Machine learning potential for modelling H<sub>2</sub> adsorption/diffusion in MOFs with open metal sites-Reference-Cited by-同舟云学术

Machine learning potential for modelling H₂ adsorption/diffusion in MOFs with open metal sites

Published:2024 Issue:14 Volume:15 Page:5294-5302
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Liu Shanping¹,Dupuis Romain¹²,Fan Dong¹,Benzaria Salma³,Bonneau Mickaele³,Bhatt Prashant³^ORCID,Eddaoudi Mohamed³^ORCID,Maurin Guillaume¹^ORCID

Affiliation:

1. UMR 5253, CNRS, ENSCM, Institute Charles Gerhardt Montpellier, University of Montpellier, Montpellier 34293, France

2. LMGC, Univ. Montpellier, CNRS, Montpellier, France

3. Division of Physical Science and Engineering, Advanced Membrane and Porous Materials Center, King Abdullah, University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia

Abstract

Machine learning potential is required to accurately describe the interactions between the challenging H2 molecules and metal organic frameworks containing open metal sites.

Funder

King Abdullah University of Science and Technology

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/SC/D3SC05612K

Reference72 articles.

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5. The new age of MOFs and of their porous-related solids

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