Machine learning-assisted high-throughput screening of transparent organic light-emitting diode anode materials.

ABSTRACT: Securing optimal work functions for two-dimensional (2D) nanomaterials in Organic Light-Emitting Diodes (OLEDs) is crucial for enhancing the internal quantum efficiency of a device. However, the conventional approach to material discovery, which relies on empirical methods and iterative experimentation, is often time-consuming and inefficient. Here, we propose a target-driven material design framework that combines high-throughput virtual screening and interpretable machine learning (ML) to accelerate the discovery of transparent OLED anode materials. We developed an ML regression model (CatBoost), which accurately predicts work functions for 2D nanomaterials with a mean absolute error (MAE) of 0.20 eV. Remarkably, global and local model interpretation based on the SHapley Additive exPlana

SUBMITTER: Cui L 

PROVIDER: S-EPMC11575640 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature