We next investigated several supervised machine learning models to fit isotherms relevant to C2 separations, namely kernel ridge regression (KRR) models, random forests (RF), and artificial neural networks (ANNs, see Sec. 2). We trained models across the full isotherm to make the models more general, as different operating conditions such as partial pressure and temperature may influence which MOF is most appropriate for the application. We take each individual point in the isotherm as a separate training data point for two reasons: 1) there is no general isotherm equation that can be used to describe the range of adsorption behavior observed in the dataset, and 2) even if such an equation existed, we would not have enough data to properly fit the equation.