where MSE is the mean square error, MARE is the mean absolute relative error (%), R 2 is the coefficient of determination, Y i are the original test set outcomes (in our case, the DFTcalculated interlayer distances for the bilayers), Ŷi are the predicted test set outcomes, and Y is the average of the original test set outcomes. The significance of R 2 is that it expresses the proportion of the variance in Ŷi that can be predicted from the descriptor vector, and is an important measure of the ML model quality. However, its values are dependent on the size of the data set, and therefore we adopt the three quantities together, R 2 , MARE and MSE, to gauge the accuracy. For the case of the band gap prediction, we do not use the MARE because some of the values obtained are zero. Thus, for each of the four models in this work, we train the model on 80% of the data set and use the remaining 20% as the test set. We construct the test set by applying the k-means clustering to the data set (the set of bilayers in BR2), which yields 6 clusters by the Silhouettes analysis. Then, we randomly choose 20% of each cluster and build our test set. In the NN model, the learning iteration stops when the MSE is below 0.03 (for BR1) and 0.05 (for BR2). Then we compare the accuracy of the models based on the R 2 , the MSE and MARE values.