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
Descripción
Sumario: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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