Exploring temperature-dependent photoluminescence dynamics of colloidal CdSe nanoplatelets using machine learning approach
Abstract 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 backw...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-81200-9 |
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| Summary: | Abstract 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-degree 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. |
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| ISSN: | 2045-2322 |