Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations

TL;DR

This paper introduces a neural ODE-based approach to model chemical reaction networks, aiming to uncover hidden dynamics and improve upon traditional empirical models for better network design.

Contribution

It combines neural ordinary differential equations with chemical reaction modeling to identify limitations of existing models and guide future network development.

Findings

Reveals hidden insights in reaction networks

Improves modeling accuracy over empirical methods

Aids in designing better chemical reaction networks

Abstract

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.