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research article

Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening

Majumdar, Sauradeep  
•
Moosavi, Seyed Mohamad  
•
Jablonka, Kevin Maik  
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December 15, 2021
ACS Applied Materials & Interfaces

By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. Therefore, when making new databases of such hypothetical MOFs, we need to ensure that they not only contribute toward the growth of the count of structures but also add different chemistries to the existing databases. In this study, we designed a database of similar to 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 find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of metal nodes. 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). All the structures developed in this study, and their properties, are provided on the Materials Cloud to encourage further use of these materials for other applications.

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Type
research article
DOI
10.1021/acsami.1c16220
Web of Science ID

WOS:000733825500001

Author(s)
Majumdar, Sauradeep  
•
Moosavi, Seyed Mohamad  
•
Jablonka, Kevin Maik  
•
Ongari, Daniele  
•
Smit, Berend  
Date Issued

2021-12-15

Publisher

AMER CHEMICAL SOC

Published in
ACS Applied Materials & Interfaces
Volume

13

Issue

51

Start page

61004

End page

61014

Subjects

Nanoscience & Nanotechnology

•

Materials Science, Multidisciplinary

•

Science & Technology - Other Topics

•

Materials Science

•

mofs

•

molecular simulations

•

machine learning

•

diversity

•

carbon capture

•

hydrogen storage

•

co2 capture

•

force-field

•

flue-gas

•

discovery

•

design

•

construction

•

algorithms

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LSMO  
Available on Infoscience
January 1, 2022
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/184109
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