Machine Learning‐Triggered Auto‐Discovery and Promotion of Luminescent Materials: From Predictive Modeling to Autonomous Laboratories

Abstract

ABSTRACT Machine learning (ML) is transforming the development of luminescent materials, including quantum dots, rare‐earth‐doped phosphors, and transition‐metal‐doped phosphors, by addressing the limitations inherent in traditional trial‐and‐error methodologies. First‐generation ML models accurately predict single properties such as bandgaps and Debye temperature, enabling virtual material screening and revealing underlying physical mechanisms. Second‐generation approaches use ensemble learning and Bayesian optimization to optimize multiple objectives simultaneously, such as quantum efficiency, thermal stability, chromaticity, and environmental compliance, leading to synergistic performance enhancements. Next‐generation autonomous laboratories, which integrate ML‐driven predictions with robotic synthesis and real‐time characterization, create closed‐loop systems that significantly accelerate the materials discovery process and compress traditional development cycles. Despite these advancements, significant challenges persist, including poor data quality and standardization, limited model generalizability across different material systems, and the low interpretability of complex “black‐box” models. Overcoming these limitations will fully harness the potential of this ML‐driven ecosystem, facilitating the intelligent design of next‐generation luminescent materials for advanced displays, energy‐efficient lighting, and biomedical applications.