Using the K-C2 dataset, we trained neural network models to predict ethane and ethylene uptake at varying temperature and pressure conditions. The models were found to perform quite well (with MAEs around 0.05 mmol/g and R 2 above 0.99) for interpolative tasks in which some isotherm data is used to train the models, which could make them useful for decreasing the amount of experimentation required to characterize an isotherm for a material. Performance declined, but was still reasonably good (with MAEs around 0.7 mmol/g and R 2 above 0.6), for extrapolative tasks where the models predicted isotherms for structures not included in the training set. We performed feature importance analysis with Shapley additive explanations to determine that the geometric (i.e., coordinating atom) environment local to the metal center is highly correlated with ethane adsorption, whereas process conditions are the most highly correlated features with ethylene adsorption. These observations suggest that MOF SBU design is critical to developing paraffin-selective adsorbents, and optimizing the process conditions in which the adsorbent will be operating is similarly important.