From Model to Material: Machine Learning Enabled Discovery of LED‐Compatible Phosphors

Abstract Phosphor‐converted white light‐emitting diodes are transforming energy‐efficient lighting technologies. At the core of their performance are Ce 3+ and Eu 2+ activated phosphors, whose photoluminescent properties significantly impact the lighting devices’ efficiency and, equally important, govern the light quality. However, the discovery of new phosphors that meet the stringent requirements needed for commercialization has remained a challenge. Recent advances in data‐driven approaches, particularly machine learning and optimization algorithms, have begun to accelerate this process. These methods enable the accurate prediction of key photoluminescent properties and facilitate the exploration of vast compositional spaces of inorganic phosphors, guiding the targeted synthesis of new high‐performance phosphors. This review highlights the major progress in data‐driven discovery of Ce 3+ and Eu 2+ phosphors, emphasizing property prediction, materials screening, and experimental validation. It concludes with an outlook on future opportunities and challenges in the application of artificial intelligence to phosphor discovery.