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

iMapD: intrinsic Map Dynamics exploration for uncharted effective free energy landscapes