A machine learning analysis to predict the stability driven structural correlations of selenium-based compounds as surface enhanced materials
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Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The 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.
Açıklama
Anahtar Kelimeler
Convex hull diagram, Machine learning, Selenides, Stability, Structural correlations, Surface-enhanced materials
Kaynak
Materials Chemistry and Physics
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
339
Sayı
Künye
Güleryüz, 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.
