Data‐Driven Discovery of Atmospheric Chemical Reactions

Abstract Detailed knowledge of chemical processes in the atmosphere is key to our understanding of regional air pollution and global climate change. However, a complete description of all atmospheric chemical reactions is still out of reach. This necessitates the discovery of new reactions for improved predictability and process understanding. Here, we propose a data‐driven, chemical kinetics‐oriented approach for atmospheric chemical reaction discovery. Our approach leverages time series of species abundances and an incomplete chemical mechanism to predict the existence of new chemistry by “completing” the mechanism. Species abundances and the incomplete mechanism serve as inputs to a variant of graph neural networks known as graph autoencoders (GAEs). The GAE learns a low‐dimensional representation of the chemical system to predict the existence of pairwise chemical interactions occurring between species. We assess our model using GEOS‐Chem, a widely used atmospheric chemical mechanism that represents the complex set of chemical interactions in the atmosphere. Our reaction discovery model achieves high predictive performance (0.9085 mean AUC; 90.06% average prec