Density Functional Theory (DFT) plays a central role in modern catalyst design by enabling atomic-scale understanding of surface structures, adsorption energetics, reaction pathways, and activation barriers. It allows researchers to predict catalytic activity, selectivity, and stability before experimental synthesis, significantly reducing trial-and-error and accelerating discovery. Building on this capability, DeepChemIQ integrates DFT with advanced kinetic and transport modeling, and AI to explore a wide range of catalytic applications, including plasma catalysis, electrocatalysis, thermal catalysis, and both heterogeneous and homogeneous systems. The platform also extends to emerging fields such as microwave-assisted catalysis and electric field-enhanced catalysis, enabling the co-design of catalysts and reaction environments under diverse operating conditions. By combining first-principles simulations with data-driven approaches, DeepChemIQ provides a unified framework to optimize catalyst performance across multiple energy and chemical processes.