The use of machine-learnt potentials (MLPs) has emerged as a promising approach for describing interatomic interactions in both molecular systems and condensed matter. MLPs leverage the power of machine learning algorithms to learn complex potential energy surfaces from a large amount of training data, enabling accurate and efficient modeling of interatomic forces. The use of MLPs bypasses the need for explicit functional forms or empirical parameterization typically used in traditional force fields, leading to more universal and adaptable models. A large diversity of MLPs are currently available and being developed, with contrasting features when it comes to their data requirements, computational cost in training and production, generalization ability, etc. In their most common use, MLPs are typically trained from large databases of atomic configurations and their associated energies and atomic forces, allowing the MLPs to encode complex relationships between atomic positions and the potential energy surface. This enables MLPs to accurately describe a wide range of phenomena, including bond breaking and formation, chemical reactions, and phase transitions -as long as the potential has been trained on representative data for these phenomena. Several studies have shown that MLPs are, in particular, well adapted for the description of amorphous phases of condensed matter -including multiple glass-forming materials -such as oxide glasses, 20 chalcogenide compounds, 21 silicates 22 or amorphous carbons. MLPs are trained on high-accuracy data that has been produced with computational chemistry methods of high computational cost, such as density functional theory (DFT) calculations, subsequently the trained MLP potential can rapidly evaluate interatomic forces and energies for arbitrary atomic configurations. There is therefore a large advantage in computational efficiency (several orders of magnitude) compared to the use of ab initio methods, while retaining an accuracy close to the reference level. This speed-up can allow for efficient exploration of complex systems, such as large-scale molecular dynamics simulations or high-throughput materials screening. In our case, it enables the simulations of amorphous ZIFs at time and length scales previously considered unreachable through first-principle calculations.