Abstract
Materials informatics in the development of organic light-emitting diode (OLED) related materials have been performed and exhibited the effectiveness for finding promising compounds with a desired property. However, the molecular structure optimization of the promising compounds through the conventional approach, namely the fine-tuning of molecules, still involves a significant amount of trial and error. This is because it is challenging to endow a single molecule with all the properties required for practical applications. The present work focused on fine-tuning triazine-based electron-transport materials using machine learning (ML) techniques. The prediction models based on localized datasets containing only triazine derivatives showed high prediction accuracy. The descriptors from density functional theory calculations enhanced the prediction of the glass transition temperature. The proposed multistep virtual screening approach extracted the promising triazine derivatives with the coexistence of higher electron mobility and glass transition temperature. Nine selected triazine compounds from 3,670,000 of the initial search space were synthesized and used as the electron transport layer for practical OLED devices. Their observed properties matched the predicted properties, and they enhanced the current efficiency and lifetime of the device. This paper provides a successful model for the ML assisted fine-tuning that effectively accelerates the development of practical materials.