Machine-learned potentials, or machine learning interatomic potentials (MLIP), have garnered significant attention in recent years due to their ability to capture complex interatomic interactions with high accuracy, as well as their computational efficiency. They are data-driven models, typically deriving the energy and atomic forces of a given molecular configuration through trained neural networks, rather than relying on a physics-informed and parameterized analytical decomposition of the interactions at play. They can achieve an accuracy that is close to the reference data that they are trained on, while retaining sufficient flexibility to describe chemically and structurally complex environments. Over the past few years, their use has been rapidly growing and they have been applied to a large and diverse number of systems, ranging from isolated molecules to materials, and from organic bonds to inorganic coordination. They have been very recently used, in particular, to describe silica systems such as amorphous glasses and zeolites. In this work, we have created and published a data set of trajectories, energies, and forces obtained from high-temperature ab initio molecular dynamics on a diverse set of zeolitic topologies (see Figure ). Trained on this new large-scale data set, we optimized a machine learning interatomic potential (MLIP) for pure silica zeolites, which we then used to simulate the structure, the thermal expansion properties and the effects of pressure on a selection of zeolites, both inside and outside its original training set, in order to probe its generalizability.