Using this threshold, we label H-matrix elements as 0 or 1 to create a binary dataset that can be used in machine learning classification. The features (X) for this classification prob- lem are the quantum numbers of the 1-mode basis in bra and ket, ⟨n ′ 1 , n ′ 2 , ..., n ′ nmode | and |n 1 , n 2 , ..., n nmode ⟩. We investigate three widely used classification algorithms on the water H-matrix: random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP). All ML models were trained using scikit-learn package. First consider the smallest H-matrix (of order 161) for water, with distribution of elements shown in Fig. . There are 13 041 matrix elements. These are randomly split into 40% training and 60% testing. For MLP and SVM models, the feature X is standardized to zero mean and unit variance by using the StandardScaler from scikit-learn, while for the RF model, we did not scale the features because it is invariant to feature scaling. The RF uses 20 estimators (also known as "decision trees") with maximum depth of 10. The MLP model uses two hidden layers with 15 neurons in each, and 'relu' as the activation function. Moving on to much larger H-matrices, consider 4-mode vinyl using a saddle-point normalmode basis and the B 2 -symmetry block of the 10-mode glycine. The respective H-matrices are order 15 552 and 8 828. Thus there are tens or hundreds of millions of matrix elements, and for vinyl each matrix element requires 4D quadrature. Note, for glycine, although we included 10 normal modes in the basis, we used 4-mode representation of the potential so the highest dimension for the numerical quadrature of potential matrix elements is still 4D.