A rapid UV/Vis assisted designing of benzodithiophene based polymers by machine learning to predict their light absorption for photovoltaics

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dc.contributor.authorHassan, Abrar U.
dc.contributor.authorGüleryüz, Cihat
dc.contributor.authorSumrra, Sajjad H.
dc.contributor.authorNoreen, Sadaf
dc.contributor.authorMahmoud, Mohamed H.H.
dc.date.accessioned2025-05-22T08:36:29Z
dc.date.available2025-05-22T08:36:29Z
dc.date.issued2025
dc.departmentMeslek Yüksekokulları, Sağlık Hizmetleri Meslek Yüksekokulu, Optisyenlik Programı
dc.description.abstractAs global energy demands escalate, developing high-performance photovoltaic (PV) materials through accelerated design methodologies is imperative. A machine learning (ML) assisted predictive models are used to accelerate the design of benzodithiophene (BDT)-based polymers for their PV applications. The current approach leverages a curated dataset of 191 compounds with experimental UV–Vis spectra, mapped to molecular electronic descriptors via RDKit. Random Forest modeling yields a predictive framework (R2 = 0.98) for predicting their maximum absorption (λmax). After it, their 5000 new designs as novel polymers, identifying top performers with Synthetic Accessibility Likelihood Index scores up to 57, ensuring synthesis feasibility have also been designed. Feature importance analysis highlights MaxPartialCharge and Aromatic rings as crucial descriptors. The designed materials exhibit optimal energy gaps (1.35–2.0 eV), paving the way for efficient PV devices. The computed UV–Vis spectra of best predicted polymers are studied with their λmax range of 487–987 nm showing a significant redshift behavior. The designed polymers presents and good potential towards and they can be good candidates for organic solar cell applications.
dc.description.sponsorshipFunding agency: Taif University
dc.identifier.citationHassan, A. U., Güleryüz, C., Sumrra, S. H., Noreen, S., & Mahmoud, M. H. (2025). A rapid UV/Vis assisted designing of benzodithiophene based polymers by machine learning to predict their light absorption for photovoltaics. Organic Electronics, 141, 107227.
dc.identifier.doi10.1016/j.orgel.2025.107227
dc.identifier.issn1566-1199
dc.identifier.scopus2-s2.0-85219428157
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5754
dc.identifier.volume141
dc.identifier.wosWOS:001440081000001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakScopus
dc.institutionauthorGüleryüz, Cihat
dc.institutionauthorid0000-0003-4812-8129
dc.language.isoen
dc.publisherElsevier B.V.
dc.relation.ispartofOrganic Electronics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectExperimental UV–Vis spectra
dc.subjectPhotovoltaic devices
dc.subjectPolymer design
dc.subjectRandom forest
dc.titleA rapid UV/Vis assisted designing of benzodithiophene based polymers by machine learning to predict their light absorption for photovoltaics
dc.typeArticle

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