Publication date: Apr 13, 2022
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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database_heat_capacity.zip
MD5md5:7f3a5a9959420b91bd7ad76e6b539db2
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8.6 MiB | The predicted heat capacity of materials in QMOF, CoREMOF, IZA, and CURATED-COFs between 250K to 400K. |
DFT_calculations.zip
MD5md5:7bcb3ce14cdbedd881a6d8fc432644e4
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137.4 MiB | heat capacity, crystal structure, and phonon calculations output of the MOFs, COFs, and zeolites in the DFT set |
Figures.zip
MD5md5:8c53d965b1a69f53a2e2566eceb37c2b
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392.7 MiB | the data and codes to generate the figures of the paper in the main text. |
ML.zip
MD5md5:8d7ea90afc26717d5b481c5c04ad4f1c
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1.1 GiB | Structures and features used for machine learning prediction of the heat capacity. |
README.txt
MD5md5:cdafda24972aaa947dbee2269111fc49
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443 Bytes | Description of the files in the repository. |
2022.53 (version v1) [This version] | Apr 13, 2022 | DOI10.24435/materialscloud:p1-2y |