Our workflow comprises of 4 section sections (i) The featurization and Initial feature section, (ii) benchmarking of select popular ML and deep learning models, (iii) evaluation of feature importance, and (iv) Ranking of the features. In the Featurization section, we start by extracting the dataset comprising crystal structures and bulk and shear modulus of the dataset from the materials project database based on section criteria (see dataset section). The extraction of useful features used for the prediction tasks was done primarily using two python-based libraries-Pymatgen 44 and materials data mining package Matminer . A total of 71 feature vectors categorized primarily across two major classes (i.e., local and global), (Fig. )