Upon 6,000 iterations, the final training dataset selected by AL consists of 6004 data points. The evolution of the average R 2 and GP mean uncertainty as a function of iteration is shown in Figure . Initially, the model had a GP mean uncertainty of 0.87 mol/kg and a low R 2 of 0.1, which were substantially improved to a final average GP mean uncertainty of 0.27 mol/kg and an R 2 of 0.94. Figure shows the number of MOFs used by the AL as the iterations increase, which by the 6000 th iteration corresponds to 893 MOFs. Using just 0.11% of the data, encompassing only 26% of the MOFs in the database, we constructed a GP-PCA model with an R 2 of 0.94. This kind of data savings are extremely encouraging for future exploration of datasets including a larger variety of adsorbate types. Notice that while we showed the possibility of training a suitable adsorption model using only 6,004 datapoints, AL is directly responsible for that outcome. To illustrate this point, we fit GPs to 100 randomly selected training data sets, each with 6,004 datapoints. Figure shows the significant variability in the average R 2 and GP mean uncertainty for these 100 randomly selected training datasets. The lowest achieved R 2 was 0.75, coupled with a GP mean uncertainty of 0.368 mol/kg. Conversely, the highest R 2 obtained was 0.91, accompanied by a GP mean uncertainty of 0.282 mol/kg. On average, across all 100 randomly selected training datasets, the average R 2 was 0.84, while the GP mean uncertainty averaged 0.312 mol/kg, which are worse than the R 2 of 0.94 and GP mean uncertainty of 0.27 mol/kg attained by the AL-selected dataset (star symbol in Figure ).