Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

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

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

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 coarsegrained, 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.

Original languageEnglish (US)
Pages (from-to)E5494-E5503
JournalProceedings of the National Academy of Sciences of the United States of America
Volume114
Issue number28
DOIs
StatePublished - Jul 11 2017

All Science Journal Classification (ASJC) codes

  • General

Keywords

  • Enhanced sampling methods
  • Free-energy surface
  • Machine learning
  • Model reduction
  • Protein folding

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