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

[Image: see text] 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 structure...

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Autores principales: Majumdar, Sauradeep, Moosavi, Seyed Mohamad, Jablonka, Kevin Maik, Ongari, Daniele, Smit, Berend
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719320/
https://www.ncbi.nlm.nih.gov/pubmed/34910455
http://dx.doi.org/10.1021/acsami.1c16220
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author Majumdar, Sauradeep
Moosavi, Seyed Mohamad
Jablonka, Kevin Maik
Ongari, Daniele
Smit, Berend
author_facet Majumdar, Sauradeep
Moosavi, Seyed Mohamad
Jablonka, Kevin Maik
Ongari, Daniele
Smit, Berend
author_sort Majumdar, Sauradeep
collection PubMed
description [Image: see text] 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 ∼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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spelling pubmed-87193202022-01-03 Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening Majumdar, Sauradeep Moosavi, Seyed Mohamad Jablonka, Kevin Maik Ongari, Daniele Smit, Berend ACS Appl Mater Interfaces [Image: see text] 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 ∼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. American Chemical Society 2021-12-15 2021-12-29 /pmc/articles/PMC8719320/ /pubmed/34910455 http://dx.doi.org/10.1021/acsami.1c16220 Text en © 2021 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Majumdar, Sauradeep
Moosavi, Seyed Mohamad
Jablonka, Kevin Maik
Ongari, Daniele
Smit, Berend
Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening
title Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening
title_full Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening
title_fullStr Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening
title_full_unstemmed Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening
title_short Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening
title_sort diversifying databases of metal organic frameworks for high-throughput computational screening
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719320/
https://www.ncbi.nlm.nih.gov/pubmed/34910455
http://dx.doi.org/10.1021/acsami.1c16220
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