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

• Published in: Nature materials Vol. 21; no. 12; pp. 1419 - 1425
• Main Authors: Moosavi, Seyed Mohamad, Novotny, Balázs Álmos, Ongari, Daniele, Moubarak, Elias, Asgari, Mehrdad, Kadioglu, Özge, Charalambous, Charithea, Ortega-Guerrero, Andres, Farmahini, Amir H., Sarkisov, Lev, Garcia, Susana, Noé, Frank, Smit, Berend
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01-12-2022; Nature Publishing Group; Springer Nature
• Subjects: 639/166/898; 639/301/1034; 639/301/299/921; 639/638/298; Biomaterials; Carbon; Carbon sequestration; Chemistry; Chemistry and Materials Science; Coal; Coal-fired power plants; Condensed Matter Physics; Data science; Density functional theory; Energy requirements; Flue gas; Hot Temperature; Machine learning; Materials Science; Metal-Organic Frameworks; Nanopores; Nanotechnology; Optical and Electronic Materials; Physics; Power Plants; Sorbents; Specific heat
• ISSN: 1476-1122; 1476-4660
• DOI: 10.1038/s41563-022-01374-3

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.
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.