Prediction of Poisson's Ratio vs Average Ionic Radius for Various Elements of the Periodic Table Using Machine Learning

Understanding the mechanical properties of materials is crucial for engineering applications. Poisson's ratio., which measures a material's lateral strain response to uniaxial stress., and the average ionic radius., defining the distance between an ion's nucleus and its outermost shell., are key parameters for material characterization. This study applies machine learning techniques to predict Poisson's ratio based on the average ionic radius across various elements. The research aims to develop a predictive model that facilitates material selection processes by leveraging a dataset of elemental properties. Poisson's ratio., ranging from -1 to Y2 for stable isotropic materials., reflects a material's elastic deformation behavior. The average ionic radius significantly influences ionic interactions., solvation., and system thermodynamics., highlighting its relevance in understanding material properties. The findings aim to bridge the gap between ionic characteristics and mechanical behavior., offering insights into advanced material design and selection.