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<p>This study presents a comprehensive graph-theoretic and computational analysis of Quantitative Structure–Activity Relationship (QSAR) molecular descriptors for Single Chain Diamond Silicates (CSn), a crucial class of silicate structures defined by their unique connectivity of SiO₄ tetrahedra. Various molecular descriptors, including the Atom Bond Connectivity (ABC) Index, Atom Bond Sum Connectivity (ABS) Index, Augmented Zagreb Index (AZI), Sum Augmented Zagreb Index (SZI), Geometric Arithmetic Index (GAI), and Arithmetic Geometric Index (AGI), are examined to assess their structural, electronic, and thermodynamic properties. Through mathematical formulations and computational modelling, this study quantifies the complexity, stability, and connectivity patterns of CSn, enhancing the predictive capabilities of QSAR models. The findings underscore the significance of molecular descriptors in characterising silicate networks, with applications spanning materials science, catalysis, and geochemistry.</p>

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Graph-theoretic and computational analysis of QSAR molecular descriptors for single chain diamond silicates

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月8日

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  • S. Mohankumar,
  • P. Sivakumar,
  • S. Priyatharshni

摘要

This study presents a comprehensive graph-theoretic and computational analysis of Quantitative Structure–Activity Relationship (QSAR) molecular descriptors for Single Chain Diamond Silicates (CSn), a crucial class of silicate structures defined by their unique connectivity of SiO₄ tetrahedra. Various molecular descriptors, including the Atom Bond Connectivity (ABC) Index, Atom Bond Sum Connectivity (ABS) Index, Augmented Zagreb Index (AZI), Sum Augmented Zagreb Index (SZI), Geometric Arithmetic Index (GAI), and Arithmetic Geometric Index (AGI), are examined to assess their structural, electronic, and thermodynamic properties. Through mathematical formulations and computational modelling, this study quantifies the complexity, stability, and connectivity patterns of CSn, enhancing the predictive capabilities of QSAR models. The findings underscore the significance of molecular descriptors in characterising silicate networks, with applications spanning materials science, catalysis, and geochemistry.