  Advances in machine learning have impacted myriad areas of materials science,
ranging from the discovery of novel materials to the improvement of molecular
simulations, with likely many more important developments to come. Given the
rapid changes in this field, it is challenging to understand both the breadth
of opportunities as well as best practices for their use. In this review, we
address aspects of both problems by providing an overview of the areas where
machine learning has recently had significant impact in materials science, and
then provide a more detailed discussion on determining the accuracy and domain
of applicability of some common types of machine learning models. Finally, we
discuss some opportunities and challenges for the materials community to fully
utilize the capabilities of machine learning.
