Optimization of Reduced Chemical Mechanisms Using Graph Theory‐Assisted Gradient Descent

ABSTRACT The oxidation of volatile organic compounds (VOCs) affects particulate matter and oxidant levels in the troposphere, making it vital to simulate for climate models and to predict air quality. Full‐complexity oxidation mechanisms for VOCs such as isoprene, however, are too large to be simulated in a 3‐dimensional atmospheric chemical transport model. Reduced VOC oxidation mechanisms have been developed, but all must make a small number of reactions represent a much larger set, leading to imprecision in stoichiometric coefficients. Some of the sacrifice of accuracy incurred in the reduction process can be recuperated by optimizing the stoichiometric coefficients after reduction. In this work, we present Coefficient Optimization for Automated MOdel REduction (CO‐AMORE), a new set of tools for the optimization of reduced mechanism stoichiometric coefficients using graph theory‐assisted gradient descent. These methods include the application of gradient descent and two new algorithms for preprocessing coefficients: Anti‐Community Clustering (ACC) and Bipartite Graph Sorting (BGS). Our application of gradient descent with data preprocessed using our ACC algorithm improved the accuracy of a reduced isoprene mechanism by up to 40% in a box model simulation. Gradient descent with data preprocessed using BGS was able to achieve similar levels of optimization as ACC across multiple mechanisms. In addition, ACC was over four times faster than standard gradient descent, showing the promise of this novel methodology. Optimized reduced mechanisms allow atmospheric models to be more accurate, enabling a better understanding of particulate matter sources and offering the potential to benefit public health.