materialscloud:2020.0029/v1

In silico discovery of covalent organic frameworks for carbon capture

Kathryn S. Deeg1, Daiane Damasceno Borges2, Daniele Ongari3*, Nakul Rampal4, Leopold Talirz3, Aliaksandr V. Yakutovich3, Johanna M. Huck1, Berend Smit3

1 Department of Chemistry, University of California, Berkeley, California, 94720, United States

2 Instituto de Física, Universidade Federal de Uberlândia, Uberlândia-MG 38408-100, Brasil

3 Laboratory of Molecular Simulation (LSMO), Institut des sciences et ingénierie chimiques (ISIC), Ecole polytechnique fédérale de Lausanne (EPFL) Valais, Rue de l’Industrie 17, 1951, Sion, Switzerland

4 Adsorption and Advanced Materials Laboratory (AAML), Department of Chemical Engineering Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge, CB3 0AS, UK

* Corresponding authors emails: daniele.ongari@epfl.ch
DOI10.24435/materialscloud:2020.0029/v1 [version v1]

Publication date: Mar 24, 2020

How to cite this record

Kathryn S. Deeg, Daiane Damasceno Borges, Daniele Ongari, Nakul Rampal, Leopold Talirz, Aliaksandr V. Yakutovich, Johanna M. Huck, Berend Smit, In silico discovery of covalent organic frameworks for carbon capture, Materials Cloud Archive 2020.0029/v1 (2020), doi: 10.24435/materialscloud:2020.0029/v1.

Description

We screen a database of more than 69,000 hypothetical covalent organic frameworks (COFs) for carbon capture, using parasitic energy as a metric. In order to compute CO2-framework interactions in molecular simulations, we develop a genetic algorithm to tune the charge equilibration method and derive accurate framework partial charges. Nearly 400 COFs are identified with parasitic energy lower than that of an amine scrubbing process using monoethanolamine; over 70 are better performers than the best experimental COFs; and several perform similarly to Mg-MOF-74. We analyze the effect of pore topology on carbon capture performance in order to guide development of improved carbon capture materials.

Files

File name Size Description
properties_COFs.csv
MD5md5:dff07dc03c138200b4e88c0e085af2ce
6.2 MiB Basic Properties of all COFs studied (includes all COFs in Figure 1 of the paper )
properties_COFs_subset.csv
MD5md5:f5ce89d2a6105ce723352f352d645db4
105.4 KiB Properties from molecular simulations for a subset of COFs (includes all COFs in Figure 3 of the paper)
export_hcofs_co2.aiida
MD5md5:31a46b296a826080fc2c56e439bc0665
6.9 GiB AiiDA v1.0.1 export file

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.

External references

Journal reference
Kathryn S. Deeg, Daiane Damasceno Borges, Daniele Ongari, Nakul Rampal, Leopold Talirz, Aliaksandr V. Yakutovich, Johanna M. Huck, and Berend Smit, Submitted.

Keywords

computational screening MARVEL/DD4 gas adsorption covalent organic frameworks cofs ERC

Version history:

2020.0029/v1 (version v1) [This version] Mar 24, 2020 DOI10.24435/materialscloud:2020.0029/v1