  Machine learning techniques applied to chemical reactions has a long history.
The present contribution discusses applications ranging from small molecule
reaction dynamics to platforms for reaction planning. ML-based techniques can
be of particular interest for problems which involve both, computation and
experiments. For one, Bayesian inference is a powerful approach to include
knowledge from experiment in improving computational models. ML-based methods
can also be used to handle problems that are formally intractable using
conventional approaches, such as exhaustive characterization of state-to-state
information in reactive collisions. Finally, the explicit simulation of
reactive networks as they occur in combustion has become possible using
machine-learned neural network potentials. This review provides an overview of
the questions that can and have been addressed using machine learning
techniques and an outlook discusses challenges in this diverse and stimulating
field. It is concluded that ML applied to chemistry problems as practiced and
conceived today has the potential to transform the way with which the field
approaches problems involving chemical reactions, both, in research and
academic teaching.
