From a practical point of view, for new MOFs or COFs crystal structures, we can now provide an estimate of the heat capacity of the material, together with an estimate of the reliability of this prediction. One can expect that, for novel structures with chemical environments that are very different from the training set, our model will indicate that the predictions are unreliable. For these structures, we need to update the machine learning model by including additional DFT calculations. Our machine learning approach in not limited to porous materials and can be generalized to other classes of materials, provided one can generate the relevant training set.