When Machine Learning Meets Multiscale Modeling in Chemical Reactions
This work addresses computational challenges in chemical reaction modeling for researchers, though it appears incremental as it builds on existing methods.
The study tackled the complexity of chemical reactions by integrating multiscale modeling with machine learning, resulting in significant computational cost reduction and automated model reduction in time-scale separated systems.
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.