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Diversifying databases of metal organic frameworks for high-throughput computational screening

Sauradeep Majumdar1, Seyed Mohamad Moosavi1, Kevin Maik Jablonka1, Daniele Ongari1, Berend Smit1*

1 Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1951 Sion, Valais, Switzerland

* Corresponding authors emails: berend.smit@epfl.ch
DOI10.24435/materialscloud:yn-de [version v1]

Publication date: Jul 30, 2021

How to cite this record

Sauradeep Majumdar, Seyed Mohamad Moosavi, Kevin Maik Jablonka, Daniele Ongari, Berend Smit, Diversifying databases of metal organic frameworks for high-throughput computational screening, Materials Cloud Archive 2021.126 (2021), doi: 10.24435/materialscloud:yn-de.

Description

By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. When making new databases of such hypothetical MOFs, we need to assure that they not only contribute towards the growth of the count of structures but also add different chemistry to existing databases. In this work, we designed a database of ~20,000 hypothetical MOFs which are diverse in terms of their chemical design space—metal nodes, organic linkers, functional groups and pore geometries. Using Machine Learning techniques, we visualized and quantified the diversity of these structures. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications---post combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post combustion carbon capture) and MOF-5 (for hydrogen storage).

Materials Cloud sections using this data

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Files

File name Size Description
mof_data.tar.gz
MD5md5:a424d0a320baaad4a97378a78d8022dd
149.0 MiB MOF structure files, MOF structure properties
README.txt
MD5md5:e1b5a3069dd8c7ee1c76c05860b336de
959 Bytes Description of the content of mof_data.tar.gz

License

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

Journal reference
S. Majumdar, S. M. Moosavi, K. M. Jablonka, D. Ongari, B. Smit, submitted (2021).

Keywords

metal-organic frameworks molecular simulations machine learning diversity MARVEL/DD4 ERC

Version history:

2021.126 (version v1) [This version] Jul 30, 2021 DOI10.24435/materialscloud:yn-de