For our curated dataset of pormake-constructed MOFs, we generated training data for the bandgap using the average prediction from an ensemble of crystal graph convolutional neural networks (CGCNNs) trained on data available from the QMOF database. To generate training data for the c p , we use the gradient-boosted decision tree model of Moosavi et al., which predicts out-of-sample testing data with an MAE of 0.02 J g -1 K -1 .