Enhancing topological index of calcium chloride network through feature selection methods exploration

Abstract

With the chemical formula CaCl 2 , calcium chloride is a salt as well as an inorganic material. At room temperature, it has the consistency of a white, crystalline solid and is very water-soluble. It can be created by neutralizing calcium hydroxide with hydrochloric acid. Calcium chloride is a solution with a large enthalpy change. It is extensively utilized in research facilities, manufacturing facilities, and pharmaceuticals, including all types of food-graded applications, the treatment of acute illnesses, packaging for drying tubes, dust controllers, and de-icing, among other uses. In this paper, firstly we compute the topological indices, coindices, and reverse indices of CaCl 2 . Further, we employ machine learning strategies to capture the best suitable set of indices for the proximity of the prediction of distinct physio-chemical properties of CaCl 2 . To strengthen the results, different regression techniques are implemented to predict HOF of CaCl 2 based on our features, and the most influential features were detected to verify our results.