Тип публикации: статья из журнала
Год издания: 2024
Идентификатор DOI: 10.1038/s41598-024-81200-9
Аннотация: The study explore machine learning (ML) techniques to predict temperature-dependent photoluminescence (PL) spectra in colloidal CdSe nanoplatelets (NPLs), leveraging polynomial regression models trained on experimental data from 85 to 270 K spanning temperatures to forecast PL spectra backward to 0 K and forward to 300 K. 6th-degreПоказать полностьюe polynomial models with Tweedie regression were optimal for band energy ( $$B_1$$ ) predictions up to 300 K, while 9th-degree models with LassoLars and Linear Regression regressors were suitable for backward predictions to 0 K. For exciton energy ( $$B_2$$ ), the Lasso model of degree 5 and the Ridge model of degree 4 performed well up to 300 K, while the Tweedie model of degree 2 and Theil-Sen model of degree 2 showed promise for predictions to 0 K. Furthermore, a GA-based approach was utilized to fit experimental data to theoretical model of Fan and Varshni equations, facilitating a comparative analysis with the ML-predicted curves.
Журнал: Scientific Reports
Выпуск журнала: Т. 14, № 1
Номера страниц: 30878
ISSN журнала: 20452322
Место издания: Berlin
Издатель: Springer Nature