Exploring structural basis of photovoltaic dye materials to tune power conversion efficiencies: A DFT and ML analysis of Violanthrone

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dc.contributor.authorSumrra, Sajjad H.
dc.contributor.authorGüleryüz, Cihat
dc.contributor.authorHassan, Abrar U.
dc.contributor.authorAbass, Zainab A.
dc.contributor.authorHanoon, Talib M.
dc.contributor.authorMohyuddin, Ayesha
dc.contributor.authorKyhoiesh, Hussein A.K.
dc.contributor.authorAlotaibi, Mohammed T.
dc.date.accessioned2024-12-05T06:52:34Z
dc.date.available2024-12-05T06:52:34Z
dc.date.issued2025en_US
dc.departmentMeslek Yüksekokulları, Sağlık Hizmetleri Meslek Yüksekokulu, Optisyenlik Programıen_US
dc.description.abstractThis study employs a systematic approach to modify Violanthrone (V) structures and analyze their impact on photovoltaic (PV) properties. We use cheminformatics based Python library based RDKit tool to calculate their structural descriptors for to correlate them with their PV parameters. Our analysis reveals a positive correlation for their Open-Circuit Voltage (Voc) and Fill Factor (FF) for indicating that their higher voltage output is associated for their efficient charge carrier mobilities. We also predict their Power Conversion Efficiency (PCE) by drawing their their Scharber diagram which achieves their promising efficiency of up to 15 %. To further enhance the reliability our work, we conduct an extensive literature survey of such organic materials to predict their PCEs by their Machine Learning (ML) after utilizing various ML models. Among five tested ML models, it identifies the Random Forecast (RF) model and Gradient Boosting (GB) models as as the optimal one (R-squared value: 0.82). Their feature importance reveals that their FF is the most significant feature to impact their PCEs (importance value: 10.9). Furthermore, we observe a negative correlation between orbital interaction strength (E(2)) values and orbital energy differences E(j)-E(i) which indicates that their stronger orbital interactions are associated with their smaller energy differences. Our study provides valuable insights for their structural basis to PV material designs for enabling their design for efficient materials in energy conversion.en_US
dc.identifier.citationSumrra, S. H., Güleryüz, C., Hassan, A. U., Abass, Z. A., Hanoon, T. M., Mohyuddin, A., Kyhoiesh, H. A. K., Alotaibi, M. T. (2025). Exploring structural basis of photovoltaic dye materials to tune power conversion efficiencies: A DFT and ML analysis of Violanthrone. Materials Chemistry and Physics, 332. 10.1016/j.matchemphys.2024.130196en_US
dc.identifier.issn0254-0584
dc.identifier.scopus2-s2.0-85210138550
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5074
dc.identifier.volume332en_US
dc.identifier.wosWOS:001414963400001
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakScopus
dc.institutionauthorGüleryüz, Cihat
dc.language.isoen
dc.publisherElsevier Ltden_US
dc.relation.ispartofMaterials Chemistry and Physics
dc.relation.isversionof10.1016/j.matchemphys.2024.130196en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDFTen_US
dc.subjectMachine learningen_US
dc.subjectOrganic photovoltaicsen_US
dc.subjectPower conversion efficiencyen_US
dc.subjectStructutal modifiicationen_US
dc.titleExploring structural basis of photovoltaic dye materials to tune power conversion efficiencies: A DFT and ML analysis of Violanthrone
dc.typeArticle
project.funder.nameTaif University, Grant Number : TU-DSPP-2024-180

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