Machine learning models for accurately predicting properties of CsPbCl3 Perovskite quantum dots

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dc.contributor.authorCadirci, Mehmet Sıddık
dc.contributor.authorCadirci, Musa
dc.date.accessioned2025-10-11T20:45:22Z
dc.date.available2025-10-11T20:45:22Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractPerovskite Quantum Dots (PQDs) have a promising future for several applications due to their unique properties. This study investigates the effectiveness of Machine Learning (ML) in predicting the size, absorbance (1S abs) and photoluminescence (PL) properties of CsPbCl<inf>3</inf> PQDs using synthesizing features as the input dataset. The study employed ML models of Support Vector Regression (SVR), Nearest Neighbour Distance (NND), Random Forest (RF), Gradient Boosting Machine (GBM), Decision Tree (DT) and Deep Learning (DL). Although all models performed highly accurate results, SVR and NND demonstrated the best accurate property prediction by achieving excellent performance on the test and training datasets, with high R2, low Root Mean Squared Error (RMSE) and low Mean Absolute Error (MAE) metric values. Given that ML is becoming more superior, its ability to understand the QDs field could prove invaluable to shape the future of nanomaterials designing. © 2025 Elsevier B.V., All rights reserved.en_US
dc.identifier.doi10.1038/s41598-025-08110-2
dc.identifier.issn2045-2322
dc.identifier.issue1en_US
dc.identifier.pmid40846707en_US
dc.identifier.scopus2-s2.0-105013890249en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1038/s41598-025-08110-2
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21314
dc.identifier.volume15en_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherNature Researchen_US
dc.relation.ispartofScientific Reportsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_Scopus_20250911
dc.subjectPerovskiteen_US
dc.subjectNanomaterialen_US
dc.subjectPerovskiteen_US
dc.subjectQuantum Doten_US
dc.subjectArticleen_US
dc.subjectControlled Studyen_US
dc.subjectDecision Treeen_US
dc.subjectDeep Learningen_US
dc.subjectMachine Learningen_US
dc.subjectMean Absolute Erroren_US
dc.subjectNormal Humanen_US
dc.subjectPhotoluminescenceen_US
dc.subjectPredictionen_US
dc.subjectRandom Foresten_US
dc.subjectRoot Mean Squared Erroren_US
dc.subjectSupport Vector Machineen_US
dc.titleMachine learning models for accurately predicting properties of CsPbCl3 Perovskite quantum dotsen_US
dc.typeArticleen_US

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