Ab initio methods, such as density functional theory (DFT), are accurate computational approaches used to calculate properties and behavior of ensembles from first principles. DFT provides high accuracy when performing energy or force calculations and can be combined with molecular dynamics (MD) to simulate high temperature time dependent processes such as melting. However, DFT has a high computational cost which restricts the applicability of DFT calculations for MOF pyrolysis due to the large temporal and spatial scales it occurs over. One solution is to use machine learned potentials (MLPs) for simulating long timescale simulations.