  In this review, we highlight recent developments in the application of
machine learning for molecular modeling and simulation. After giving a brief
overview of the foundations, components, and workflow of a typical supervised
learning approach for chemical problems, we showcase areas and state-of-the-art
examples of their deployment. In this context, we discuss how machine learning
relates to, supports, and augments more traditional physics-based approaches in
computational research. We conclude by outlining challenges and future research
directions that need to be addressed in order to make machine learning a
mainstream chemical engineering tool.
