For glycine, as explained in detail below, because the number of modes is larger than than 4, many matrix elements are known to be exactly zero and so not calculated. In these two cases, only 1% of the matrix elements are used to train the ML models. The MLP models for these two examples have three hidden layers of 20 neurons, while the RF models use 50 estimators with maximum depth of 25. For 4-mode vinyl that uses the saddle-point normalmode basis, the H-matrix is of order 15 552 and only the MLP classifier is used, since the random forest also becomes too expensive. Now we present the training and testing accuracy of the ML models on the five Hmatrices in Table . It can be seen that all ML models give accurate predictions for the labels, with an accuracy close to or greater than 90%. The random forest models slightly overfit the training set, indicated by the difference in the training and testing accuracy. Next we reconstruct the H-matrices using the predicted labels from various ML models.