Microkinetic modeling and kinetic Monte Carlo (kMC) simulations are central to DeepChemIQ’s ability to translate atomic-scale insights into predictive catalytic performance. Starting from simulation data such as DFT-calculated adsorption energies and transition states, we construct kinetic models grounded in transition state theory to describe reaction networks across catalyst surfaces. These models provide key descriptors for AI-driven screening, identify rate-limiting steps, and generate mechanistic rate expressions under realistic conditions. The resulting rate expressions can be directly integrated into computational fluid dynamics (CFD) models to bridge catalyst behavior with reactor-scale performance. In addition, predicted kinetics can be systematically compared with experimental data, enabling model validation, refinement, and improved confidence in catalyst design and process optimization.