Datadriven discovery of multiscale chemical reactions governed by the law of mass action
Abstract
In this paper, we propose a datadriven method to discover multiscale chemical reactions governed by the law of mass action. First, we use a single matrix to represent the stoichiometric coefficients for both the reactants and products in a system without catalysis reactions. The negative entries in the matrix denote the stoichiometric coefficients for the reactants and the positive ones for the products. Second, we find that the conventional optimization methods usually get stuck in the local minima and could not find the true solution in learning the multiscale chemical reactions. To overcome this difficulty, we propose a partialparametersfreezing (PPF) technique to progressively determine the network parameters by using the fact that the stoichiometric coefficients are integers. With such a technique, the dimension of the searching space is gradually reduced in the training process and the global minima can be eventually obtained. Several numerical experiments including the classical MichaelisMenten kinetics, the hydrogen oxidation reactions and the simplified GRI3.0 mechanism verify the good performance of our algorithm in learning the multiscale chemical reactions. The code is available at https://github.com/JuntaoHuang/multiscalechemicalreaction.
 Publication:

Journal of Computational Physics
 Pub Date:
 January 2022
 DOI:
 10.1016/j.jcp.2021.110743
 arXiv:
 arXiv:2101.06589
 Bibcode:
 2022JCoPh.44810743H
 Keywords:

 Chemical reactions;
 Multiscale;
 Machine learning;
 Nonlinear regression;
 Ordinary differential equations;
 Physics  Chemical Physics;
 Computer Science  Machine Learning;
 Mathematics  Numerical Analysis;
 Mathematics  Optimization and Control;
 Physics  Computational Physics
 EPrint:
 doi:10.1016/j.jcp.2021.110743