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

Exploring Temperature-Dependent Photoluminescence Dynamics in Colloidal CdSe Nanoplatelets using Machine Learning Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exploring Temperature-Dependent Photoluminescence Dynamics in Colloidal CdSe Nanoplatelets using Machine Learning Approach Ivan P. Malashin, Daniil Daibagya, Vadim Tynchenko, Vladimir Nelyub, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4445221/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version 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. Physical sciences/Nanoscience and technology/Nanoscale materials Physical sciences/Mathematics and computing/Scientific data Physical sciences/Nanoscience and technology/Nanoscale devices Physical sciences/Optics and photonics/Optical materials and structures CdSe - Cadmium Selenide NPLs - nanoplateletes ML - machine learning PL - photoluminescence GA - genetic algorithm TEM - transmission electron microscopy Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Nov, 2024 Reviews received at journal 11 Oct, 2024 Reviewers agreed at journal 02 Oct, 2024 Reviews received at journal 03 Jun, 2024 Reviewers agreed at journal 24 May, 2024 Reviewers agreed at journal 22 May, 2024 Reviewers invited by journal 22 May, 2024 Editor assigned by journal 22 May, 2024 Editor invited by journal 22 May, 2024 Submission checks completed at journal 22 May, 2024 First submitted to journal 19 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4445221","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":309896620,"identity":"fb9d45e4-7edf-47ff-8ed4-8caf170aa96f","order_by":0,"name":"Ivan P. 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