Catalyst development plays a pivotal role in enhancing industrial chemical production by optimizing processes to reduce material, financial, and environmental costs . The conventional approach to developing these catalysts relies on a labor-intensive cycle of design, making, and testing of numerous candidates to find the most effective ones for specific reactions. As the number of candidates grows, this method becomes increasingly time-consuming and resource-intensive . With pressing environmental challenges, the need for more efficient strategies is evident, prompting the integration of density functional theory (DFT) simulations into the discovery process. DFT simulations offer valuable theoretical insights, acting as a decision-making tool to predict reactions' outcomes more accurately . However, effectively utilizing these insights requires not only prediction but also the visualization of the complex networks formed by reactions, a significant challenge in both homogeneous and heterogeneous catalysis due to their branched and complex nature. .