We next sought to train interpretable machine learning (ML) models to identify essential features that distinguish working capacity for the two gas molecules in the two databases. In order to select the suitable machine learning (ML) model for our study, we performed initial comparison of model accuracy across models with different complexities: linear regression, random forest (RF), and artificial neural network (ANN) models. For the featurization, we use geometry features computed from Zeo++ and RACs, atomic property-centric features computed on the graph of the MOF (see Sec. 2). In the case of CH4 working capacity for USMOF DB, all models showed high train and test R 2 values (i.e., exceeding 0.97, Supporting Information Table ). Models predicting the mechanically stable USMOF DB CO2 working capacity, on the other hand, yielded lower test R 2 values (linear regression: 0.77, RF: 0.83, and ANN: 0.78). Because predicting CO2 working capacity is the more challenging learning task, we selected an RF model due to its trade-off of accuracy and interpretability.