Modelling complex atmospheric chemistry with artificial intelligence: data, constraints, and scalability

Chemical processes significantly impact air pollution and its effects on climate, human health, ecosystems, and food security. Therefore, accounting for atmospheric chemistry is essential for reliable air pollution assessments and effective mitigation strategies.

This is typically achieved through the use of chemistry-transport models, which involve solving large systems of ordinary differential equations (ODEs) derived from chemical kinetics. However, as more species and reactions are incorporated into the models, the chemical mechanisms considered become increasingly complex, and the computational burden of the ODE solvers limits atmospheric simulations. This calls for alternative approaches, with artificial intelligence (AI) emerging as one of the most promising.

The EACH (Emulating Atmospheric Chemistry) project, a collaboration between the Max Planck Institute for Chemistry, the Barcelona Supercomputing Center, and Freie Universität Berlin, investigates the potential of using artificial intelligence in atmospheric chemistry modelling. Key results of the project presented here include a comprehensive training and benchmark dataset for AI-driven chemistry models, which will be publicly available. We also address the integration of physical constraints into AI chemistry models, such as element conservation and the non-negativity of concentrations, which are crucial for realistic and stable simulations. While such constraints have been explored in simple chemical mechanisms, scaling their application to complex mechanisms presents new challenges.