A materials informatics driven fine-tuning of triazine-based electron-transport layer for organic light-emitting devices.

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 densi

SUBMITTER: Sato K 

PROVIDER: S-EPMC10881559 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature