AI-powered OLEDs: speeding up innovation in displays

Abstract

The widespread adoption of organic light-emitting diodes (OLEDs) in high-end electronics is due to their superior contrast, color accuracy, and flexible form factors. Despite these advancements, significant challenges remain, which limit overall device lifetime and efficiency. Traditional materials discovery methods are often slow, costly, and inefficient in exploring the vast chemical space, while conventional computational approaches are resource-intensive. The availability of materials databases and sophisticated algorithms has propelled machine learning to reshape OLED research. This review highlights machine learning's pivotal role throughout the entire OLED research and development process—from accelerating materials design and discovery through accurate property prediction, de novo molecular design, and high-throughput virtual screening to predicting device performance metrics such as external quantum efficiency and lifetime. Furthermore, machine learning is improving OLED characterization and analysis, enabling advanced spectroscopic data interpretation and image analysis for automated defect detection and manufacturing process control. Looking ahead, the future of machine learning-driven OLED innovation will focus on overcoming data ecosystem challenges, improving model interpretability using explainable artificial intelligence (XAI) techniques, expanding applicability to emerging OLED technologies (e.g., perovskite and quantum dot OLEDs) via transfer learning and physics-informed machine learning and deploying advanced methods for smart manufacturing. Ultimately, a collaborative effort between humans and artificial intelligence is set to accelerate scientific progress toward next-generation OLEDs.