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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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81200-9 |
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| author | Ivan P. Malashin Daniil Daibagya Vadim Tynchenko Vladimir Nelyub Aleksei Borodulin Andrei Gantimurov Alexandr Selyukov Sergey Ambrozevich Roman Vasiliev |
| author_facet | Ivan P. Malashin Daniil Daibagya Vadim Tynchenko Vladimir Nelyub Aleksei Borodulin Andrei Gantimurov Alexandr Selyukov Sergey Ambrozevich Roman Vasiliev |
| author_sort | Ivan P. Malashin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5b13c91b33354680b69dbf1bf840152e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5b13c91b33354680b69dbf1bf840152e2024-12-29T12:15:44ZengNature PortfolioScientific Reports2045-23222024-12-0114111410.1038/s41598-024-81200-9Exploring temperature-dependent photoluminescence dynamics of colloidal CdSe nanoplatelets using machine learning approachIvan P. Malashin0Daniil Daibagya1Vadim Tynchenko2Vladimir Nelyub3Aleksei Borodulin4Andrei Gantimurov5Alexandr Selyukov6Sergey Ambrozevich7Roman Vasiliev8Bauman Moscow State Technical UniversityBauman Moscow State Technical UniversityBauman Moscow State Technical UniversityBauman Moscow State Technical UniversityBauman Moscow State Technical UniversityBauman Moscow State Technical UniversityBauman Moscow State Technical UniversityBauman Moscow State Technical UniversityLomonosov Moscow State UniversityAbstract 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.https://doi.org/10.1038/s41598-024-81200-9Machine learningCdSeNanoplateletsLuminescenceTemporal dynamics |
| spellingShingle | Ivan P. Malashin Daniil Daibagya Vadim Tynchenko Vladimir Nelyub Aleksei Borodulin Andrei Gantimurov Alexandr Selyukov Sergey Ambrozevich Roman Vasiliev Exploring temperature-dependent photoluminescence dynamics of colloidal CdSe nanoplatelets using machine learning approach Scientific Reports Machine learning CdSe Nanoplatelets Luminescence Temporal dynamics |
| title | Exploring temperature-dependent photoluminescence dynamics of colloidal CdSe nanoplatelets using machine learning approach |
| title_full | Exploring temperature-dependent photoluminescence dynamics of colloidal CdSe nanoplatelets using machine learning approach |
| title_fullStr | Exploring temperature-dependent photoluminescence dynamics of colloidal CdSe nanoplatelets using machine learning approach |
| title_full_unstemmed | Exploring temperature-dependent photoluminescence dynamics of colloidal CdSe nanoplatelets using machine learning approach |
| title_short | Exploring temperature-dependent photoluminescence dynamics of colloidal CdSe nanoplatelets using machine learning approach |
| title_sort | exploring temperature dependent photoluminescence dynamics of colloidal cdse nanoplatelets using machine learning approach |
| topic | Machine learning CdSe Nanoplatelets Luminescence Temporal dynamics |
| url | https://doi.org/10.1038/s41598-024-81200-9 |
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