The errors obtained when 𝑆 EICJ/KI EIKL is estimated using the MT-SISSO model trained on the whole data set, i.e., the training errors, decrease as the rung 𝑞 and the dimension 𝐷 increase (dashed lines in Fig. ). The training RMSEs are practically zero at 𝐷 = 3 for the three considered 𝑞. This evidences the flexibility of expressions selected by SISSO to fit the input data. The average CV-RMSEs (solid lines in Fig. ), however, do not decrease monotonically with rung and dimension. Instead, the average CV-RMSEs achieve a minimum value of 6.76% at 𝑞 = 3, 𝐷 = 2 with respect to an optimal predictability. This is therefore the identified appropriate complexity. The training error for such model is 1.46%. We note that a large fraction of the CV-RMSE is associated to the CV iteration in which the MoVTeNbOx catalyst is left-out. Since this catalyst achieves a much higher 𝑆 EICJ/KI EIKL compared to the remaining ones in the data set (Fig. and Fig. ), it is probably dominated by a different process and therefore it is hard to correctly predict its performance based only on the remaining materials. These primary features characterize electronic properties of the working catalysts, which can be related to the charge transfer from the catalyst to adsorbed reaction intermediates. 𝑥 e,EIN