Heat capacity of nanoporous materials is important for processes such as carbon capture, as this can affect process design energy requirements. Here, a machine learning approach for heat capacity prediction, trained on density functional theory simulations, is presented and experimentally verified.
- Seyed Mohamad Moosavi
- Balázs Álmos Novotny
- Berend Smit
