This work establishes a robust framework for generating high-quality training datasets for MLPs applied to challenging chemical transformations. Building on these findings, we are leveraging foundational models to create more diverse training sets that better capture the long timescale dynamics of MOF pyrolysis. This approach promises to yield more stable models capable of accurate simulations at temperatures up to 2000 K. Moving forward, these computational advances will enable deeper insights into pyrolysis mechanisms and help elucidate the critical structure-property relationships between crystalline MOFs and their amorphous catalytically-active derivatives, ultimately accelerating the rational design of high-performance MOF-derived catalytic materials.