Several important scientific and engineering research areas depend on the ability to accurately predict phenomena that occur in gas-phase, chemically reacting systems. For example, high-fidelity simulation of the combustion of new fuel mixtures enables the co-design of fuels and engines for cleaner and more efficient use of transportation energy. Such simulations play an important role in atmospheric sciences and astrochemistry as well. The accuracy of these simulations depends on what is known as a “kinetic model.” Kinetic models are data files prescribing the chemical network of a simulation: the possible chemical species formed and consumed, and the rates at which they undergo various reactions as a function of temperature and pressure. The process of collating the thousands of reaction rates and other properties needed for such a model has been recently facilitated by machine-learning tools that extrapolate from a database of known values. However, such technologies suffer from a shortage of data needed to produce reliable predictive models that are applicable to a broad range of engineering applications. To address this shortage, this project transforms and scales the AutoMech software suite to enable both the generation of new, high-accuracy kinetic models from scratch and the population of databases to enhance the predictions made by machine learning tools. This project leverages and extends existing open-source software for parallel workflow orchestration, standard