Data-driven machine learning models for quick prediction of thermal stability properties of OLED materials. (December 2021)

• Record Type: Journal Article
• Authors: Zhao, Y.; Fu, C.; Fu, L.; Liu, Y.; Lu, Z.; Pu, X.
• Is Part Of: Materials today chemistry. Volume 22(2021)
• Publication Date: 2021-12
• Subjects: OLEDs -- Thermal stability -- Machine learning -- Ensemble learning; Chemistry -- Periodicals; Materials -- Research -- Periodicals; Materials science -- Periodicals; Chemistry; Materials -- Research; Electronic journals; Periodicals; 660.282
• DOI: 10.1016/j.mtchem.2021.100625
• Languages: English
• ISSNs: 2468-5194

Abstract

Abstract: 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 ( T g ) and decomposition temperature ( T d ), but experimental determinations of these two important properties generally 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 1, 000 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 mean absolute error, root mean squared error, and R 2 were 17.15 K, 24.63 K, and 0.77 for T g prediction and 24.91 K, 33.88 K, and 0.78 for T d prediction. The prediction performance and the generalization of the ML models were further tested by two applications, which also exhibited satisfactory results. Experimental validation further demonstrated the reliability and the practical potential of the ML-based models. 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://www.oledtppxmpugroup.com . We expect that this platform will become a useful tool for experimental investigation of T g and T d, accelerating the design of OLED materials with desired properties. Graphical abstract: Image 1 Highlights: The quick prediction of T g and T d of OLED materials by ML. Experimental verification showed that the ML models screen high-thermal stability OLED materials. An online prediction platform was constructed based on the optimal models.