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Yuan, H., Qi, L., Paris, M., Chen, F., Shen, Q., Faulques, E., Massuyeau, F. & Gautier, R. (2021) Machine Learning Guided Design of Single-Phase Hybrid Lead Halide White Phosphors. Advanced Science, n/a 2101407. 
Added by: Richard Baschera (2021-07-29 08:08:14)   Last edited by: Richard Baschera (2021-07-29 08:09:56)
Type de référence: Article
DOI: 10.1002/advs.202101407
Numéro d'identification (ISBN etc.): 2198-3844
Clé BibTeX: Yuan2021
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Mots-clés: high color rendering, machine-learning, single-phase white phosphors, tunable color temperature
Créateurs: Chen, Faulques, Gautier, Massuyeau, Paris, Qi, Shen, Yuan
Collection: Advanced Science
Consultations : 1/268
Indice de consultation : 9%
Indice de popularité : 2.25%
Liens URLs     https://onlinelibr ... 002/advs.202101407
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.
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