Data-driven discovery of multiscale chemical reactions governed by the law of mass action

TL;DR

This paper introduces a data-driven approach to discover multiscale chemical reactions governed by the law of mass action, overcoming optimization challenges with a novel partial-parameters-freezing technique, and demonstrates its effectiveness on various chemical systems.

Contribution

The paper presents a new method using a single matrix representation and a partial-parameters-freezing technique to accurately learn multiscale chemical reactions from data.

Findings

Successfully applied to Michaelis-Menten kinetics

Effective in hydrogen oxidation reactions

Accurate in simplified GRI-3.0 mechanism

Abstract

In this paper, we propose a data-driven 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 partial-parameters-freezing (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 mimina can be eventually obtained. Several numerical experiments including the classical Michaelis-Menten kinetics, the hydrogen oxidation reactions, and the simplified GRI-3.0 mechanism verify the good performance of our algorithm in learning the multiscale chemical reactions. The code is available at \url{https://github.com/JuntaoHuang/multiscale-chemical-reaction}.