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

Seyed Mohamad Moosavi1,2*, Balázs Álmos Novotny1, Daniele Ongari1, Elias Moubarak1, Mehrdad Asgari1, Özge Kadioglu1, Charithea Charalambous3, Andres Ortega-Guerrero1, Amir H. Farmahini4, Lev Sarkisov4, Susana Garcia3, Frank Noé2, Berend Smit1*

1 Laboratory of Molecular Simulation, Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951 Sion, Valais, Switzerland

2 Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195 Berlin, Germany

3 The Research Centre for Carbon Solutions (RCCS), School of Engineering and Physical Sciences, Heriot-Watt University, EH14 4AS Edinburgh, United Kingdom

4 Department of Chemical Engineering and Analytical Science, School of Engineering, The University of Manchester, Manchester M13 9PL, United Kingdom

* Corresponding authors emails: seyedmohamad.moosavi@fu-berlin.de, berend.smit@epfl.ch
DOI10.24435/materialscloud:p1-2y [version v1]

Publication date: Apr 13, 2022

How to cite this record

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, A data-science approach to predict the heat capacity of nanoporous materials, Materials Cloud Archive 2022.53 (2022), doi: 10.24435/materialscloud:p1-2y.


The heat capacity of a material is a fundamental property that is of significant 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 to accurately predict the heat capacity of these materials, i.e., zeolites, metal-organic frameworks, and covalent-organic frameworks. The accuracy of our prediction is confirmed with novel 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 energy requirement is reduced by as much as a factor of two using the correct heat capacity.

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File name Size Description
8.6 MiB The predicted heat capacity of materials in QMOF, CoREMOF, IZA, and CURATED-COFs between 250K to 400K.
137.4 MiB heat capacity, crystal structure, and phonon calculations output of the MOFs, COFs, and zeolites in the DFT set
392.7 MiB the data and codes to generate the figures of the paper in the main text.
1.1 GiB Structures and features used for machine learning prediction of the heat capacity.
443 Bytes Description of the files in the repository.


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

S.M. Moosavi, et al. (2020) submitted.


MOF zeolite machine learning thermal properties heat capacity SNSF Horizon2020

Version history:

2022.53 (version v1) [This version] Apr 13, 2022 DOI10.24435/materialscloud:p1-2y