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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Autores principales:	Seyed Mohamad Moosavi, Aditya Nandy, Kevin Maik Jablonka, Daniele Ongari, Jon Paul Janet, Peter G. Boyd, Yongjin Lee, Berend Smit, Heather J. Kulik
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/1372a595c9304ad5a57637579b729d38
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spelling	oai:doaj.org-article:1372a595c9304ad5a57637579b729d382021-12-02T19:06:44ZUnderstanding the diversity of the metal-organic framework ecosystem10.1038/s41467-020-17755-82041-1723https://doaj.org/article/1372a595c9304ad5a57637579b729d382020-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-17755-8https://doaj.org/toc/2041-1723At 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.Seyed Mohamad MoosaviAditya NandyKevin Maik JablonkaDaniele OngariJon Paul JanetPeter G. BoydYongjin LeeBerend SmitHeather J. KulikNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q 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
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.
format	article
author	Seyed Mohamad Moosavi Aditya Nandy Kevin Maik Jablonka Daniele Ongari Jon Paul Janet Peter G. Boyd Yongjin Lee Berend Smit Heather J. Kulik
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 Portfolio
publishDate	2020
url	https://doaj.org/article/1372a595c9304ad5a57637579b729d38
work_keys_str_mv	AT seyedmohamadmoosavi understandingthediversityofthemetalorganicframeworkecosystem AT adityanandy understandingthediversityofthemetalorganicframeworkecosystem AT kevinmaikjablonka understandingthediversityofthemetalorganicframeworkecosystem AT danieleongari understandingthediversityofthemetalorganicframeworkecosystem AT jonpauljanet understandingthediversityofthemetalorganicframeworkecosystem AT petergboyd understandingthediversityofthemetalorganicframeworkecosystem AT yongjinlee understandingthediversityofthemetalorganicframeworkecosystem AT berendsmit understandingthediversityofthemetalorganicframeworkecosystem AT heatherjkulik understandingthediversityofthemetalorganicframeworkecosystem
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Understanding the diversity of the metal-organic framework ecosystem

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