  Machine learning techniques have been widely employed as effective tools in
addressing various engineering challenges in recent years, particularly for the
challenging task of microstructure-informed materials modeling. This work
provides a comprehensive review of the current machine learning-assisted and
data-driven advancements in this field, including microstructure
characterization and reconstruction, multiscale simulation, correlations among
process, microstructure, and properties, as well as microstructure optimization
and inverse design. It outlines the achievements of existing research through
best practices and suggests potential avenues for future investigations.
Moreover, it prepares the readers with educative instructions of basic
knowledge and an overview on machine learning, microstructure descriptors and
machine learning-assisted material modeling, lowering the interdisciplinary
hurdles. It should help to stimulate and attract more research attention to the
rapidly growing field of machine learning-based modeling and design of
microstructured materials.
