To obtain the best set of hyperparameters for each model, we conduct a 5-fold grid search cross-validation on the training set. The best set of hyperparameters is then used to train and evaluate the performance of the models on the test set. The SVR model obtained the best accuracy (MAE 11.675 GPa) compared to the other models in predicting bulk modulus (K), as shown in Figure (a). The prediction error bars for the other models, RF, KRR, MLP, GBM, GPR, and SVR, were within ~0.5 GPa except for LR and KNN. This suggests that traditional ML models may have reached a point of saturation in terms of predictability. The SHAP predicted feature importance of the SVR model (Fig. ) reveales that density and formation energy per atom are the two most important features. These are global attributes that directly affect the physical properties and stability of the material. Invariant structural attributes, such as "F2", "F5", and "F0", were also seemingly important for the prediction of the bulk modulus. A similar trend in feature importance can be observed in the other equally performing models as well (see supplementary Fig. ). From the training error shown in Figure (a), it also seems that the RF, KRR, and SVR models tend to overfit the data slightly.