Issue 5, 2023

Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification

Abstract

Chemical reaction neural network (CRNN), a recently developed tool for autonomous discovery of reaction models, has been successfully demonstrated on a variety of chemical engineering and biochemical systems. It leverages the extraordinary data-fitting capacity of modern deep neural networks (DNNs) while preserving high interpretability and robustness by embedding widely applicable physical laws such as the law of mass action and the Arrhenius law. In this paper, we further developed Bayesian CRNN to not only reconstruct but also quantify the uncertainty of chemical kinetic models from data. Two methods, the Markov chain Monte Carlo algorithm and variational inference, were used to perform the Bayesian CRNN, with the latter mainly adopted for its speed. We demonstrated the capability of Bayesian CRNN in the kinetic uncertainty quantification of different types of chemical systems and discussed the importance of embedding physical laws in data-driven modeling. Finally, we discussed the adaptation of Bayesian CRNN for incomplete measurements and model mixing for global uncertainty quantification.

Graphical abstract: Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification

Article information

Article type
Paper
Submitted
30 out 2022
Accepted
11 jan 2023
First published
12 jan 2023
This article is Open Access
Creative Commons BY-NC license

Phys. Chem. Chem. Phys., 2023,25, 3707-3717

Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification

Q. Li, H. Chen, B. C. Koenig and S. Deng, Phys. Chem. Chem. Phys., 2023, 25, 3707 DOI: 10.1039/D2CP05083H

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