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arXiv · 1701.01513

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

Eliodoro Chiavazzo·Ronald R. Coifman·Roberto Covino·C. William Gear·Anastasia S. Georgiou·Gerhard Hummer·Ioannis G. Kevrekidis
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Abstract

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.

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Eliodoro Chiavazzo, Ronald R. Coifman, Roberto Covino, C. William Gear, Anastasia S. Georgiou, Gerhard Hummer, Ioannis G. Kevrekidis. 2016-12-31. iMapD: intrinsic Map Dynamics exploration for uncharted effective free energy landscapes. https://doi.org/10.1073/pnas.1621481114

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