In the static DFT model, the studied structure was obtained at 0 K, which represents the bottom of the potential energy surface, while in this QM/MM MD study not only the average over all conformations is taken into account, but also the thermal energy, so the structures are not 0 K structures and therefore no minima on the potential energy surface. In addition, we assume that the product can undergo slow relaxation to lower energy conformations, which are too slow to observe in the timescales investigated here and which further contribute to the difference in reaction energies decreasing the variance between the static and the dynamic approach. Besides energetics, the reaction profile allowed for the categorization of the structures into three ensembles, namely educt, transition state, and product. This step was a key in improving the reaction coordinate recognition, as it allowed for the use of supervised learning methods to reduce the coordinate space. Due to the large energy difference between reactants and products, which renders the transformation irreversible in the context of unbiased MD at 300K, we sample a single chemical reaction per trajectory. Thus, there is one transition state structure before the large drop in energy and as we have not biased our simulation, this structure should be on the dividing surface. Naturally, structures very similar to the TS are likely to be found in the sampling leading up to the identified TS due to recrossing events. Such TS and TS-like structures could probably be identified using a structural criterion and, in an iterative process, be remove them from the educt ensemble. However, we are able to observe a good separation between TS and reactant ensembles, thus indicating that there is no large TS structure contamination within the reactant ensemble.