Foundational models are trained on large open source structural databases, which consists of low energy relaxation trajectories and ground state structures. However, MLPs are only accurate on simulations within the domain of what they have been trained on. Therefore, high energy regions of the foundational model PES might be misrepresented leading to unreliable high temperature MD trajectories. This limitation can be improved by further training the foundational models using additional DFT data of the system to finetune the foundational model. Additionally, the way this data is sampled may have an impact on model accuracy as the training data can be biased to explore the phase space. In this work, we investigate foundational models for accurately simulating the high temperature decomposition of CALF-20 and ZIF-8 (Figure ). Both are zinc based MOFs, where ZIF-8 has an imidazolate linker and CALF-20 has triazolate linkers and an oxalate pillar connecting metal nodes. We also consider the impact of sampling methods for finetuning foundational models and training models "from scratch" to provide the best accuracy for treating these challenging high temperature processes. We then used the best model to simulate a 1 ns decomposition trajectory to gain atomistic information of pyrolysis mechanisms and the evolution of MOF-derived catalytic structures.