Predicting hydrogen storage in MOFs via machine learning

The H(2) capacities of a diverse set of 918,734 metal-organic frameworks (MOFs) sourced from 19 databases is predicted via machine learning (ML). Using only 7 structural features as input, ML identifies 8,282 MOFs with the potential to exceed the capacities of state-of-the-art materials. The identif...

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Detalles Bibliográficos
Autores principales: Ahmed, Alauddin, Siegel, Donald J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8276024/
https://www.ncbi.nlm.nih.gov/pubmed/34286305
http://dx.doi.org/10.1016/j.patter.2021.100291

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8276024/
https://www.ncbi.nlm.nih.gov/pubmed/34286305
http://dx.doi.org/10.1016/j.patter.2021.100291

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