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Mahato, Kapil Dev, Kumar, Uday (2024) Optimized Machine learning techniques Enable prediction of organic dyes photophysical Properties: Absorption Wavelengths, emission Wavelengths, and quantum yields. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 308. 123768 doi:10.1016/j.saa.2023.123768

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Reference TypeJournal (article/letter/editorial)
TitleOptimized Machine learning techniques Enable prediction of organic dyes photophysical Properties: Absorption Wavelengths, emission Wavelengths, and quantum yields
JournalSpectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
AuthorsMahato, Kapil DevAuthor
Kumar, UdayAuthor
Year2024 (March)Volume308
PublisherElsevier BV
DOIdoi:10.1016/j.saa.2023.123768Search in ResearchGate
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Mindat Ref. ID17059670Long-form Identifiermindat:1:5:17059670:3
GUID0
Full ReferenceMahato, Kapil Dev, Kumar, Uday (2024) Optimized Machine learning techniques Enable prediction of organic dyes photophysical Properties: Absorption Wavelengths, emission Wavelengths, and quantum yields. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 308. 123768 doi:10.1016/j.saa.2023.123768
Plain TextMahato, Kapil Dev, Kumar, Uday (2024) Optimized Machine learning techniques Enable prediction of organic dyes photophysical Properties: Absorption Wavelengths, emission Wavelengths, and quantum yields. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 308. 123768 doi:10.1016/j.saa.2023.123768
In(2024) Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy Vol. 308. Elsevier BV

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