  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.
