Predicting chemical reactivity is an important task in many research fields, including drug discovery, 1-3 retrosynthetic organic reaction planning, and new catalyst design. The central bottleneck in reactivity prediction is the localization of transition states (TSs), which is time consuming and error-prone with manual approaches. Although the development of more efficient TS algorithms-including varieties of double-ended searches and more recent data driven approaches -and the development of various automated reaction prediction and characterization packages-including but not limited to Chemoton, YARP, autodE, and ZStructure -have improved accuracy and reduced costs, fundamentally different approaches are required to address the scaling challenges of large reacting systems.