The SISSO approach identifies (nonlinear) analytical expressions that describe a given data set. Starting from the candidate descriptive parameters, SISSO builds an immense pool of analytical expressions (e.g., containing billions of expressions) by iteratively applying mathematical operators such as addition, subtraction, exponential, and more to the primary features and to previously generated expressions. Then, by compressed sensing SISSO identifies the few expressions that, combined by weighting coefficients, best correlate with a given target of interest, here the selectivity. Typically, only few key descriptive parameters, out of all initially offered candidates, appear in the models identified by SISSO. The predictive performance and optimal complexity of the SISSO models were assessed by a nested five-fold cross-validation scheme. By determining SISSO model hyperparameters and thus the model complexity via cross validation, we prevent the identification of models that fit well the training set but which are not generalizable (overfitting). Additional details on the SGD and SISSO approaches are described in ESI and in references .