Abstract

Reaction networks are essential tools for the analysis, visualization and understanding of chemical processes in such diverse fields as catalysis, combustion and the origin of life. For complex processes, the number of individual reaction steps in such a network is so large that an exhaustive first-principles calculation of all reaction energies and rates becomes prohibitively expensive. In this talk, we use machine learning (ML) to accelerate the exploration of chemical reaction space, in analogy to the more established ML-based exploration of chemical space. To this end, we generated a new reactive reference database (Rad-6) of open- and closed-shell organic molecules. This allows us to apply “chemical space” ML methods to predict the chermochemistry of reaction networks. The performance of these methods confirms the potential of ML for the high-throughput screening of large reaction networks.