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

doi:10.1073/pnas.1621481114

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

Chemical & Biological Engineering

Research output: Contribution to journal › Article › peer-review

91 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 language	English (US)
Pages (from-to)	E5494-E5503
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	114
Issue number	28
DOIs	https://doi.org/10.1073/pnas.1621481114
State	Published - 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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10.1073/pnas.1621481114

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title = "Intrinsic map dynamics exploration for uncharted effective free-energy landscapes",

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.",

keywords = "Enhanced sampling methods, Free-energy surface, Machine learning, Model reduction, Protein folding",

author = "Eliodoro Chiavazzo and Roberto Covino and Coifman, {Ronald R.} and Gear, {C. William} and Georgiou, {Anastasia S.} and Gerhard Hummer and Kevrekidis, {Ioannis G.}",

note = "Funding Information: E.C., R.C., and I.G.K. acknowledge the hospitality and support of the Institute for Advanced Study Technical University of Munich. This work was partially supported by the US Air Force Office of Scientific Research (FA9950-17-1-00114), the US National Science Foundation (ECCS-1462241), and Defense Advanced Research Projects Agency (I.G.K.). E.C. acknowledges partial support of the Italian Ministry of Education through NANO-BRIDGE Project PRIN 2012 Grant 2012LHPSJC. R.C. and G.H. were supported by the Max Planck Society.",

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doi = "10.1073/pnas.1621481114",

language = "English (US)",

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T1 - Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

AU - Chiavazzo, Eliodoro

AU - Covino, Roberto

AU - Coifman, Ronald R.

AU - Gear, C. William

AU - Georgiou, Anastasia S.

AU - Hummer, Gerhard

AU - Kevrekidis, Ioannis G.

N1 - Funding Information: E.C., R.C., and I.G.K. acknowledge the hospitality and support of the Institute for Advanced Study Technical University of Munich. This work was partially supported by the US Air Force Office of Scientific Research (FA9950-17-1-00114), the US National Science Foundation (ECCS-1462241), and Defense Advanced Research Projects Agency (I.G.K.). E.C. acknowledges partial support of the Italian Ministry of Education through NANO-BRIDGE Project PRIN 2012 Grant 2012LHPSJC. R.C. and G.H. were supported by the Max Planck Society.

PY - 2017/7/11

Y1 - 2017/7/11

N2 - 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.

AB - 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.

KW - Enhanced sampling methods

KW - Free-energy surface

KW - Machine learning

KW - Model reduction

KW - Protein folding

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JF - Proceedings of the National Academy of Sciences of the United States of America

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ER -

Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

Abstract

All Science Journal Classification (ASJC) codes

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