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 to achieve an effective capture even at low gas pressure conditions. In particular, the presence 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 gain a complete understanding of the thermodynamics and dynamics of H2 in this sub-class of MOFs since the generic interatomic potentials (IPs) fail to accurately capture the OMS/H2 interactions. This clearly hampers the computational-assisted identification of existing or novel MOFs since the standard high-throughput screening approach based on generic IPs are not applicable. Therefore, there is a critical need to derive IPs to achieve accurate and effective evaluation of MOFs for H2 adsorption. On this path, as a proof-of-concept, the Al-soc-MOF containing Al-OMS, previously envisaged as a potential candidate for H2 adsorption, was selected and a machine learning potential (MLP) was derived from a dataset initially generated by ab-initio molecular dynamics (AIMD) simulations. This MLP was further implemented in MD simulations to explore the binding modes of H2 as well as its temperature dependence distribution in the MOFs pores from 10K to 90K. MLP- Grand Canonical Monte Carlo (GCMC) simulations were further performed to predict the H2 sorption isotherm of Al-soc-MOF at 77K that was further confirmed by gravimetric sorption measurements. As a further step, MLP-based MD simulations were conducted to anticipate the kinetics of H2 in this MOF. This work delivers the first MLP able to describe accurately the interactions between the challenging H2 guest molecule and MOFs containing OMS. This innovative strategy applied to one of the most complex molecules owing to its highly polarizable nature alongside its quantum-mechanical effects that are only accurately described by quantum calculations, paves the way towards a more systematic accurate and efficient in silico assessment of the MOFs containing OMS for H2 adsorption and beyond to the low-pressure capture/sensing of diverse molecules.