When Machine Learning Meets Multiscale Modeling in Chemical Reactions

TL;DR

This paper demonstrates how integrating machine learning with multiscale modeling can effectively reduce computational costs and automatically perform model reduction in complex chemical reactions, especially in biological systems.

Contribution

It introduces a novel approach combining machine learning and multiscale modeling to address complexity and nonlinearity in chemical reactions, with practical biological examples.

Findings

Machine learning reduces computational costs in chemical modeling.

Multiscale modeling aids in automatic model reduction.

Integration improves understanding of complex biological reactions.

Abstract

Due to the intrinsic complexity and nonlinearity of chemical reactions, direct applications of traditional machine learning algorithms may face with many difficulties. In this study, through two concrete examples with biological background, we illustrate how the key ideas of multiscale modeling can help to reduce the computational cost of machine learning a lot, as well as how machine learning algorithms perform model reduction automatically in a time-scale separated system. Our study highlights the necessity and effectiveness of an integration of machine learning algorithms and multiscale modeling during the study of chemical reactions.