In order to identify correlations between physicochemical parameters and the catalyst performance, we apply the SISSO approach as implemented in the SISSO++ code. SISSO is tailored to the typical scenario in materials science and heterogeneous-catalysis research, in which the number of characterized materials is small and the intricacy of underlying processes is high. SISSO identifies models as typically nonlinear analytical equations. From the model expressions, the key parameters emerge, enabling insights into the underlying processes. As such, SISSO determines interpretable descriptors. The SISSO approach is based in three steps. (i) Firstly, a set of input quantities, termed primary features, is chosen (Table and). These parameters characterize the materials in the data set and the potentially relevant processes that govern their performance. (ii) Secondly, mathematical operators (e.g., subtraction, multiplication, logarithm) are applied to these primary features and then again and again to resulting functions. As a result, an immense number of expressions -up to trillions -is generated, each of them describing a different possibly intricate interplay of underlying processes for each material of the data set. (iii) Then, SISSO identifies the few expressions, typically < 4, which combined by weighting coefficients, best correlate with a chosen target performance of interest & (e.g., a material function) for the samples in the training set. The result is a model for the target of the form