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

ABSTRACT: Metal-organic frameworks (MOFs) incorporating open metal sites (OMS) have been identified as promising sorbents for many societally relevant-adsorption applications including CO₂ capture, natural gas purification and H₂ storage. This has been ascribed to strong specific interactions between OMS and the guest molecules that enable the MOF to achieve an effective capture even under low gas pressure conditions. In particular, the presence of OMS in MOFs was demonstrated to substantially boost the H₂ binding energy for achieving high adsorbed hydrogen densities and large usable hydrogen capacities. So far, there is a critical bottleneck to computationally attain a full understanding of the thermodynamics and dynamics of H₂ in this sub-class of MOFs s

SUBMITTER: Liu S 

PROVIDER: S-EPMC10988610 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature