The high training set accuracies of the SVM model suggest that it could be overfitted like the interlayer distance model, although the test set predictions are similar to those of the RF and NN models. However, we must still be cautious using the band gap models that are based on a small data set, as they will not perform as well for bilayers that are well outside the domain of applicability of the model (defined by the training data). Figure displays the correlation between the values of the band gap obtained by DFT and those obtained by each of the four ML models. The SVM and RVM greatly underestimate the band gap of boron nitride|MoS 2 , which is 1.867 eV by 0.6 eV and 0.7 eV, respectively. RF and NN, on the contrary, predict it within an error of 0.3 eV and 0.2 eV, respectively. Using ML models trained using the BR1 representation, we predicted the band gaps for all of the possible 20 × 21/2 = 210 bilayers, based (summarized in Table ). The NN, RVM and SVM all predict small negative values (-0.15 eV) for the minimum band gap, which is unphysical. These are most likely represent metallic bilayers and are probably due to extrapolation errors in the models. The BN bilayer is consistently predicted by the NN, RVM and SVM models to have the largest band gap, but the RF model predicts a substantially lower band gap. The distribution of the predicted band gaps varies significantly across the ML model, as can be seen in the percentile values. Unlike the interlayer distance predictions in Table , where very similar values were seen across the models, the larger standard deviation in Table compared can be attributed to the small size of the data set that was used to train the models for band gap prediction.