Open Access

ARTICLE

Topological Characterization and Predictive Modeling of Graph Energy in Ionic Covalent Organic Frameworks

Micheal Arockiaraj^1,*, Aravindan Maaran², C. I. Arokiya Doss²

1 Department of Mathematics, Loyola College, Chennai, 600034, India
2 Department of Mathematics, Loyola College, University of Madras, Chennai, 600034, India

* Corresponding Author: Micheal Arockiaraj. Email:

(This article belongs to the Special Issue: Computational Modeling and Simulation of Energy and Environmental Materials)

Computers, Materials & Continua 2025, 85(1), 637-655. https://doi.org/10.32604/cmc.2025.065674

Received 19 March 2025; Accepted 29 July 2025; Issue published 29 August 2025

Abstract

Covalent organic frameworks (COFs) are crystalline materials composed of covalently bonded organic ligands with chemically permeable structures. Their crystallization is achieved by balancing thermal reversibility with the dynamic nature of the frameworks. Ionic covalent organic frameworks (ICOFs) are a subclass that incorporates ions in positive, negative, or zwitterionic forms into the frameworks. In particular, spiroborate-derived linkages enhance both the structural diversity and functionality of ICOFs. Unlike electroneutral COFs, ICOFs can be tailored by adjusting the types and arrangements of ions, influencing their formation mechanisms and physical properties. This study focuses on analyzing the graph-based structural characteristics of ICOFs with spiroborate linkages. We compute graph based entropy using hybrid topological descriptors that capture both local and global structural patterns. Furthermore, statistical regression models are developed to predict graph energies of larger-dimensional ICOF structures based on these descriptors. To ensure the robustness and accuracy of our results, we validated our findings using a pseudocode algorithm specifically designed for computing degree-based topological indices. This computational validation confirms the consistency of the derived descriptors and supports their applicability in quantitative structure-property relationship (QSPR) modeling. Overall, this approach provides valuable insights for future applications in material design and property prediction within the framework of ICOFs.

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