Finally, the recent developments and increased availability of machine-learned interatomic potentials have been recognized in the past few years as a breakthrough, in particular for flexible framework materials. Among the successful examples of their application reported in the literature, Goeminne et al. trained machine learning potentials (MLPs) on DFT data for CO 2 adsorption in MOFs ZIF-8 and Mg-MOF-74, demonstrating the possibility to derive accurate adsorption isotherms based on these MLPs -and very recently extended this to water adsorption in flexible MOFs. Castel et al. trained a MLP on data from ab initio trajectories of a ZIF liquid, and used it to produce high-quality atomistic models of ZIF glasses through melt-quench simulations, describing the crystal-toliquid and liquid-to-glass transitions of the system.