This is a reasonable approximation given that the temperature at which the materials were calcined is relatively low and thus the B and B' species are expected to be randomly distributed across the material. The ? @A for the 19 additional materials are shown in Figure as circles. The 3 ABO3, 12 AMn(1-x)CuxO3, and 4 AMn0.7B'0.3O3 perovskites are displayed in red, blue, and green, respectively. The activity of these materials is not well captured by 7 , <9=> or RS , (Figure 1A and 1B). Indeed, the ? @A values for the substituted AMn(1-x)B'xO3 materials do not follow the ? @A vs. RS , trend that was apparent from the analysis of ABO3 materials (Figure , blue and green circles). However, the SISSO descriptor of Eq. 3 does capture the ? @A values of the additional 19 perovskites with overall good accuracy (circles in Figure ). Interestingly, the high activity of SmCoO3 and NdCoO3 perovskites, which display ? @A values lower than any material of the training set (105 and 113°C, respectively), is well captured by Eq. 3. The absolute prediction errors for SmCoO3 and NdCoO3 are 19 and 2 °C, respectively. These results show that the descriptor identified based on the 13 ABO3 materials is valid beyond the materials space used for training and it can correctly describe highly active materials. We analyzed the prediction errors (Table ) to understand when the SISSO model might fail. The mean absolute prediction error evaluated on the 19 materials is 45 °C. However, the absolute prediction errors for PrMn0.7Zn0.3O3 and LaMn0.6Cu0.4O3 are 165 and 102 °C, respectively. These materials contain the elements Zn and Cu, which were not present in any material of the training set. Thus, the relatively high errors could be associated to the significantly different physical properties of these two elements compared to the elements constituting the materials of the training set. For instance, Zn displays the highest Ts values among all elements considered in this work (Table ). This results in a descriptor value that is higher than the descriptor values of the training set (Figure ). Additionally, the underlying processes governing the reactivity of these two materials could be different compared to processes dictating the activity in the training set due to the limited tendency of Cu and Zn to change the oxidation state and the resulting deviating redox properties. Indeed, Eq. 3 is expected to be valid for new materials as long as the reactivity of the examined compounds is governed by the same processes controlling the catalytic performance of the perovskites in the training set. In order to improve the domain of applicability of the SISSO model and cover larger portions of the materials space with high accuracy, the SISSO modelling can be combined with systematic data acquisition in a sequential-learning setting. A SISSO-based sequential-learning approach will be discussed in an upcoming study. Finally, we note that the parameters used as primary features in this work can be measured by relatively simple, widely accessible characterization techniques such as XRD or XPS. However, these parameters only reflect the thermodynamic properties of the materials in their precursor state (ex situ). Our target ? @A , in turn, is obtained from a kinetic measurement. While our thermodynamic parameters are able to correctly describe ? @A under fixed reaction conditions, e.g., feed composition, in this study, the inclusion of primary features that capture the interaction of the material with the reaction environment, e.g., by using in situ spectroscopy, might be crucial for the description of the reactivity in other systems or for systems that operate under significantly different environments, e.g., chemical potentials of the reaction mixture.