One possibility to evade this bottleneck is to construct analytical, parametrized models of the potential energy surfaces. Most prominently, large advances towards this goal have been realized using machine learning techniques. Applied to electronically excited states, on-the-fly computed machine-learning potentials have both enabled nanosecond-timescale nonadiabatic dynamics simulations and screening of large compounds classes of organic molecules. For excited-state properties of transition-metal complexes, machine-learning approaches so far are scarce and lacking behind. In this light, the combination of potentials parametrized using the vibronic coupling model and trajectory surface hopping (SH) for nonadiabatic molecular dynamics simulations has become an affordable pathway to investigate photodynamics, in particular of transition-metal complexes. In vibronic coupling theory, the potential energy surfaces are expressed in Taylor expansion around a reference geometry in a basis of diabatic electronic states and a set of nuclear coordinates, for which typically the harmonic normal modes of the reference geometry are used. Using a linear vibronic coupling (LVC) approach, where only first-order terms are included, the model can be parametrized from a single-point calculation including gradients, non-adiabatic couplings and spin-orbit couplings. Alternatively, parameters can be obtained from finitedisplacements around the reference geometry along each normal mode , amounting to ∼ 6N single-point calculations. Notably, this is much less effort than usually required to train machinelearning models to predict potential energy surfaces. While the usage of harmonic normal modes and the truncation to first-order constrain the LVC model to predict meaningful potential energy surfaces only in vicinity of its reference geometry, the rigid structure of many transition-metal complexes confines their motion in photophysical processes close to its starting area, making LVC model a useful approximation in the study of excited-state dynamics of transition-metal complexes.