Machine Learning Guided Design of Single–Phase Hybrid Lead Halide White Phosphors

• Main Authors: Hailong Yuan, Luyuan Qi, Michael Paris, Fei Chen, Qiang Shen, Eric Faulques, Florian Massuyeau, Romain Gautier
• Format: Article
• Language: English
• Published: Wiley 2021-10-01
• Series: Advanced Science
• Subjects: high color rendering; machine‐learning; single‐phase white phosphors; tunable color temperature
• Online Access: https://doi.org/10.1002/advs.202101407

Abstract Designing new single‐phase white phosphors for solid‐state lighting is a challenging trial–error process as it requires to navigate in a multidimensional space (composition of the host matrix/dopants, experimental conditions, etc.). Thus, no single‐phase white phosphor has ever been reported to exhibit both a high color rendering index (CRI ‐ degree to which objects appear natural under the white illumination) and a tunable correlated color temperature (CCT). In this article, a novel strategy consisting in iterating syntheses, characterizations, and machine learning (ML) models to design such white phosphors is demonstrated. With the guidance of ML models, a series of luminescent hybrid lead halides with ultra‐high color rendering (above 92) mimicking the light of the sunrise/sunset (CCT = 3200 K), morning/afternoon (CCT = 4200 K), midday (CCT = 5500 K), full sun (CCT = 6500K), as well as an overcast sky (CCT = 7000 K) are precisely designed.