Compared to the previous machine learning approaches, the main advantage of our methodology is that each MOF contains many different chemical environments for each atom. Hence, in our selection of our training set, we only need to ensure to generate data for a diverse set of atoms in different chemical environments. The different chemical environments include different elements and coordination environments, as well as different sizes of crystals. As we can obtain such a diverse set using MOFs with a relatively small number of atoms or with high symmetry, we have a training set that we also can use to predict the heat capacities of those MOFs that are too large for DFT calculations. In essence, our machine learning approach is staying as close as possible to the physics of the problem. Once trained, it predicts the contribution of each atom to the heat capacity, depending on the local chemical environment.