  Machine learning (ML) is transforming all areas of science. The complex and
time-consuming calculations in molecular simulations are particularly suitable
for a machine learning revolution and have already been profoundly impacted by
the application of existing ML methods. Here we review recent ML methods for
molecular simulation, with particular focus on (deep) neural networks for the
prediction of quantum-mechanical energies and forces, coarse-grained molecular
dynamics, the extraction of free energy surfaces and kinetics and generative
network approaches to sample molecular equilibrium structures and compute
thermodynamics. To explain these methods and illustrate open methodological
problems, we review some important principles of molecular physics and describe
how they can be incorporated into machine learning structures. Finally, we
identify and describe a list of open challenges for the interface between ML
and molecular simulation.
