One of the practical motivation of our work is to understand the importance of accurate knowledge of the heat capacity in the use of porous materials in carbon capture processes. In particular, in a temperature swing adsorption process (TSA), one needs to heat the material to release the captured CO 2 . As the contributions from heating the adsorbed gases in the column are negligible for this application (see SI for discussion), this energy requirement depends on the heat capacity of the material. Figure shows that for zeolites, it is indeed reasonable to assume that the heat capacity does not differ significantly among different zeolites. However, these results show that this assumption does not hold for MOFs. As our machine learning model gives the heat capacity with high accuracy, we can quantify the impact of the differences in the heat capacity on the ranking of materials in a TSA process.