  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}.
