Benzothiophene semiconductor polymer design by machine learning with low exciton binding energy: A vast chemical space generation for new structures

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dc.contributor.authorMallah, Shaimaa H.
dc.contributor.authorGüleryüz, Cihat
dc.contributor.authorSumrra, Sajjad H.
dc.contributor.authorHassan, Abrar U.
dc.contributor.authorGüleryüz, Hasan
dc.contributor.authorMohyuddin, Ayesha
dc.contributor.authorKyhoiesh, Hussein A.K.
dc.date.accessioned2025-02-06T18:01:21Z
dc.date.available2025-02-06T18:01:21Z
dc.date.issued2025
dc.departmentMeslek Yüksekokulları, Sağlık Hizmetleri Meslek Yüksekokulu, Optisyenlik Programıen_US
dc.description.abstractThe development of new organic semiconductors with low exciton binding energies (Eb) is crucial for improving the efficiency of organic photovoltaic (PV) devices. Here, we report the generation of a chemical space of benzothiophene (BDT)-based organic semiconductors with lowest Eb energies using machine learning (ML). Our study involves the design of over 500 organic semiconductor structures with low Eb energies and their synthetic accessibility scores. For this, we collect 1061 BDT based compounds from literature, calculated their Eb energies, and predicted them using ML with Random Forest (RF) regression, yielding the best results. Our analysis, using SHAP values, reveals that heavy atoms are the main factors in lowering Eb values. Furthermore, we tested new organic chromophore structures, which showed an efficient shift of their molecular charges. The UV–Vis spectra of these structures exhibits a redshift in the range of 358–667 nm, while their open-circuit voltage (Voc) and light-harvesting efficiency (LHE) ranges from 1.64 to 1.954 V and 52–91 %, respectively. Current study provides a valuable chemical space for the development of new organic semiconductors with improved efficiency. © 2025 Elsevier Ltden_US
dc.identifier.doi10.1016/j.mssp.2025.109331
dc.identifier.issn1369-8001
dc.identifier.scopus2-s2.0-85216088475
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.mssp.2025.109331
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5340
dc.identifier.volume190en_US
dc.identifier.wosWOS:001413739900001
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakScopus
dc.institutionauthorGüleryüz, Cihat
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofMaterials Science in Semiconductor Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_Scopus_20250206
dc.subjectBenzothiopheneen_US
dc.subjectExciton binding energyen_US
dc.subjectOpen circuit voltageen_US
dc.subjectOrganic semiconductorsen_US
dc.subjectRandom forest regressionen_US
dc.titleBenzothiophene semiconductor polymer design by machine learning with low exciton binding energy: A vast chemical space generation for new structuresen_US
dc.typeArticleen_US

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