(eq. 4), where 𝑑 … are the descriptor components selected from the many billions of candidates, and 𝑐 … are the fitting coefficients. Importantly, only few primary features, out of the fifty offered ones, appear in the finally selected descriptor. The SISSO-derived descriptors are interpretable in the sense that one can identify the key primary features by simply inspecting the output expressions. These primary features are the relevant "catalyst genes". Because the functional forms of the immensity of descriptor candidates offered to the SISSO analysis are very flexible, it is important to avoid overfitting, i.e., to avoid models that fit the provided data but are not generalizable. Two parameters control the model complexity: 𝐷 (see Eq. 4) and the number of times the mathematical operators are iteratively applied to the features (depth of symbolic-regression tree) in order to generate the descriptor candidates, called hereafter rung, and denoted by 𝑞. The model complexity is assessed using leave-one-material-out cross-validation (CV). This CV procedure consists of training models with a data set in which one of the catalysts is removed, and then using the so-obtained ensemble of best models to predict the property of the left-out material. This procedure is iterated until all the catalysts are left-out once. The root mean squared errors (RMSEs) averaged over all CV iterations (averaged CV-RMSEs) are used as our performance metric. The optimal complexity is considered the one with the lowest CV-RMSE. Further details on the CV procedure are provided in ESI. In this work, we use the multi-task version of SISSO (MT-SISSO). 14 In the context of SISSO, multi-task refers to a transfer-learning approach for the identification of a single multi-dimensional descriptor for a target property across different material classes or external conditions, each of them corresponding to different fitting coefficients (𝑐 … in Eq. 4). MT-SISSO thus provides a single descriptor for the property and different models for each class of materials or external condition. In the case of this work, such different external conditions correspond to the different reaction temperatures applied in the catalyst test. Predicting the target property at each of the measured temperatures is therefore a different task. We stress that, in addition to allowing for the simultaneous modelling of the catalytic performance at different temperatures, MT-SISSO also enables us to efficiently exploit the experimental data available, increasing the effective number of data points. This is because every material is measured in a large range of temperatures, providing multiple data points per material. The multi-task approach thus improves the reliability of the identified descriptors. In spite of the application of MT-SISSO, the number of data points in our experimental data set is very small and by no means comparable to the amount of data needed for widely used machine-learning approaches as, e.g., kernel ridge regression or artificial neural networks. The latter typically requires >10 3 data points. However, we stress that for the SISSO approach a large amount of physicochemical information about the considered materials is provided by the immense number of descriptor candidates considered, with their quantitative values. The AI strategy of SISSO enables the identification of descriptors that capture the intricate underlying processes without the need for a large amount of experimentally characterized materials. The bigdata aspect is thus in the intricacy of the materials functions as the signal to be reconstructed (using the language of compressed sensing). It does not focus on the number of materials (or observations). We nevertheless point out that the more experimental data are available, the more generalizable the SISSO-derived models will be. Obviously, SISSO can only capture processes that are governing the target properties in the employed experimental data.