Machine learning algorithms for predicting the photoionization cross section of CdS/ZnSe core/shell spherical quantum dots surrounded by dielectric matrices

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dc.contributor.authorCherni, A.
dc.contributor.authorZeiri, N.
dc.contributor.authorYahyaoui, N.
dc.contributor.authorBaser, P.
dc.contributor.authorSaid, M.
dc.contributor.authorSaadaoui, S.
dc.contributor.authorMurshed, Mohammad N.
dc.date.accessioned2025-05-04T16:42:04Z
dc.date.available2025-05-04T16:42:04Z
dc.date.issued2025
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractIn this study, we explore the prediction of the photoionization cross section (PCS) of CdS/ZnSe core/shell spherical quantum dots (CSQD) surrounded by different dielectric matrices. The quantum dot systems, embedded in polyvinyl alcohol (PVA), polyvinyl chloride (PVC), and silicon dioxide (SiO2) matrices, were modeled under varying core-shell dimensions and dielectric environments. Our findings show that the resonant peak of the PCS experience a redshift with improvement in their amplitude in the case of the PVA matrix, while in the case of the PVC and SiO2 the magnitude of the PCS is reduced and their resonant peak suffers a blueshift. Three different machine learning algorithms were used to estimate the photoionization cross-section, namely Artificial Neural Networks (ANN), Decision Trees (DT), and Random Forest Regressors (RFR). Among these, Random Forest Regression proved to be the most successful algorithm, particularly for the SiO2 matrix, achieving exceptional performance with the coefficient of determination R2 = 0.999 Mean Squared Error MSE=10-4 and the Root Mean Squared Error RMSE=0.0077. While DT exhibited lower MSE, MAE, and RMSE than ANN in the SiO2 matrix, ANN showed potential in capturing more complex nonlinear relationships. These results demonstrate the superior predictive capabilities of RFR and highlight the applicability of machine learning in modeling quantum dot systems. This work offers valuable insights into the optimization of optoelectronic device design through accurate and efficient computational methods. © 2025 The Author(s)
dc.description.sponsorshipDeanship of Scientific Research, King Khalid University, (RGP.2/186/45)
dc.description.sponsorshipDeanship of Scientific Research, King Khalid University
dc.identifier.doi10.1016/j.rinp.2025.108186
dc.identifier.issn2211-3797
dc.identifier.scopus2-s2.0-85219743999
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.rinp.2025.108186
dc.identifier.urihttps://hdl.handle.net/20.500.12418/35040
dc.identifier.volume71
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier B.V.
dc.relation.ispartofResults in Physics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_Scopus_20250504
dc.subjectArtificial Neural Network ANN
dc.subjectDecision Tree DT
dc.subjectHydrogenic impurities HI
dc.subjectPhotoionization cross-section PCS Machin learning ML
dc.subjectQuantum dots QDs
dc.subjectRandom Forest Regression RFR
dc.titleMachine learning algorithms for predicting the photoionization cross section of CdS/ZnSe core/shell spherical quantum dots surrounded by dielectric matrices
dc.typeArticle

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