A machine learning assisted designing and chemical space generation of benzophenone based organic semiconductors with low lying LUMO energies

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dc.contributor.authorGüleryüz, Cihat
dc.contributor.authorHassan, Abrar U.
dc.contributor.authorGüleryüz, Hasan
dc.contributor.authorKyhoiesh, Hussein A.K.
dc.contributor.authorMahmoud, Mohamed H.H.
dc.date.accessioned2025-06-13T05:05:50Z
dc.date.available2025-06-13T05:05:50Z
dc.date.issued2025
dc.departmentMeslek Yüksekokulları, Sağlık Hizmetleri Meslek Yüksekokulu, Optisyenlik Programı
dc.description.abstractCurrent study presents a machine learning (ML) approach to design benzophenone-based organic chromophore with their lowest possible LUMO energy (ELUMO). A dataset of their 1142 donors is collected from literature and their molecular descriptors are designed by using RDKit. Among various models, the Random Forest regression model produces accurate results to predict their ELUMO values. Based on these predictions, their 5000 new donors are designed with their Synthetic Accessibility Likelihood Index (SALI) scores. Their SHAP value analysis reveals that their electro topological state indices are the most critical descriptors to lowering ELUMOs. The top- performing donor are further extended with acceptors and their photovoltaic (PV) properties by density functional theory (DFT). Their results show their maximum open-circuit voltage (Voc) of 2.30 V, a short-circuit current (Jsc) of 47.19 mA/cm2, and a light-harvesting efficiency (LHE) of 93 %. This study demonstrates the potential of ML assisted design to design new organic chromophores.
dc.description.sponsorshipFunding agency : Taif University Grant number : TU-DSPP-2024-93
dc.identifier.citationGüleryüz, C., Hassan, A. U., Güleryüz, H., Kyhoiesh, H. A., & Mahmoud, M. H. (2025). A machine learning assisted designing and chemical space generation of benzophenone based organic semiconductors with low lying LUMO energies. Materials Science and Engineering: B, 317, 118212.
dc.identifier.doi10.1016/j.mseb.2025.118212
dc.identifier.issn0921-5107
dc.identifier.scopus2-s2.0-105000042762
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5779
dc.identifier.volume317
dc.identifier.wosWOS:001486781800001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.institutionauthorGüleryüz, Cihat
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofMaterials Science and Engineering: B
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectMachine Learning: LUMO Energy
dc.subjectOrganic semiconductors
dc.subjectPhotovoltaic parameters
dc.subjectSALI score
dc.titleA machine learning assisted designing and chemical space generation of benzophenone based organic semiconductors with low lying LUMO energies
dc.typeArticle

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