The point in the testing dataset with the maximum GP uncertainty was identified and fed into the Gómez-Gualdrón and coworkers MLP model to compute the considered ground truth adsorption value, as it earlier proved to have accurate correlations with adsorption from GCMC simulations . Adsorbate and fugacity combinations continued to be added iteratively to the training data until the mean predicted uncertainty of the GP was under 0.05 mol/kg. Once the threshold was met, the final GP model was utilized to predict the adsorption in the testing dataset. For each MOF, the entire testing dataset is inputted into the MLP model to generate the MLP ground truth adsorption values for comparisons with the GP predicted adsorption. We can use the MLP model to generate the ground truth data because of the high accuracy of the model when compared to GCMC simulations. We assessed the GP model performance by calculating the R 2 between the "MLP ground truth" and the GP model predictions. Figure presents the evolution of R 2 and GP model uncertainty for the two most extreme cases within the 1800 MOFs. The one that required the most AL iterations to reach the 0.05 mol/kg (Figure ), and the one that required the fewest iterations (Figure ).