Open AccessMachine Learning Guided Design of Single–Phase Hybrid Lead Halide White Phosphors
Author(s) -
Yuan Hailong,
Qi Luyuan,
Paris Michael,
Chen Fei,
Shen Qiang,
Faulques Eric,
Massuyeau Florian,
Gautier Romain
Publication year - 2021
Publication title -
advanced science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.388
H-Index - 100
ISSN - 2198-3844
DOI - 10.1002/advs.202101407
Subject(s) - phosphor , color rendering index , sunrise , color temperature , glazing , high color , materials science , computer science , rendering (computer graphics) , dopant , halide , optics , optoelectronics , doping , artificial intelligence , chemistry , physics , image processing , composite material , inorganic chemistry , image (mathematics) , color image
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.