most citedUnravelling Negative In-plane Stretchability of 2D MOF by Large Scale Machine Learning Potential Molecular Dynamics

1 citations · 2 across the 2 of their papers we have counts for

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2 papers

cond-mat.mtrl-sci2023★ 1 cited

Machine Learning Potential for Modelling H Adsorption/Diffusion in MOF with Open Metal Sites

Shanping Liu, Romain Dupuis, Dong Fan +5

Metal-organic frameworks (MOFs) incorporating open metal sites (OMS) have been identified as promising sorbents for many societally relevant-adsorption applications including CO$_2…

cond-mat.mtrl-sci2023★ 1 cited

Unravelling Negative In-plane Stretchability of 2D MOF by Large Scale Machine Learning Potential Molecular Dynamics

Dong Fan, Aydin Ozcan, Pengbo Lyu +1

Two-dimensional (2D) metal-organic frameworks (MOFs) hold immense potential for various applications due to their distinctive intrinsic properties compared to their 3D analogues. H…