Strategies and Challenges of Data-driven Research and Discovery in Luminescent Materials

Artificial intelligence has been bringing a great of convenience for human in manufacture，life，science and technology，for its abilities of efficient data analyses，accurate prediction，automatic task executing，and personalized service. Machine learning and high-throughput computing have extensively permeated and successfully applied in the field of materials，opening up new horizons for innovative research and design methods in luminescent materials. By employing efficient algorithms for mining and processing large-scale data，the screening and design process of new materials is accelerated，thereby driving the discovery and application progress of novel materials. This article provides an overview of the recent advances in data-driven research on luminescent materials. Based on relevant research cases，it outlines the entire process of data-driven material research and elaborates on the importance and implementation strategies of data acquisition in the development of luminescent materials. It also conducts a thorough analysis of how to accurately extract core features that characterize material performance，while exploring algorithm selection strategies applicable to the field of luminescent materials. Finally，the bottleneck of the current research on data-driven luminescent materials is pointed out，such as a lack of high-quality data and difficulties in constructing complex structure-performance correlation models. It also provides an outlook on future development directions，with a particular emphasis on the construction of luminescent material database platforms，implementation of high-throughput experiments，and establishment of corresponding data production standards. © 2024 Editorial Office of Chinese Optics. All rights reserved.