A data-science approach to predict the heat capacity of nanoporous materials

Seyed Mohamad Moosavi, Balázs Álmos Novotny, Daniele Ongari, Elias Moubarak, Mehrdad Asgari, Özge Kadioglu, Charithea Charalambous, Andres Ortega-Guerrero, Amir H. Farmahini, Lev Sarkisov, Susana Garcia, Frank Noé, Berend Smit

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The heat capacity of a material is a fundamental property of great practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications, the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine learning approach, trained on density functional theory simulations, to accurately predict the heat capacity of these materials, that is, zeolites, metal-organic frameworks and covalent-organic frameworks. The accuracy of our prediction is confirmed with experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials, the heat requirement is reduced by as much as a factor of two using the correct heat capacity.

Original languageEnglish
JournalNature Materials
Early online date13 Oct 2022
DOIs
Publication statusE-pub ahead of print - 13 Oct 2022

Fingerprint

Dive into the research topics of 'A data-science approach to predict the heat capacity of nanoporous materials'. Together they form a unique fingerprint.

Cite this