Exploring Temperature-Dependent Photoluminescence Dynamics in Colloidal CdSe Nanoplatelets using Machine Learning Approach

This is the authors' abstract. We don't add key points for this paper.

Abstract The study explore machine learning techniques to predict temperature-dependent photoluminescence spectra in colloidal CdSe nanoplatelets, leveraging polynomial regression models trained on experimental data from 85 to 270 K spanning temperatures to forecast photoluminescence spectra backward to 0 K and forward to 300 K. 6th-degree polynomial models with Tweedie regression were optimal for band energy B1 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 B2, 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, we utilize a GA-based approach to fit experimental data to theoretical model of Fan and Varshni equations, facilitating a comparative analysis with the ML-predicted curves.