Machine Learning Guided Design of Single–Phase Hybrid Lead Halide White Phosphors
| Autor: | Hailong Yuan, Luyuan Qi, Michael Paris, Fei Chen, Qiang Shen, Eric Faulques, Florian Massuyeau, Romain Gautier |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Advanced Science, Vol 8, Iss 19, Pp n/a-n/a (2021) |
| Druh dokumentu: | article |
| ISSN: | 2198-3844 20210140 |
| DOI: | 10.1002/advs.202101407 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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