Machine Learning Guided Design of Single–Phase Hybrid Lead Halide White Phosphors
Hailong Yuan, Luyuan Qi, Michaël Paris, Fei Chen, Qiang Shen, E. Faulques, Florian Massuyeau, Romain Gautier
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.