The SISSO approach starts with the collection of its input parameters, called primary features. These include all possibly relevant physicochemical parameters that may relate to the processes governing the catalysis question of interest. Concerning these parameters, it is better to offer many possibilities, and it does not matter if some of these "primary features" are correlated with others. Our choice of primary features for this work is given in Table . They correspond to the measured materials properties as well as reaction parameters such as the temperature. Altogether, these are fifty. In the second step, we construct the descriptor candidates. For this, the primary features are systematically combined using mathematical operators such as addition, multiplication, difference, etc. (see details in ESI). Thus, we follow a symbolic regression approach. This step results in the generation of billions of descriptor candidates. Each of them provides different numerical values for the different materials and/or processes. Thus, the big data challenge is related to the intricacy of the underlying physics and chemistry. From the large number of candidate descriptors, and using the provided values of the targets properties for the materials in the data set, SISSO selects very few, typically just 𝐷 = 1, or 2, or 3 best descriptor candidates, whose linear combination, with weighting coefficients, provides the best fit to the target property. 𝐷 is referred to as the descriptor dimension. The final selected descriptor is thus the vector containing the selected candidate descriptors as individual components. The selection of descriptors and the identification of the coefficients is done by compressed sensing. The resulting models for the target property 𝑃 have the form