Authors: Shan-Ping Liu | Romain Dupuis | Dong Fan | Salma Benzaria | Mickaële Bonneau | Prashant M. Bhatt | Mohamed Eddaoudi | Guillaume Maurin
Source: Chemical Science (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Machine learning potential for modelling H<sub>2</sub> adsorption/diffusion in MOFs with open metal sites Metal-organic frameworks (MOFs) incorporating open metal sites (OMS) have been identified as promising sorbents for many societally relevant-adsorption applications including CO<sub>2</sub> capture, natural gas purification and H<sub>2</sub> 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 H<sub>2</sub> 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 H<sub>2</sub> in this sub-class of MOFs since the generic classical force fields (FFs) are known to fail to accurately describe the interactions between OMS and any guest molecules, in particular H<sub>2</sub>. This clearly hampers the computational-assisted identification of MOFs containing OMS for a target adsorption-related application since the standard high-throughput screening approach based on these generic FFs is not applicable. Therefore, there is a need to derive novel FFs to achieve accurate and effective evaluation of MOFs for H<sub>2</sub> adsorption. On this path, as a proof-of-concept, the soc-MOF-1d containing OMS, previously envisaged as a potential platform for H<sub>2</sub> adsorption, was selected as a benchmark material and a machine learning potential (MLP) was derived for the Al-soc-MOF-1d from a dataset initially generated by <i>ab initio</i> molecular dynamics (AIMD) simulations. This MLP was further implemented in MD simulations to explore the H<sub>2</sub> binding modes as well as the temperature dependence distribution of H<sub>2</sub> in the MOF pores from 10 K to 80 K. MLP-Grand Canonical Monte Carlo (GCMC) simulations were then performed to predict the H<sub>2</sub> sorption isotherm of Al-soc-MOF-1d at 77 K that was further confirmed using sorption data we collected on this sample. As a further step, MLP-based molecular dynamics (MD) simulations were conducted to anticipate the kinetics of H<sub>2</sub> in this MOF. This work delivers the first MLP able to describe accurately the interactions between the challenging H<sub>2</sub> guest molecule and MOFs containing OMS. This innovative strategy applied to one of the most complex molecules owing to its highly polarizable nature, paves the way towards a more systematic accurate and efficient <i>in silico</i> assessment of MOFs containing OMS for H<sub>2</sub> adsorption and beyond to the low-pressure capture of diverse molecules. Adsorption ; Metal-organic framework ; Metal ; Diffusion ; Molecule ; Organic molecules ; Computer science ; Materials science ; Chemical physics ; Nanotechnology ; Chemistry ; Artificial intelligence ; Physical chemistry ; Physics ; Organic chemistry ; Thermodynamics ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications