During the writing of this work, Goeminne et al. applied MLPs to describe the adsorption of CO 2 in two MOFs (ZIF-8 and Mg-MOF-74) to derive adsorption isotherms from first principles. Here, we use the case of the ZIF family of materials to show that machine-learnt potentials offer an attractive atomistic description of disordered phases of MOFs. Based on first-principles molecular dynamics, we trained neural network-based MLPs and used them to produce high-quality atomistic models of ZIF glasses, with accuracy close to density functional theory (DFT) but at much lower computational cost.