Using the LASSO algorithm, the optimal number of descriptors for predicting d is 35 per bilayer. We summarize these descriptors in the Supporting Information. We summarize the MSE, MARE and R 2 values for the predictions of each ML model in Tables 2 for both bilayer representations BR1 and BR2. Comparing the MSE and MARE values of the test sets, it is clear that all ML models exhibit very similar accuracy in predicting the properties of materials in the test set not used to train the models. The MSE and MARE% are similar for most models and for both classes of descriptors, BR1 and BR2. The RVM model using the BR1 descriptors is slightly less accurate in predicting the properties of the test set than the other three models. The SVM model using BR2 descriptors gives the lowest prediction accuracy, probably because it was overfitted. The NN model makes slightly better predictions, although the differences between all models are small. The relatively large difference between the training and test set accuracies suggest that the SVM model is overfitted, a phenomenon that has been noted by others. Figure displays the correlation between the predicted and the DFT-calculated d values for the bilayers in the test set using the four ML models trained on BR1 descriptors. Figure summarizes the average and standard deviation of the MARE values for bilayers, as a function of their component monolayers, for the four ML models training using BR1 descriptors. An interesting feature in Figure is that the presence of some component monolayers in bilayers leads to high d prediction errors, irrespective of the ML model used. For example, CdO and TiTe 2 result in the largest prediction errors in all four ML models. Similarly, ReS 2 and PdTe 2 are common higher prediction error components in the RF, SVM and RVM models.