Heterogeneous catalysis plays a crucial role in the production of 80% of chemical products worldwide, and the catalyst market is expected to grow by 4.4% annually from 2020 to 2027. Designing e cient and cost-e↵ective catalysts necessitates an understanding of the underlying mechanism. This knowledge enables the optimization of catalyst morphology and reaction conditions, such as temperature and pressure, to improve catalyst performance. By manipulating the active sites and reaction conditions based on the reaction mechanism, catalysts can be designed to achieve higher levels of activity, selectivity, and stability. Meanfield micro-kinetic modeling (MKM) has proven to be a powerful tool to identify and interpret intermediates and reactions in processes such as gas-phase combustion and catalysis, and has been widely used for catalyst optimization. As demonstrated by its widespread and growing use, MKM has the potential to help discover and design new catalysts to support critical industrial processes. Furthermore, the linear scaling relationships (LSR) developed by Abild-Pedersen et al. enhance the utility of MKM without excessive computational cost by creating a fast and simple way to predict the binding energy of surface species on di↵erent metal surfaces by using the adsorption energy of a species on one metal and scaling it to any other metal. While density functional theory (DFT) calculations are commonly used to compute the binding energies of surface species, performing DFT calculations for species on a large number of metals is computationally expensive. Consequently, LSRs are a useful approximation for rapidly estimating species' thermodynamic properties and screening potential catalysts.