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  Intrinsic map dynamics exploration for uncharted effective free-energy landscapes

Chiavazzo, E., Covino, R., Coifman, R. R., Gear, C. W., Georgiou, A. S., Hummer, G., et al. (2017). Intrinsic map dynamics exploration for uncharted effective free-energy landscapes. Proceedings of the National Academy of Sciences of the United States of America, 114(28), E5494-E5503. doi:10.1073/pnas.1621481114.

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 Creators:
Chiavazzo, Eliodoro1, Author
Covino, Roberto2, Author           
Coifman, Ronald R.3, Author
Gear, C. William4, Author
Georgiou, Anastasia S. 4, Author
Hummer, Gerhard2, 5, Author           
Kevrekidis, Ioannis G.4, 6, 7, Author
Affiliations:
1Energy Department, Politecnico di Torino, Turin 10129, Italy, ou_persistent22              
2Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society, ou_2068292              
3Department of Mathematics, Program in Applied Mathematics, Yale University, New Haven, CT 06510, ou_persistent22              
4Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, ou_persistent22              
5Institute of Biophysics, Goethe University, 60438 Frankfurt am Main, Germany, ou_persistent22              
6The Program in Applied & Computational Mathematics, Princeton University, Princeton, NJ 08544, ou_persistent22              
7Institute for Advanced Study Technical University of Munich, 85748 Garching, Germany, ou_persistent22              

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Free keywords: free-energy surface; model reduction; machine learning; protein folding; enhanced sampling methods
 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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Language(s): eng - English
 Dates: 2016-12-302017-05-182017-06-202017-07-11
 Publication Status: Published in print
 Pages: 10
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1073/pnas.1621481114
 Degree: -

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Title: Proceedings of the National Academy of Sciences of the United States of America
  Other : Proceedings of the National Academy of Sciences of the USA
  Other : Proc. Acad. Sci. USA
  Other : Proc. Acad. Sci. U.S.A.
  Abbreviation : PNAS
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Washington, D.C. : National Academy of Sciences
Pages: - Volume / Issue: 114 (28) Sequence Number: - Start / End Page: E5494 - E5503 Identifier: ISSN: 0027-8424
CoNE: https://pure.mpg.de/cone/journals/resource/954925427230