To obtain a more reliable estimate of feature importance, the SHAP predicted feature importance from the top six best performing models, namely GPR, RF, KRR, MLP, SVR, and GBM, are combined with their overall performance. It is important to compare the performance of and feature importance for different models because a single model may overfit the data which leads to biased feature importance estimates, that do not generalize well for new data. For instance, RF, KRR, and SVR tend to overfit when predicting bulk modulus, while RF ad KRR tends to overfit when predicting shear modulus. Combining the results of multiple models can help produce more generalizable feature importance estimates. Additionally, since different models may have different biases or assumptions, combining their results can help obtain a more robust estimate of feature importance. Thus, the performance of the models measured on unseen data combined with the estimated feature importance is used to compute the overall importance, following the equation, 𝑜𝑣𝑒𝑟𝑎𝑙𝑙 𝑖𝑚𝑝𝑜𝑟𝑡𝑎𝑛𝑐𝑒 = .