Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack

In recent years, machine learning (ML) has grown exponentially within the field of structure property predictions in materials science. In this issue of Patterns, Ahmed and Siegel scrutinize several redeveloped ML techniques for systematic investigations of over 900,000 metal-organic framework (MOF)...

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Detalles Bibliográficos
Autores principales: Glasby, Lawson T., Moghadam, Peyman Z.
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/PMC8276009/
https://www.ncbi.nlm.nih.gov/pubmed/34286309
http://dx.doi.org/10.1016/j.patter.2021.100305
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author Glasby, Lawson T.
Moghadam, Peyman Z.
author_facet Glasby, Lawson T.
Moghadam, Peyman Z.
author_sort Glasby, Lawson T.
collection PubMed
description In recent years, machine learning (ML) has grown exponentially within the field of structure property predictions in materials science. In this issue of Patterns, Ahmed and Siegel scrutinize several redeveloped ML techniques for systematic investigations of over 900,000 metal-organic framework (MOF) structures, taken from 19 databases, to discover new, potentially record-breaking, hydrogen-storage materials.
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spelling pubmed-82760092021-07-19 Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack Glasby, Lawson T. Moghadam, Peyman Z. Patterns (N Y) Preview In recent years, machine learning (ML) has grown exponentially within the field of structure property predictions in materials science. In this issue of Patterns, Ahmed and Siegel scrutinize several redeveloped ML techniques for systematic investigations of over 900,000 metal-organic framework (MOF) structures, taken from 19 databases, to discover new, potentially record-breaking, hydrogen-storage materials. Elsevier 2021-07-09 /pmc/articles/PMC8276009/ /pubmed/34286309 http://dx.doi.org/10.1016/j.patter.2021.100305 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Preview
Glasby, Lawson T.
Moghadam, Peyman Z.
Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack
title Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack
title_full Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack
title_fullStr Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack
title_full_unstemmed Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack
title_short Hydrogen storage in MOFs: Machine learning for finding a needle in a haystack
title_sort hydrogen storage in mofs: machine learning for finding a needle in a haystack
topic Preview
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8276009/
https://www.ncbi.nlm.nih.gov/pubmed/34286309
http://dx.doi.org/10.1016/j.patter.2021.100305
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