In the context of dynamics simulations, most successful is the linear vibronic coupling (LVC) model, which builds on the availability of gradients on the PES. While being standard for TD-DFT, computations of gradients at the BSE@GW level of theory remain scarce and are restricted to the numerical determination. Furthermore, to our knowledge there exists no straightforward and easy-to-use implementation of numerical gradients at the BSE@GW level of theory. This work aims at filling this gap, yielding a LVC model that paves the way to perform TSH or full quantum mechanical dynamics simulations in the form of MCTDH. The multi-configuration time-dependent Hartree (MCTDH) method has proven a ver-satile approach to high-dimensional quantum dynamics. Introduced in 1990 by Meyer and coworkers there have been numerous applications, e.g., to nonadiabatic electronvibrational dynamics, including transition metal compounds, excitation energy transfer in pigment-protein complexes, but also to vibrational dynamics (for an overview, see also Ref. 41). It should be noted that for efficiency MCTDH requires to have at hand a Hamiltonian in the form of a sum of products. Therefore, the LVC model discussed above is ideally suited for the combination with MCTDH dynamics. A boost concerning the dimensionality came with the multi-layer extension (ML-MCTDH) introduced by Wang and Thoss and later generalized in Refs. 43,44. While in MCTDH the wave packets are expanded into Hartree products of single-particle functions (SPFs), in ML-MCTDH the idea is to combine strongly correlated DOFs into multidimensional particles, which in turn are expanded in MCTDH form. This is repeated until one-or low-dimensional particles are obtained. The resulting wave packet has a tree-like (ML-tree) structure that needs to be converged with respect to the number of SPFs assigned to the branches. This results in the challenge to find cost-efficient ML-trees, since the numerical performance in terms of CPU time can vary vastly for different trees. While there are simple guidelines for combining modes (e.g. similar frequencies), standardized routines for the systematic generation of reliable ML-tree structures remain scarce. Recently, Mendive-Tapia et al. proposed to use hierarchical clustering for generating ML-trees, focusing on vibrational dynamics. In this work we will present a method based on spectral clustering, 47 yielding ML-trees for nonadiabatic electron-vibrational dynamics.