In this work, we developed an alternative machine learning approach that is not based on such global descriptors. Recall that the heat capacity is given by the summation over atomic vibrations (equation 1). Hence, we can build a machine learning model to directly predict these atomic contributions. The importance of this step is that we can now make use of the locality approximation in our featurization. Especially, our simulations on ZIFs show that changes in the pore topology have a minor effect on the heat capacity, indicating that the relevant chemical environment is relatively short-ranged. Hence, our features only need to account for the local environment around each atom (see Figure ).