Exploring new useful phosphors by combining experiments with machine learning
| Autor: | Takashi Takeda, Yukinori Koyama, Hidekazu Ikeno, Satoru Matsuishi, Naoto Hirosaki |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Science and Technology of Advanced Materials, Vol 25, Iss 1 (2024) |
| Druh dokumentu: | article |
| ISSN: | 14686996 1878-5514 1468-6996 |
| DOI: | 10.1080/14686996.2024.2421761 |
| Popis: | New phosphors are consistently in demand for advances in solid-state lighting and displays. Conventional trial-and-error exploration experiments for new phosphors require considerable time. If a phosphor host suitable for the target luminescent property can be proposed using computational science, the speed of development of new phosphors will significantly increase, and unexpected/overlooked compositions could be proposed as candidates. As a more practical approach for developing new phosphors with target luminescent properties, we looked at combining experiments with machine learning on the topics of emission wavelength, full width at half maximum (FWHM) of the emission peak, temperature dependence of the emission spectrum (thermal quenching), new phosphors with new chemical composition or crystal structure, and high-throughput experiments. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|