In this work, we employ an invariant Smooth Overlap of Atomic position (SOAP) as the representation of the geometric attributes, with the features denoted with an 'F' and subscript. To demonstrate, the correlation between these features and the bulk and shear modulus, we use PCA representation of SOAP feature vectors and mark possible super hard structures in 2D PCA space, as shown in Fig. ). These structures seem to form a clustered ridge in the PCA space, an indication to share geometric similarities. To better understand the relationship between the structural attributes (represented by a 24-dimensional SOAP feature vector) and the target variables (shear and bulk moduli), we computed the Pearson correlation coefficient (Fig. ), which ranges from -1 to 1 and indicates both the strength and direction of the relationship. Our analysis reveals a clear correlation between the G and K moduli and the SOAP feature vector, highlighting the importance of these structural attributes in predicting material properties. Interestingly, we found that the correlation between the SOAP vector and the shear modulus was stronger than that for the bulk modulus. This can be explained by the high directional dependence of the shear modulus in contrast to the bulk modulus, which has a more volumetric dependence.