We defined a variety of descriptors for the bare active site that should influence the energy of the reactive steps, e.g., see Eqs. 6-7 and Figure . However, it is not possible to determine which of these parameters is directly related to the activity of a site by simple linear fits. Therefore, we used a non-linear ML model (the RF algorithm) to look for correlations. Although the QM simulations in this work represent a considerable computational effort, the data set is too small for quantitative predictions with ML. Thus, we limit our analysis to the determination of the most important descriptors, so that this information can be used in the future to create quantitative models. The RMSE results of 100 random trials on 41 C I structures and the subsequent feature importance analysis revealed that the ϕ ij angle, which is a proxy for the available volume in a pocket next to the metal center (see Eq. 7), is the most important descriptor for ∆E a and ∆E tot ads . However, we need a non-linear combination of three parameters for ∆E a (see Figure ) and two parameters for ∆E tot ads (see Figure in the SI) to obtain the best fit to the ground truth. The average RMSE for these fits is rather encouraging for such a small data set (about 1.5 Kcal/mol for ∆E a and 2 Kcal/mol for ∆E tot ads ) and it can be significantly reduced with larger training sets.