In our initial tests, we only varied model hyperparameters over a limited range as a lowcost initial screen for the best model type (Supporting Information Tables ). All model types exhibited significant overfitting, likely due to the relatively small size of the dataset and the large number of features. The KRR model performs very poorly with a negative test set R 2 and mean absolute errors (MAEs) over 2.0 mmol/g for both the ethane and ethylene models and thus was not considered further. The RF performs moderately well on both adsorbates with test set MAE and R 2 of 0.9 mmol/g and 0.419, respectively, on the ethane uptake data, and test set MAE and R 2 of 0.8 mmol/g and 0.574 on the ethylene uptake data. Lastly, the ANN exhibits a sharp contrast in performance between the models for the two adsorbates. The ethylene ANN achieves a test set MAE of 0.7 mmol/g and R 2 of 0.638 while the ethane ANN MAE and R 2 are 1.1 mmol/g and -0.079, respectively (Supporting Information Figure ). Despite the divergence in performance between gases, we decided to focus on ANNs for further optimization since the ethylene ANN achieved the most accurate predictions of the six model type/gas pairings. Based on test set errors, the final model may be somewhat constrained in its ability to provide quantitative predictions on new structures, but it nevertheless provides valuable insight as a qualitative tool for comparing general selectivity trends.