A machine learning model exploits the similarities between structures in the training set to predict the target properties of new structures in the test set without performing the actual computations or experimental measurements on those. The similarity between different materials and structures can be explicitly encoded by chemically motivated descriptors or learned as part of a deep learning framework. 28, For example, machine learning models based on the chemical formula or property-labeled materials fragment descriptor were found to be effective in predicting heat capacity of solids and semiconductors. However, these approaches are based on a global description of the entire material. Such an approach is of limited use for MOFs as their chemical diversity is so large that one needs an extremely large data set to be able to train a model based on such a global description. As we have to rely on DFT calculations, we can only generate data for a small subset of MOFs.