  We describe and implement iMapD, a computer-assisted approach for
accelerating the exploration of uncharted effective Free Energy Surfaces (FES),
and more generally for the extraction of coarse-grained, macroscopic
information from atomistic or stochastic (here Molecular Dynamics, MD)
simulations. The approach functionally links the MD simulator with nonlinear
manifold learning techniques. The added value comes from biasing the simulator
towards new, unexplored phase space regions by exploiting the smoothness of the
(gradually, as the exploration progresses) revealed intrinsic low-dimensional
geometry of the FES.
