  Machine learning encompasses a set of tools and algorithms which are now
becoming popular in almost all scientific and technological fields. This is
true for molecular dynamics as well, where machine learning offers promises of
extracting valuable information from the enormous amounts of data generated by
simulation of complex systems. We provide here a review of our current
understanding of goals, benefits, and limitations of machine learning
techniques for computational studies on atomistic systems, focusing on the
construction of empirical force fields from ab-initio databases and the
determination of reaction coordinates for free energy computation and enhanced
sampling.
