While the quality of this level of description is crucial to the validity of the models derived, none of the currently used options are truly satisfying. Classical force fields do not directly allow the description of bond breaking and formation. Reactive force fields are typically limited in the accuracy of their description of the chemical environment around metals, leading to issues in the structures obtained and their properties. Ab initio methods have high chemical accuracy, but their expensive computational cost limits their use to small time and length scales, which in turn limits the statistical accuracy of the atomistic models obtained. In this work, we decided to apply a radically new approach to solve this issue by training a machine-learnt potential for the description of disordered phases of a zeolitic imidazolate framework (ZIF), and using it to produce high-quality atomistic models of ZIF glasses.