Preview of machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

Metal–organic frameworks (MOFs) are a class of chemical compounds used for the storage of gases such as hydrogen and carbon dioxide. They also have potential applications in gas purification, catalysis and as supercapacitors. A database of quantum-chemical properties for over 14,000 MOF structures (...

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Detalles Bibliográficos
Autor principal: Callaghan, Sarah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8085598/
https://www.ncbi.nlm.nih.gov/pubmed/33982029
http://dx.doi.org/10.1016/j.patter.2021.100239
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author Callaghan, Sarah
author_facet Callaghan, Sarah
author_sort Callaghan, Sarah
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description Metal–organic frameworks (MOFs) are a class of chemical compounds used for the storage of gases such as hydrogen and carbon dioxide. They also have potential applications in gas purification, catalysis and as supercapacitors. A database of quantum-chemical properties for over 14,000 MOF structures (the “QMOF database”) has been created and made available to the community along with code for machine learning and other related resources.
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spelling pubmed-80855982021-05-11 Preview of machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery Callaghan, Sarah Patterns (N Y) Preview Metal–organic frameworks (MOFs) are a class of chemical compounds used for the storage of gases such as hydrogen and carbon dioxide. They also have potential applications in gas purification, catalysis and as supercapacitors. A database of quantum-chemical properties for over 14,000 MOF structures (the “QMOF database”) has been created and made available to the community along with code for machine learning and other related resources. Elsevier 2021-04-09 /pmc/articles/PMC8085598/ /pubmed/33982029 http://dx.doi.org/10.1016/j.patter.2021.100239 Text en © 2021 The Author https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Preview
Callaghan, Sarah
Preview of machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
title Preview of machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
title_full Preview of machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
title_fullStr Preview of machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
title_full_unstemmed Preview of machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
title_short Preview of machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
title_sort preview of machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
topic Preview
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8085598/
https://www.ncbi.nlm.nih.gov/pubmed/33982029
http://dx.doi.org/10.1016/j.patter.2021.100239
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