In a different approach, Moosavi et al. devised a computational workflow for the prediction of heat capacities of nanoporous frameworks spanning different families: zeolites, MOFs and COFs. Their approach is based on the calculation of heat capacities through DFT calculations for 230 structures with diverse chemical environments, used for training and validating machine learning models. Their ML models are based on a sum of atomic contributions, where each atom in the framework is featurized in terms of atomic identity, local geometry and chemistry. Their final model demonstrated a very good accuracy (ca. 3%