Bandgap tuned indaceno based polymer design by machine learning for chemical space by descriptor designing and data guidelines

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dc.contributor.authorSumrra, Sajjad H.
dc.contributor.authorGüleryüz, Cihat
dc.contributor.authorHassan, Abrar U.
dc.contributor.authorAfzaal, Maria
dc.contributor.authorSiddiqa, Ayesha
dc.contributor.authorEl Azab, Islam H.
dc.contributor.authorElnaggar, Ashraf Y.
dc.contributor.authorMahmoud, Mohamed H.H.
dc.date.accessioned2025-08-14T14:54:38Z
dc.date.available2025-08-14T14:54:38Z
dc.date.issued2025
dc.departmentMeslek Yüksekokulları, Sağlık Hizmetleri Meslek Yüksekokulu, Optisyenlik Programı
dc.descriptionArticle number : 109899
dc.description.abstractThis research introduces an innovative approach for developing bandgap-tuned polymers by machine learning assisted modeling of indaceno polymer chemical space. Using a dataset of indaceno donor moieties, the descriptors that encode fundamental molecular features are designed and trained to predict their bandgaps. After three modeling rounds of breaking retrosynthesis in Python, 1000 new polymers with their bandgaps are designed. The study shows that descriptors like valence electrons, Labute ASA, and Morgan Density has a significant impact on model performance to highlight their key role. Additionally, the synthetic accessibility scores of newly designed polymers reach a maximum of 17 for RDkit based descriptors to indicate their ease of synthesis and promising practical applicability. This work not only deepens the understanding of indaceno polymers but also lays the groundwork indaceno based polymer design of through data-driven approaches.
dc.description.sponsorshipFunding agency : Taif University Grant number : TU-DSPP-2024-93
dc.identifier.citationSumrra, S. H., Güleryüz, C., Hassan, A. U., Afzaal, M., Siddiqa, A., El Azab, I. H., ... & Mahmoud, M. H. (2025). Bandgap tuned indaceno based polymer design by machine learning for chemical space by descriptor designing and data guidelines. Materials Science in Semiconductor Processing, 200, 109899. 10.1016/j.mssp.2025.109899
dc.identifier.doi10.1016/j.mssp.2025.109899
dc.identifier.issn1369-8001
dc.identifier.scopus2-s2.0-105011590270
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5856
dc.identifier.volume200
dc.identifier.wosWOS:001542130700001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorGüleryüz, Cihat
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofMaterials Science in Semiconductor Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBandgap
dc.subjectIndaceno
dc.subjectMachine learning
dc.subjectPolymer design
dc.subjectReverse engineering
dc.titleBandgap tuned indaceno based polymer design by machine learning for chemical space by descriptor designing and data guidelines
dc.typeArticle

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