The MOF with the highest number of AL iterations of 1882, initially had a GP mean uncertainty of 0.142 mol/kg and an R 2 value of 0.68 in the first iteration. Over subsequent iterations, it achieved a final GP The GPR model with the input features of fugacity, effective epsilon, effective sigma, bond length, and charge is used to predict the target variable of adsorption loading, after the model has been trained with some initial training data. The test data set (the test input features) with the highest predicted GP uncertainty is fed into the MLP model and the ground-truth is computed. This point is added to the initial training data and the model is retrained and the predictions on the test dataset is done again. This is done iteratively until the GP mean uncertainty is less than 0.05 mol/kg. This procedure is conducted independently in each of the 1800 MOFs. Prior as used in this figure is the initial training data. mean uncertainty of 0.049 mol/kg, accompanied by an R 2 value of 0.99. Conversely, the MOF with the fewest AL iterations, only 106, started with a higher initial GP mean uncertainty of 0.496 mol/kg and a lower R 2 value of 0.46. However, it also reached a final R 2 value of 0.99 at a GP mean uncertainty of 0.049 mol/kg. Notably, fluctuations in the GP mean uncertainty (represented by the blue line) closely corresponded to fluctuations in the R 2 value (represented by the orange line). These fluctuations highlight the correlation between GP mean uncertainty and R 2 values, emphasizing the impact of iterative data inclusion on model performance.