In order to determine what set of descriptors provides the most reliable fitting, we repeated the 100 random trial test for all possible combinations of one, two, etc., descriptors (up to all six), where the only constant is the angle ϕ ij . The results for the best combination in each descriptor space is shown in Figure , which reports the average RMSE and the standard deviation from the average. The best result among all of these combinations is obtained with a 3-descriptor set composed of ϕ ij , γ 0i and ω i , with average RMSE of 1.6±0.5 Kcal/mol and maximum RMSE of 2.8 Kcal/mol for the worst trial. Although the differences in average RMSE values across the combinations in Figure are relatively small (< 0.5 Kcal/mol), the standard deviations are considerably larger with one and two-descriptor sets (indicating too few descriptors) and with the six-descriptor set (indicating too many descriptors). These RMSE values would likely decrease with larger data set (both for the average and the standard deviation), but the relative performance across descriptor spaces could also change. Nonetheless, these results provide We performed an analogous analysis for ∆E tot ads , see Eq. 4, using the RF model and 100 random trials. The correlation plots for the best and worst trials, and for the Shapely feature importance analysis are reported in Figures S19-S20 of the SI. Here we only report the RMSE values in Table for the best and worst trial and for the average across the 100 trials, and discuss the summary of the analysis. The data in Table are similar to those in Table for the reaction barrier. The fit for the test set in the best trial for ∆E tot ads is somewhat larger than that for ∆E a by 0.1 Kcal/mol, and the worst result is larger for ∆E tot ads by 0.5 Kcal/mol. The average RMSE for the test set is also 0.5 Kcal/mol larger for ∆E tot ads than for ∆E a , which is likely due to the weak interaction of C 2 H 4 in the reactant cluster structures. Nonetheless, the results are of similar quality for the two energy quantities. Furthermore, Figure of the SI on the feature importance shows that the angle ϕ ij is still the most important descriptor. In the analysis for the best combination of descriptors over repeated 100 random trials for ∆E tot ads , the lowest average RMSE is obtained with a twodescriptor space including ϕ ij and q N b , shown in Figure of the SI. The importance of the Nb charge seems reasonable as this guides the binding of H 2 O 2 in the first place. However, contrary to the ∆E a , the standard deviation from the RMSE is large (about 1 Kcal/mol) for every descriptor space, again probably due to the labile binding of C 2 H 4 . Further studies with more data points are necessary to determine whether these errors can be reduced. Although the ML models provides insightful information about the main descriptors for the activation energy in this catalyzed reaction even with such a small data set, a key question remain on what makes a catalytic site more active than another (at least within the C I category used for the ML analysis). It is hard to tease out this kind of information from a nonlinear regression model of multiple descriptors. However, the feature importance analysis (see Figure ) clearly shows that ϕ ij is the most important descriptor and we can use it to obtain a qualitative understanding of the site activity. We plot the data of ∆E a vs ϕ ij in Figure . The linear fit is rather poor, which is to be expected as the best ML regression is based on a non-linear model. However, a clear trend is visible in the data: larger values of ϕ ij correspond to lower reaction barriers. The only two exceptions (with ϕ ij ≃ 155 ○ ) are sites with nearby silanol groups that produce steric hindrance around the active site. Therefore, in general, sites with larger available volume in the pocket around the metal center have lower reaction barrier.