Predictive modeling of copper iodide properties using graph-theoretical descriptors

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.