Publication date: Dec 02, 2019
We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal’s postcombustion flue gases. This workflow is applied to a database of 324 covalent−organic frameworks (COFs) reported in the literature, to characterize their CO2 adsorption properties using the following steps: (1) optimization of the crystal structure (atomic positions and unit cell) using density functional theory, (2) fitting atomic point charges based on the electron density, (3) characterizing the pore geometry of the structures before and after optimization, (4) computing carbon dioxide and nitrogen isotherms using grand canonical Monte Carlo simulations with an empirical interaction potential, and finally, (5) assessing the CO2 parasitic energy via process modeling. The full workflow has been encoded in the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). Both the workflow and the automatically generated provenance graph of our calculations are made available on the Materials Cloud, allowing peers to inspect every input parameter and result along the workflow, download structures and files at intermediate stages, and start their research right from where this work has left off. In particular, our set of CURATED (Clean, Uniform, and Refined with Automatic Tracking from Experimental Database) COFs, having optimized geometry and high-quality DFT-derived point charges, are available for further investigations of gas adsorption properties. We plan to update the database as new COFs are being reported. *** UPDATE December 2019 *** - Database extended to include 417 COFs (from paper published until the 1st September 2019) - Migration to AiiDA-v1.0.0 - Using the publicly available plugin aiida-lsmo
File name | Size | Description |
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cifs_cellopt_Dec19.zip
MD5md5:7480cf066681ac761da4ca7749186580
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485.4 KiB | CIF files with DFT-optimized coordinates/unit cell and atomic DDEC charges. |
cofs_export_Dec19.aiida
MD5md5:926267e470070fee94dd97a7df5392d1
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1.2 GiB | AiiDA provenance graph exported using aiida-core 1.0.0 |
2021.100 (version v9) | Jun 30, 2021 | DOI10.24435/materialscloud:z6-jn |
2021.35 (version v8) | Feb 24, 2021 | DOI10.24435/materialscloud:5q-jt |
2020.133 (version v7) | Oct 29, 2020 | DOI10.24435/materialscloud:42-fm |
2020.107 (version v6) | Sep 09, 2020 | DOI10.24435/materialscloud:kd-wj |
2020.68 (version v5) | Jun 26, 2020 | DOI10.24435/materialscloud:97-2x |
2019.0034/v4 (version v4) | Feb 26, 2020 | DOI10.24435/materialscloud:2019.0034/v4 |
2019.0034/v3 (version v3) | Feb 13, 2020 | DOI10.24435/materialscloud:2019.0034/v3 |
2019.0034/v2 (version v2) [This version] | Dec 02, 2019 | DOI10.24435/materialscloud:2019.0034/v2 |
2019.0034/v1 (version v1) | Jun 25, 2019 | DOI10.24435/materialscloud:2019.0034/v1 |