#5497. Data-driven discovery of multiscale chemical reactions governed by the law of mass action

August 2026publication date
Proposal available till 20-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Applied Mathematics;
Numerical Analysis;
Computational Mathematics;
Modeling and Simulation;
Physics and Astronomy (all);
Physics and Astronomy (miscellaneous);
Computer Science Applications;
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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 minima can be eventually obtained.
Keywords:
Chemical reactions; Machine learning; Multiscale; Nonlinear regression; Ordinary differential equations

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