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Predictive modeling of copper iodide properties using graph-theoretical descriptors.

Hafiz Muhammad Fraz1, Kashif Ali1, Muhammad Faisal Nadeem1

  • 1Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan.

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|November 13, 2025
PubMed
Summary

This study uses graph theory to analyze copper iodide (CuI) structure and properties. It models relationships between topological indices and physicochemical properties for material design.

Keywords:
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Area of Science:

  • Inorganic Chemistry
  • Materials Science
  • Computational Chemistry

Background:

  • Metal halides are crucial in various applications.
  • Copper iodide (CuI) is a versatile metal halide used in synthesis, semiconductors, catalysis, and cloud seeding.
  • Understanding CuI's structure-property relationships is key for optimizing its applications.

Purpose of the Study:

  • To analyze the structural properties of copper iodide (CuI) using graph-theoretical approaches.
  • To develop regression models correlating topological indices with CuI's physicochemical properties.
  • To visualize these structure-property relationships through graphical representations.

Main Methods:

  • Application of degree-based topological indices (Zagreb, Randić, harmonic, Sombor, atom-bond connectivity).
  • Development of regression models to link topological indices with heat of formation, molecular weight, and density.
  • Utilizing graphical methods (line plots, bar charts, violin plots, heatmaps) for data visualization.

Main Results:

  • Established quantitative structure-property relationships for copper iodide.
  • Identified key topological indices influencing CuI's physicochemical characteristics.
  • Demonstrated the utility of graph theory in predicting material properties.

Conclusions:

  • Graph-theoretical analysis provides valuable insights into copper iodide's structure-property correlations.
  • The developed models can aid in material design and optimization for CuI's diverse technological applications.
  • This computational approach offers a pathway for predicting and tailoring material properties in inorganic compounds.