A machine learning analysis to predict the stability driven structural correlations of selenium-based compounds as surface enhanced materials

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dc.contributor.authorGüleryüz, Cihat
dc.contributor.authorSumrra, Sajjad H.
dc.contributor.authorHassan, Abrar U.
dc.contributor.authorMohyuddin, Ayesha
dc.contributor.authorElnaggar, Ashraf Y.
dc.contributor.authorNoreen, Sadaf
dc.date.accessioned2025-06-12T14:12:29Z
dc.date.available2025-06-12T14:12:29Z
dc.date.issued2025
dc.departmentMeslek Yüksekokulları, Sağlık Hizmetleri Meslek Yüksekokulu, Optisyenlik Programı
dc.description.abstractThe selenium-based compounds are gaining significance for their surface-enhanced properties. In order to accelerate their discovery, a machine learning (ML) approach has been employed to predict their structural correlations. For this a dataset of 618 compounds is collected from literature and is trained by using Support Vector Machine (SVM) with its Linear Kernal. Among ten ML evaluated models, three top-performing models are selected to make predictions for their stability energy. A Convex Hull Distribution (CHD) is constructed to elucidate the relationship for their stability and structural correlations. The main finding of this study reveals its strong correlation between stability and its related structural descriptors, particularly Bertz Branching Index" corrected for the number of Terminal atoms (BertzCT), Partial Equalization of Orbital Electronegativities-Van der Waals Surface Area with 14 bins (PEOE_VSA14), and First-Order Connectivity Index (chi 1). The analysis demon strates that the current ML models can effectively predict the stability of such materials to enable their rapid screening. Their calculations can provide a framework to understand their complex relationships between their material properties, structure, and stability.
dc.description.sponsorshipFunding agency : Taif University Grant number : TU-DSPP-2024-76
dc.identifier.citationGüleryüz, C., Sumrra, S. H., Hassan, A. U., Mohyuddin, A., Elnaggar, A. Y., & Noreen, S. (2025). A machine learning analysis to predict the stability driven structural correlations of selenium-based compounds as surface enhanced materials. Materials Chemistry and Physics, 339, 130786.
dc.identifier.doi10.1016/j.matchemphys.2025.130786
dc.identifier.issn0254-0584
dc.identifier.scopus2-s2.0-105000983997
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5776
dc.identifier.volume339
dc.identifier.wosWOS:001458109300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.institutionauthorGüleryüz, Cihat
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofMaterials Chemistry and Physics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectConvex hull diagram
dc.subjectMachine learning
dc.subjectSelenides
dc.subjectStability
dc.subjectStructural correlations
dc.subjectSurface-enhanced materials
dc.titleA machine learning analysis to predict the stability driven structural correlations of selenium-based compounds as surface enhanced materials
dc.typeArticle

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