ArticleScientific reports2024
On topological indices and entropy measures of beryllonitrene network via logarithmic regression model.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Comparative entropy analysis of 2D transition metal tetrahydroxyquinones via machine learning approaches.Scientific reports · 2026Article
- Face reverse degree topological analysis of TP-COFs, existence of isentropic COFs and spectral characteristics.Frontiers in chemistry · 2026Article
- Exploring entropy measures in polymer graphs using logarithmic regression model.Scientific reports · 2025Article
- Quantitative structure-property relationship modeling and ranking of necrotizing fasciitis drugs via degree-based topological indices.Frontiers in chemistry · 2025Article
- Predictive modelling and ranking:Frontiers in chemistry · 2025Article
- Analyzing boron oxide networks through Shannon entropy and Pearson correlation coefficient.Scientific reports · 2024Article
- Exploring the properties of antituberculosis drugs through QSPR graph models and domination-based topological descriptors.Scientific reports · 2024Article
- On analysis of silicon dioxide based on topological indices and entropy measure via regression model.Scientific reports · 2024Article
- On curve fitting between topological indices and Gibb's energy for semiconducting carbon nitrides network.Scientific reports · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chemical graph theory, a subfield of graph theory, is used to investigate chemical substances and their characteristics. Chemical graph analysis sheds light on the connection, symmetry, and reactivity of molecules. It supports chemical property prediction, research of molecular reactions, drug development, and understanding of molecular networks. A crucial part of computational chemistry is chemical graph theory, which helps researchers analyze and manipulate chemical structures using graph algorithms and mathematical models. Beryllonitrene , a compound of interest due to its potential applications in various fields, is examined through the lens of graph theory and mathematical modeling. The study involves the calculation and interpretation of topological indices and graph entropy measures, which provide valuable insights into the structural and energetic properties of Beryllonitrene's molecular graph. Logarithmic regression models are employed to establish correlations between these indices, entropy, and other relevant molecular attributes. The results contribute to a deeper understanding of Beryllonitrene's complex characteristics, facilitating its potential applications in diverse scientific and technological domains. In this study, degree-based topological indices
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