  Machine learning (ML) methods are being used in almost every conceivable area
of electronic structure theory and molecular simulation. In particular, ML has
become firmly established in the construction of high-dimensional interatomic
potentials. Not a day goes by without another proof of principle being
published on how ML methods can represent and predict quantum mechanical
properties - be they observable, such as molecular polarizabilities, or not,
such as atomic charges. As ML is becoming pervasive in electronic structure
theory and molecular simulation, we provide an overview of how atomistic
computational modeling is being transformed by the incorporation of ML
approaches. From the perspective of the practitioner in the field, we assess
how common workflows to predict structure, dynamics, and spectroscopy are
affected by ML. Finally, we discuss how a tighter and lasting integration of ML
methods with computational chemistry and materials science can be achieved and
what it will mean for research practice, software development, and postgraduate
training.
