Distributions of the geometrical and mechanical properties were generated through kernel density estimator using the Gaussian function as implemented in the scikit-learn, a machine-learning library in Python. The bandwidth was adjusted by dividing the differences between the maximum and minimum values by 15. Moreover, principal component analysis (PCA) was used to reduce the dimensionality of the geometrical/mechanical features into two principal features. The features were firstly scaled using the StandardScaler in scikit-learn.