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

TL;DR

iMapD is a novel computational method that accelerates the exploration of free energy landscapes by combining molecular dynamics with manifold learning to efficiently identify new phase space regions.

Contribution

The paper introduces iMapD, a new approach that integrates MD simulations with nonlinear manifold learning to explore uncharted free energy surfaces more efficiently.

Findings

Successfully accelerates free energy landscape exploration.

Effectively identifies unexplored phase space regions.

Enhances understanding of macroscopic properties from atomistic data.

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.