In this paper, we propose a  method to discover multiscale chemical reactions governed by the law of mass action from data . First, we use one 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 denote 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  multiscale chemical reactions. To overcome this difficulty, we propose to perform a round operation on the stoichiometric coefficients which are closed to integersand do not update them in the afterwards training . With such a treatment , the dimension of the searching space is greatly reduced and the global mimina is eventually obtained. Several numerical experiments including the classical Michaelis-Menten kinetics and the hydrogen oxidation reactions verify the good performance of our algorithm in learning  multiscale chemical reactions. The code is available at URL
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 and the hydrogen oxidation reactions verify the good performance of our algorithm in learning the multiscale chemical reactions. The code is available at URL
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data-driven
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from data
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one
a single
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denote
for
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the
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to perform a round operation on the stoichiometric coefficients which are closed to integersand do not update them in the afterwards training
a partial-parameters-freezing (PPF) technique to progressively determine the network parameters by using the fact that the stoichiometric coefficients are integers
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treatment
technique
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greatly reduced
gradually reduced in the training process
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is
can be
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the