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

@article{Huang2022DatadrivenDO, title={Data-driven discovery of multiscale chemical reactions governed by the law of mass action}, author={Juntao Huang and Y. Zhou and W. A. Yong}, journal={ArXiv}, year={2022}, volume={abs/2101.06589} }

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… Expand

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SHOWING 1-10 OF 56 REFERENCES

Inference of chemical reaction networks based on concentration profiles using an optimization framework.

- Medicine, Computer Science
- Chaos
- 2019

An approach to infer the stoichiometric subspace of a chemical reaction network from steady-state concentration data profiles obtained from a continuous isothermal reactor is presented and the proposed framework is, in principle, applicable to many other reaction systems, thus providing future extensions to understanding reaction networks guiding chemical reactors and complex biological mixtures. Expand

Inference of chemical reaction networks

- Chemistry
- 2008

Abstract This paper demonstrates how, in principle, a chemical reaction mechanism (reaction network) can be inferred using relatively simple systematic mathematical and statistical analyses of… Expand

Reactive SINDy: Discovering governing reactions from concentration data.

- Medicine, Computer Science
- The Journal of chemical physics
- 2019

This work extends the sparse identification of nonlinear dynamics (SINDy) method to vector-valued ansatz functions, each describing a particular reaction process, and shows that a gene regulation network can be correctly estimated from observed time series. Expand

Inference of chemical reaction networks using mixed integer linear programming

- Computer Science
- Comput. Chem. Eng.
- 2016

A framework is developed that allows an almost entirely automated recovery of sets of reactions comprising a CRN using experimental data and is designed to promote sparse connectivity and can integrate known structural properties using linear constraints. Expand

Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms

- Computer Science
- Comput. Phys. Commun.
- 2016

The RMG software package also includes CanTherm, a tool for computing the thermodynamic properties of chemical species and both high-pressure-limit and pressure-dependent rate coefficients for chemical reactions using results from quantum chemical calculations. Expand

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

- Computer Science
- J. Comput. Phys.
- 2019

Abstract We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear… Expand

Data-driven discovery of partial differential equations

- Medicine, Computer Science
- Science Advances
- 2017

The sparse regression method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Expand

Learning Interpretable and Thermodynamically Stable Partial Differential Equations

- Computer Science, Mathematics
- ArXiv
- 2020

Numerical results indicate that the learned PDEs can achieve good accuracy in a wide range of Knudsen numbers and can give satisfactory results with randomly sampled discontinuous initial data although it is trained only with smooth initial data. Expand

Data-driven discovery of coordinates and governing equations

- Medicine, Computer Science
- Proceedings of the National Academy of Sciences
- 2019

A custom deep autoencoder network is designed to discover a coordinate transformation into a reduced space where the dynamics may be sparsely represented, and the governing equations and the associated coordinate system are simultaneously learned. Expand

Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics

- Computer Science, Mathematics
- IEEE Transactions on Molecular, Biological and Multi-Scale Communications
- 2016

This method, implicit-SINDy, succeeds in inferring three canonical biological models: 1) Michaelis-Menten enzyme kinetics; 2) the regulatory network for competence in bacteria; and 3) the metabolic network for yeast glycolysis. Expand