With the full LVC Hamiltonian at hand, TDH dynamics simulations were performed for 2000 fs with the dynamics being triggered by a field according to Eq. (10). The field was assumed to be polarized along the direction of the y-component of the dipole (direction with largest transition dipole) and resonant to the strongest transition (ω = 2.34 eV). Furthermore, we used t 0 = 100 fs, σ = 24 fs, and E 0 = 0.001 hartree/bohr. These values gave at ground state depopulation of about 27%. Since our focus is on the ML-tree generation, a model of reduced dimensionality has been selected. Specifically, we included only those modes with a maximum absolute coordinate expectation value larger than 0.15. This resulted in a 63-dimensional model with 755 and 3895 terms for κ and λ, respectively. The population dynamics of this model is qualitatively similar to that of the full-dimensional case. The obtained adjacency matrix, Eq. ( ), is shown in Fig. . Normal Mode Index ). This occurs because the likelihood increases that, at a specific node, the clustering algorithm groups a small set of normal modes into one branch, while passing the majority of normal modes further down to the next layer. This way a small threshold in the adjacency matrix may be seen as a tool to deepen the ML-tree by choice. However, increasing the threshold could lead to a situation where most entries of the adjacency matrix are located around the diagonal. This may aid the spectral clustering algorithm in identifying highly correlated pairs and thus reducing the depth of the ML-tree (cf. ESI † Fig. ). For the present models (I) to (IV) we have 10, 11, 18, and 17 layers, respectively. In general, having deeper ML-trees could be desirable as related research has shown that this may lead to a lower computational effort. However, at the same time, this could increase the number of SPFs, which may counterbalance the positive effect on efficiency. The remaining tree (V) was chosen such that the full adjacency matrix was divided into clusters of different sizes.