We describe and implement a computer-assisted approach for accelerating the exploration of uncharted effective free-energy surfaces (FESs). More generally, the aim is the extraction of coarse-grained, macroscopic information from stochastic or atomistic simulations, such as molecular dynamics (MD). The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator toward unexplored phase-space regions by exploiting the smoothness of the gradually revealed intrinsic low-dimensional geometry of the FES.

Intrinsic map dynamics exploration for uncharted effective free-energy landscapes / Chiavazzo, Eliodoro; Covino, Roberto; Coifman, R. Ronald; Gear, C. William; Georgiou, S. Anastasia; Hummer, Gerhard; Kevrekidis, G. Ioannis. - In: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA. - ISSN 0027-8424. - STAMPA. - 114:28(2017), pp. 5494-5503. [10.1073/pnas.1621481114]

Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

CHIAVAZZO, ELIODORO;
2017

Abstract

We describe and implement a computer-assisted approach for accelerating the exploration of uncharted effective free-energy surfaces (FESs). More generally, the aim is the extraction of coarse-grained, macroscopic information from stochastic or atomistic simulations, such as molecular dynamics (MD). The approach functionally links the MD simulator with nonlinear manifold learning techniques. The added value comes from biasing the simulator toward unexplored phase-space regions by exploiting the smoothness of the gradually revealed intrinsic low-dimensional geometry of the FES.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2677060
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