First, data-driven suggestions of novel molecules are usually performed without constraints for synthetic feasibility. Consequently, synthesizability, which often requires considering catalyzed pathways, is relegated to post hoc analysis using a combination of historical data, experimental chemists, and computer aided synthesis planning tools. The utility of human expert chemists is clear; yet such a strategy faces practical challenges for assessing large numbers of reactions and intertwined multi-step pathways. The challenges are further compounded in the presence of competing side reactions and/or stereochemical targets. This gives rise to the second issue, namely that it remains both labor intensive and expensive to experimentally refine reaction conditions and characterize the behavior of metal-catalyzed pathways. One potentially cost-effective solution is to perform quantum chemical calculations, for example, density functional theory (DFT), to directly model mechanisms, transition states, and reaction intermediates , thus enabling quantification of reaction thermodynamics and development of kinetic models. However, as the third and main motivating issue, DFT calculations are often too slow (days-to-weeks for single pathways) to be truly synergistic and guide experimental studies. To overcome this limitation our goal is to dramatically reduce the time required to model Pd catalyzed cross-couplings to a seconds-tominutes scale without sacrificing the quality of thermodynamic and kinetic characterizations.