From the results in section 3.2, some features of the active site emerge that are clearly related to the reaction barrier, e.g., large available volume leads to favorable adsorption of the reactive species close to the metal center. However, multiple TS configurations within the same pocket indicate that other geometrical features must be at play that affect the reactivity. In this section, we use machine learning (ML) to try to identify the features that most closely correlate with the activation energy ∆E a and the total adsorption energy ∆E tot ads . The goal is not to get an accurate predictive model of the activation energy (although that is desirable in order to evaluate effective reaction rates in catalytic cycles, see Eq. 1) because our test set is too small for the proper training of the ML model. Rather, we aim for the more contained goal of determining the most relevant descriptors that can allow us in future studies to create the training set for quantitative k ef f predictions.