Understanding the diversity of the metal-organic framework ecosystem

Understanding the diversity of the metal-organic framework ecosystem

At present there are databases with over 500,000 predicted or synthesized MOF structures, yet a method to establish whether a new material adds new information does not exist. Here the authors propose a machine-learning based approach to quantify the structural and chemical diversity in common MOF d...

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Main Authors: Seyed Mohamad Moosavi, Aditya Nandy, Kevin Maik Jablonka, Daniele Ongari, Jon Paul Janet, Peter G. Boyd, Yongjin Lee, Berend Smit, Heather J. Kulik
Format: Article
Language:English
Published: Nature Publishing Group 2020-08-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-020-17755-8
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spelling doaj-1372a595c9304ad5a57637579b729d382021-08-15T11:41:49ZengNature Publishing GroupNature Communications2041-17232020-08-0111111010.1038/s41467-020-17755-8Understanding the diversity of the metal-organic framework ecosystemSeyed Mohamad Moosavi0Aditya Nandy1Kevin Maik Jablonka2Daniele Ongari3Jon Paul Janet4Peter G. Boyd5Yongjin Lee6Berend Smit7Heather J. Kulik8Laboratory of Molecular Simulation, Institut des Sciences et Ingénierie Chimiques, École, Polytechnique Fédérale de Lausanne (EPFL)Department of Chemical Engineering, Massachusetts Institute of TechnologyLaboratory of Molecular Simulation, Institut des Sciences et Ingénierie Chimiques, École, Polytechnique Fédérale de Lausanne (EPFL)Laboratory of Molecular Simulation, Institut des Sciences et Ingénierie Chimiques, École, Polytechnique Fédérale de Lausanne (EPFL)Department of Chemical Engineering, Massachusetts Institute of TechnologyLaboratory of Molecular Simulation, Institut des Sciences et Ingénierie Chimiques, École, Polytechnique Fédérale de Lausanne (EPFL)School of Physical Science and Technology, ShanghaiTech UniversityLaboratory of Molecular Simulation, Institut des Sciences et Ingénierie Chimiques, École, Polytechnique Fédérale de Lausanne (EPFL)Department of Chemical Engineering, Massachusetts Institute of TechnologyAt present there are databases with over 500,000 predicted or synthesized MOF structures, yet a method to establish whether a new material adds new information does not exist. Here the authors propose a machine-learning based approach to quantify the structural and chemical diversity in common MOF databases.https://doi.org/10.1038/s41467-020-17755-8
collection DOAJ
language English
format Article
sources DOAJ
author Seyed Mohamad Moosavi
Aditya Nandy
Kevin Maik Jablonka
Daniele Ongari
Jon Paul Janet
Peter G. Boyd
Yongjin Lee
Berend Smit
Heather J. Kulik
spellingShingle Seyed Mohamad Moosavi
Aditya Nandy
Kevin Maik Jablonka
Daniele Ongari
Jon Paul Janet
Peter G. Boyd
Yongjin Lee
Berend Smit
Heather J. Kulik
Understanding the diversity of the metal-organic framework ecosystem
Nature Communications
author_facet Seyed Mohamad Moosavi
Aditya Nandy
Kevin Maik Jablonka
Daniele Ongari
Jon Paul Janet
Peter G. Boyd
Yongjin Lee
Berend Smit
Heather J. Kulik
author_sort Seyed Mohamad Moosavi
title Understanding the diversity of the metal-organic framework ecosystem
title_short Understanding the diversity of the metal-organic framework ecosystem
title_full Understanding the diversity of the metal-organic framework ecosystem
title_fullStr Understanding the diversity of the metal-organic framework ecosystem
title_full_unstemmed Understanding the diversity of the metal-organic framework ecosystem
title_sort understanding the diversity of the metal-organic framework ecosystem
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2020-08-01
description At present there are databases with over 500,000 predicted or synthesized MOF structures, yet a method to establish whether a new material adds new information does not exist. Here the authors propose a machine-learning based approach to quantify the structural and chemical diversity in common MOF databases.
url https://doi.org/10.1038/s41467-020-17755-8
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