Unravelling abnormal in-plane stretchability of two-dimensional metal–organic frameworks by machine learning potential molecular dynamics

Abstract

Two-dimensional (2D) metal-organic frameworks (MOFs) hold immense potential for various applications due to their distinctive intrinsic properties compared to their 3D analogues. Herein, we designed in silico a highly stable...

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

“… 34 Fan et al equally developed a MLP to explore the mechanical properties of a novel 2D MOF. 40 Johnson et al further combined MLP for the UiO-66 framework with classical FFs for rare gases to explore the host/guest interactions. 35 Very recently, Vandenhaute et al built a neural network MLP with parallelized sampling and on-the-fly training to explore the phase transformation for different MOFs.…”