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First-principles (1p)-based computational modeling of surface chemical reactions has matured into a predictive-quality instrument for the study of heterogeneous catalysis. While empirical observations of different catalyst materials could be successfully reconciled with theoretical calculations on the macroscopic scale, the intricate processes and interactions on the microscopic scale still pose a challenging field of research. Although important milestones on the way to an in-depth understanding of a catalytic process have been accomplished, the growing availability of computational resources and the ongoing development of algorithms and data-driven methods pave the way for the efficient computational modeling of highly complex microkinetic reaction networks.Based on the energetic input from density functional theory (DFT) and various scaling relations, an efficient, yet approximate method for the modeling of catalytic reactions is the meanfield approximation (MFA). In combination with a reductionist representation of the catalytic surface under investigation, it served as a reliable and successful tool for the screening and evaluation of activity trends of catalyst materials while keeping the computational cost tractable. This cumulative thesis describes the basics of such an MFA-based approach unveiling its shortcomings and the concomitant need for a more detailed active site representation. Additionally, a data-driven approach to predict the adsorption energetics is described, overcoming the inherent shortcomings of the linear scaling relations when applied to mixed-metal catalysts. Here, a comparison of models with different levels of active site representation illustrates the importance of the active site resolution on binary alloy catalysts and allows for the identification of possible improved methanation catalysts due to synergistic effects, which cannot be described in a more coarse-grained description.An MFA-based approach avoids an explicit spatially resolved surface representation and the concomitant individual interactions between adsorbates, and consequently precludes an indepth understanding of the microscopic mechanisms on the catalyst surface. While such an approach has successfully aided in the prediction of activities and activity trends among different catalysts facilitating large-scale screening studies, it is not capable of accurately including adsorbate interactions and often fails to explain selectivity trends. To capture these, a more detailed description on the microscale is required. This is realized by a kinetic Monte Carlo (KMC) approach, which properly accounts for the probabilistic character of microkinetics by simulating the underlying Markov process. For the catalytic activity and selectivity of the carbon monoxide (CO) hydrogenation reaction on Rh catalysts, a surprising dependency of the activity and selectivity on correlations among the adsorbed species influencing the coverage is found, caused by subtle variations in adsorption energetics originating from attractive or repulsive interactions. This demonstrates the essential importance of accurately describing interactions and local coverages on the atomic scale, as a catalyst usually performs best in regions of intermediate coverages.