Application of Machine Learning to Development of Atmospheric Chemical Mechanisms

Abstract:

The temporal evolution of species emitted to or formed in the atmosphere is evaluated by three-dimensional chemical transport models (CTMs) simulating simultaneous chemical reactions and transport. The number of species comprising atmospheric chemistry can be immense. Moreover, representing the detailed steps that proceed from initial oxidation of a volatile organic compound (VOC) with the principal oxidants, OH, O3, or NO3, to important products is a major challenge in atmospheric chemistry. Generally, chemical mechanisms employed in atmospheric models are limited to a few hundred species and reactions, which still can require up to 90% of the overall computational resources for solving the overall chemical transport model. This is a severe limitation on the ability to simulate atmospheric chemistry. Machine learning has been demonstrated to be successful in handling complex simulations by significantly reducing computational burdens while maintaining precision. Here we explore the potential of employing machine learning in atmospheric chemical mechanisms to reproduce with fidelity the molecular routes leading from compounds emitted into the atmosphere to ultimate oxidation products, including conversion to aerosol species. Chemical reaction networks generated by machine learning offer the opportunity not only to replace existing mechanisms used in atmospheric models but also to be computationally efficient. Machine learning-derived reaction mechanisms based on the explicit atmospheric chemical mechanisms that have been developed in the laboratory and theoretically will be trained by state-of-the-art long short-term memory networks. We will illustrate these concepts by application to the atmospheric mechanism for isoprene oxidation, comprising 1975 reactions and 612 species.