Zeolites are a family of natural and artificial porous aluminosilicates that rank among the most widely-produced crystalline nanoporous materials in the chemical industry, where they are used in particular for their active role in catalysis, adsorption, and ion exchange. Computational modelling plays a key role in understanding their fundamental properties, linking their physical and chemical behaviour at the macroscopic scale to their structure and dynamics at the atomic scale. With this goal, different levels of chemical description have been used in molecular dynamics (MD) simulations of zeolites, ranging from classical interatomic potentials -with low accuracy but high transferabilityto first-principles or ab initio schemes based on Density Functional Theory (DFT) -which feature high accuracy along with high computational expense. While DFT calculations are feasible on relatively small zeolitic systems, their high CPU cost makes them unsuitable for applications at large time or length scales, or in high-throughput computational workflows (for example, identifying materials with a specific property by screening a database).