In this work, we have used the state-of-the art quantum mechanical calculations to accurately predict the heat capacity of a set of representative MOFs. However, as these calculations scale with the number of atoms (N a ) as O(N 4 a ), they can only be carried out for a subset of MOFs for which the number of atoms is not too large. We show that we can take advantage of the fact that the heat capacity can be estimated as a local property, and hence use this data as a training set for a machine-learning methodology that enables fast, accurate, reliable prediction of the heat capacity of these materials, even if the number of atoms far exceeds the limit of our quantum calculations. We evaluate the accuracy of this approach using newly measured experimental data. In addition, we show that for carbon capture applications, the assumption of a constant heat capacity of these materials has resulted in a significant overestimation of the energy requirements.