Methanol-to-hydrocarbon (MTH) conversion from renewable feedstocks is a viable source of light olefins or fuels, provided catalyst selectivity and lifetime can be enhanced. Hierarchical zeolite structures, imbued with a secondary mesopore system within the innate microporous crystal are promising architectures for improved MTH conversion. However, the origins of increased lifetime and short-olefin selectivity in these catalysts is poorly understood. To guide future catalyst development, this project will establish the first quantum mechanical models of hierarchical zeolites to clarify mesopore surface chemistry and elucidate the impact on diffusion and reactivity of key MTH intermediates and products. Rigorous structural validation with experiment will help define accurate structural models, to which state-of-the-art operando modeling techniques will be applied. To accelerate simulation times with first-principles accuracy and facilitate the use of larger models machine learning potentials will be developed with the generated data. Thus, this work will provide crucial insights into mesopore chemistry and stimulate future reaction modeling in more accurate hierarchical models.