Developments in machine learning (ML) over the past decade have ushered in both profound and superficial changes in the means of chemical research. Profound, because of the pervasiveness of ML in virtually every branch and aspect of the scientific enterprise. Superficial, because in many cases ML is being aimed at problems for which accurate and often more robust solutions already exist. Nevertheless, the position of this perspective is that ML has already achieved convincing success on one frontier of problems and has created even more exciting opportunities along a second frontier.