Evaluating the electronic and structural basis of carbon selenide-based quantum dots as photovoltaic design materials : A DFT and ML analysis

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dc.contributor.authorKadhum, Afaf M.
dc.contributor.authorWaheeb, Azal S.
dc.contributor.authorAwad, Masar A.
dc.contributor.authorHassan, Abrar U.
dc.contributor.authorSumrra, Sajjad H.
dc.contributor.authorGüleryüz, Cihat
dc.contributor.authorMohyuddin, Ayesha
dc.contributor.authorNoreen, Sadaf
dc.contributor.authorKyhoiesh, Hussein A.K.
dc.contributor.authorAlotaibi, Mohammed T.
dc.date.accessioned2024-11-14T08:42:19Z
dc.date.available2024-11-14T08:42:19Z
dc.date.issued2024en_US
dc.departmentMeslek Yüksekokulları, Sağlık Hizmetleri Meslek Yüksekokulu, Optisyenlik Programıen_US
dc.description.abstractWe present a new study on the design, discovery and space generation of carbon selenide based photovoltaic (PV) materials. By extending acceptors and leveraging density functional theory (DFT) and machine learning (ML) analysis, we discover new QDs with remarkable PV properties. We employ various ML models, to correlate the exciton binding energy (Eb) of 938 relevant compounds from literature with their molecular descriptors of structural features that influence their performance. Our study demonstrates the potential of ML approaches in streamlining the design and discovery of high-efficiency PV materials. Also the RDKit computed molecular descriptors correlates with PV parameters revealed maximum absorption (λmax) ranges of 509–531 nm, light harvesting efficiency (LHE) above 92 %, Open Circuit Voltage (Voc) of 0.22–0.45 V, and short Circuit (Jsc) currents of 37.92–42.75 mA/cm2. Their Predicted Power Conversion Efficiencies (PCE) using the Scharber method reaches upto 09–13 %. This study can pave the way for molecular descriptor-based design of new PV materials, promising a paradigm shift in the development of high-efficiency solar energy conversion technologies.en_US
dc.identifier.citationKadhum, A. M., Waheeb, A. S., Awad, M. A., Hassan, A. U., Sumrra, S. H., Güleryüz, C., Mohyuddin, A., Noreen, S., Kyhoiesh, H. A. K., Alotaibi, M. T. (2024). Evaluating the electronic and structural basis of carbon selenide-based quantum dots as photovoltaic design materials: A DFT and ML analysis. Solar Energy, 284. 10.1016/j.solener.2024.113068en_US
dc.identifier.issn0038-092X
dc.identifier.scopus2-s2.0-85208021876
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4993
dc.identifier.volume284en_US
dc.identifier.wosWOS:001351333900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorKadhum, Afaf M.
dc.institutionauthorGüleryüz, Cihat
dc.language.isoen
dc.publisherElsevier Ltden_US
dc.relation.ispartofSolar Energy
dc.relation.isversionof10.1016/j.solener.2024.113068en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine Learningen_US
dc.subjectPower Conversion Efficiencyen_US
dc.subjectQuantum Dotsen_US
dc.subjectRDKiten_US
dc.subjectScharber methoden_US
dc.titleEvaluating the electronic and structural basis of carbon selenide-based quantum dots as photovoltaic design materials : A DFT and ML analysis
dc.typeArticle

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