Predictive modeling of copper iodide properties using graph-theoretical descriptors

Abstract

Metal halides, inorganic compounds formed through the combination of metals and halogens, play a significant role in diverse applications. Copper iodide (CuI, Marshite) stands out as a particularly versatile metal halide with important uses in organic synthesis, semiconductor technology, catalytic processes, and atmospheric modification through cloud seeding. This study employs graph-theoretical approaches to analyze the structural properties of CuI, focusing on degree-based topological indices such as the Zagreb indices, Randić index, harmonic index, Sombor index, and atom-bond connectivity index. We develop regression models to establish relationships between these topological indices and key physicochemical properties of CuI, including heat of formation, molecular weight, and density. The study further presents comprehensive graphical representations through line plots, bar charts, violin plots, and heatmaps to visualize trends and correlations in these properties. Our computational approach provides valuable insights into the structure-property relationships of CuI, offering potential applications in material design and optimization for its various technological uses.