  Molecular Dynamics (MD) simulation is widely used to analyze the properties
of molecules and materials. Most practical applications, such as comparison
with experimental measurements, designing drug molecules, or optimizing
materials, rely on statistical quantities, which may be prohibitively expensive
to compute from direct long-time MD simulations. Classical Machine Learning
(ML) techniques have already had a profound impact on the field, especially for
learning low-dimensional models of the long-time dynamics and for devising more
efficient sampling schemes for computing long-time statistics. Novel ML methods
have the potential to revolutionize long-timescale MD and to obtain
interpretable models. ML concepts such as statistical estimator theory,
end-to-end learning, representation learning and active learning are highly
interesting for the MD researcher and will help to develop new solutions to
hard MD problems. With the aim of better connecting the MD and ML research
areas and spawning new research on this interface, we define the learning
problems in long-timescale MD, present successful approaches and outline some
of the unsolved ML problems in this application field.
