A machine learning and DFT assisted analysis of benzodithiophene based organic dyes for possible photovoltaic applications

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dc.contributor.authorGüleryüz, Cihat
dc.contributor.authorSumrra, Sajjad H.
dc.contributor.authorHassan, Abrar U.
dc.contributor.authorMohyuddin, Ayesha
dc.contributor.authorWaheeb, Azal S.
dc.contributor.authorAwad, Masar A.
dc.contributor.authorJalfan, Ayad R.
dc.contributor.authorNoreen, Sadaf
dc.contributor.authorKyhoiesh, Hussein A.K.
dc.contributor.authorEl Azab, Islam H.
dc.date.accessioned2024-12-04T12:25:25Z
dc.date.available2024-12-04T12:25:25Z
dc.date.issued2025en_US
dc.departmentMeslek Yüksekokulları, Sağlık Hizmetleri Meslek Yüksekokulu, Optisyenlik Programıen_US
dc.description.abstractWe present a synergistic approach to combine Machine Learning (ML), Density Functional Theory (DFT), and molecular descriptor analysis for designing high-performance benzodithiophene (BDT) based chromophores. A dataset of 366 BDT incorporated moieties is compiled from literature while their molecular descriptors are designed by using Python programming language. Linear and Random Forest Regression models produces best results to predict their exciton binding energy (Eb) with their R-Squared (R2) value 0.87 and 0.94 respectively. Their DFT calculations provides additional features, including molecular charges. Their ML models also reveals that their Eb values are a crucial predictor for their photovoltaic (PV) performance as its lower value could facilitate efficient charge carrier separation. For this, their hydrogen bond acceptors (HBA) and topological polar surface area (TPSA) emerges as key descriptors during their regression analysis. Their DFT validation shows negligible differences in their molecular charges to suggest their electron donor/acceptor moieties can significantly impact their chromophore nature. The current research work is helpful for efficiently screening the suitability of organic chromophores for their PV applications through advanced computational tools.en_US
dc.identifier.citationGüleryüz, C., Sumrra, S. H., Hassan, A. U., Mohyuddin, A., Waheeb, A. S., Awad, M. A., Jalfan, A. R., Noreen, S., Kyhoiesh, H. A. K., El Azab, I. H. (2025). A machine learning and DFT assisted analysis of benzodithiophene based organic dyes for possible photovoltaic applications. Journal of Photochemistry and Photobiology A: Chemistry, 460. 10.1016/j.jphotochem.2024.116157en_US
dc.identifier.issn1010-6030
dc.identifier.scopus2-s2.0-85208976054
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5066
dc.identifier.volume460en_US
dc.identifier.wosWOS:001359378500001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorGüleryüz, Cihat
dc.language.isoen
dc.publisherElsevier B.V.en_US
dc.relation.ispartofJournal of Photochemistry and Photobiology A: Chemistry
dc.relation.isversionof10.1016/j.jphotochem.2024.116157en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBenzodithiopheneen_US
dc.subjectDFT calculationsen_US
dc.subjectExciton Binding Energyen_US
dc.subjectMLen_US
dc.subjectPhotovoltaic Chromophoresen_US
dc.titleA machine learning and DFT assisted analysis of benzodithiophene based organic dyes for possible photovoltaic applications
dc.typeArticle

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