The logarithms of the adsorbate features were used as input to the GP model, except in the case of charge and bond-length. All features underwent z-score standardization before being inputted into the GP model, which utilized a rational quadratic (RQ) kernel to perform the regression. At each AL iteration, the GP model trained (using the initial training dataset) for each MOF was used to compute the GP mean uncertainty for each prediction, which is a direct output of the GP model.