emitting-diode (OLED) materials have exhibited a wide range of applications. However, further development and commercialization of OLEDs requires higher-quality OLED materials, including high thermal stability associated with the glass transition temperature (Tg) and decomposition temperature (Td). Experimental determinations of the two important properties genernally involve a time-consuming and laborious process. Thus, it is highly desired to develop a quick and accurate prediction tool. Motivated by the changelle, we explored machine learning by constructing new dataset with more than one thousand samples collected from a wide range of literaturesm, through which ensemble learning models were explored. Models trained with the LightGBM algorithm exhibit the best prediction performance, where the values of MAE, RMSE, and R 2 are 17.15 K, 24.63 K, and 0.77 for Tg prediction, 24.91 K, 33.88 K, and 0.78 for Td prediction. The prediction performance and the generalization of the machine learning models are further tested by out-of-sample dataset, also exhibiting satisfactory results. Experimental verification further demonstrates the reliability and the practical potential of the ML-based model. In order to extend the practice application of the ML-based models, an online prediction platform was constructed, including the optimal predition models and all the thermal stability data under study, which are freely available at http://oledtppxmpugroup.com.We expect that they will become a useful tool for experimental investigations on Tg and Td, in turn accelerating the design of the OLED materials with high performance.

Organic light-emitting-diode (OLED) materials have exhibited a wide range of applications. However, the further development and commercialization of OLEDs requires higher-quality OLED materials, including materials with a high thermal stability. Thermal stability is associated with the glass transition temperature (Tg) and decomposition temperature (Td), but experimental determinations of these two important properties genernally involve a time-consuming and laborious process. Thus, the development of a quick and accurate prediction tool is highly desirable. Motivated by the challenge, we explored machine learning (ML) by constructing a new dataset with more than one thousand samples collected from a wide range of literature, through which ensemble learning models were explored. Models trained with the LightGBM algorithm exhibited the best prediction performance, where the values of MAE, RMSE, and R 2 were 17.15 K, 24.63 K, and 0.77 for Tg prediction and 24.91 K, 33.88 K, and 0.78 for Td prediction. The prediction performance and the generalization of the machine learning models were further tested by out-of-sample data, which also exhibited satisfactory results. Experimental validation further demonstrated the reliability and the practical potential of the ML-based model. In order to extend the practical application of the ML-based models, an online prediction platform was constructed. This platform includes the optimal prediction models and all the thermal stability data under study, and it is freely available at http://oledtppxmpugroup.com. We expect that this platform will become a useful tool for experimental investigation of Tg and Td, accelerating the design of OLED materials with desired properties.

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