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

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 exPlanations (SHAP) method revealed that the space group is the decisive factor in work function prediction for most materials, while atomic-scale features of the material composition are the dominant factors for other materials, refreshing the traditional understanding of the nature of material work functions. Certain space groups (2_1 and 6̄2) tend to exhibit relatively higher work functions (>7 eV), while some other space groups (4/ and 1̄) often present relatively lower work functions (<4 eV). Our methodology, combining robust ML models, multi-condition screening, and DFT calculations, has identified a promising 2D nanomaterial-PS. The material demonstrates exceptional conductivity ( > 10 S m), high transparency (transmittance > 90%), and favorable work function (>5 eV), significantly outperforming the commonly used indium tin oxide (ITO), emerging as a potential candidate for transparent OLED anodes. This study provides new insights into the intrinsic mechanisms affecting the work function of 2D nanomaterials, and provides a cost-effective design framework for identifying other high-performance materials.