Establishing the kinetic details of complex reaction networks is central to understanding heterogeneous catalytic surfaces. The development of such networks for new systems is often painstaking, even when good hypotheses exist for the governing reactions and cycles. Nevertheless, this sort of domain knowledge is often outpaced by the increased synthetic and experimental throughput that are driving exploratory catalyst development. The time and cost of generating reaction data for new systems are thus impediments to interpreting catalyst performance, rationalizing structure-function relationships, and leveraging burgeoning (and data demanding) machine learning approaches to catalyst development. For these reasons it is urgent to develop computational methods to accelerate and automate the exploration, characterization, and refinement of complex reaction networks at surfaces.