In this work, we relied on a spectral clustering algorithm, in contrast to Ref. 46 which used hierarchical clustering. How the two methods compare in the context of ML-MCTDH efficiency will require further investigation. We prefer spectral clustering as it provides a means to directly control the size of the clusters, which will be important for the discussion below. In addition, it circumvents the problem of predefining a metric, which dictates the structure of the obtained dendrogram in hierarchical clustering. In short spectral clustering proceeds as follows. First, the symmetrized Laplacian is computed, for which the lowest k eigenvalues and eigenvectors are determined. Afterward, each row of the matrix, whose columns are the eigenvectors, is treated as a data point. Data points are grouped into k clusters using k-means. Given a clustering at the level of the full correlation matrix, the procedure is repeated until subclusters of either size one or two are obtained.