  Machine learning models are poised to make a transformative impact on
chemical sciences by dramatically accelerating computational algorithms and
amplifying insights available from computational chemistry methods. However,
achieving this requires a confluence and coaction of expertise in computer
science and physical sciences. This review is written for new and experienced
researchers working at the intersection of both fields. We first provide
concise tutorials of computational chemistry and machine learning methods,
showing how insights involving both can be achieved. We then follow with a
critical review of noteworthy applications that demonstrate how computational
chemistry and machine learning can be used together to provide insightful (and
useful) predictions in molecular and materials modeling, retrosyntheses,
catalysis, and drug design.
