Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations
This work addresses the problem of incomplete empirical models in chemical reaction network theory, which is significant for chemists and researchers in the field, and appears to be an incremental contribution.
The authors tackled the problem of modelling chemical reaction networks by using neural ordinary differential equations, which helped to identify shortcomings of existing empirical models. The result is an improved understanding of reaction networks, although no specific numbers are provided.
In chemical reaction network theory, ordinary differential equations are used to model the temporal change of chemical species concentration. As the functional form of these ordinary differential equations systems is derived from an empirical model of the reaction network, it may be incomplete. Our approach aims to elucidate these hidden insights in the reaction network by combining dynamic modelling with deep learning in the form of neural ordinary differential equations. Our contributions not only help to identify the shortcomings of existing empirical models but also assist the design of future reaction networks.