Overall, our findings emphasize the need for caution when using Monte Carlo simulations to study nanoparticle oxidation, as the lack of kinetic aspects can lead to results that are less relevant to catalytic conditions. Additionally, it is important to correctly describe the potential energy surface at a wide range of conditions when using classical forcefields. We report that recent machine learning models such as MACE-MP-0 show promise as a more reliable alternative for studying oxidized platinum nanoparticles, providing geometries and energetics more consistent with DFT calculations. Future work should focus on extending this multistep approach to alloyed systems and investigating the kinetic aspects of oxidation, as the thermodynamic stability of highly oxidized states may not necessarily translate to their practical relevance under catalytic conditions. By combining accurate forcefields, such as MACE-MP-0, with DFT calculations and experimental validation, it may be possible to design more efficient and stable platinum-based catalysts for the oxygen reduction reaction and other important applications. This study serves as a foundation for a more comprehensive understanding of the complex interplay between nanoparticle structure, oxidation state, and catalytic performance, paving the way for the rational design of advanced catalytic materials.