期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 114, 期 28, 页码 E5494-E5503出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1621481114
关键词
free-energy surface; model reduction; machine learning; protein folding; enhanced sampling methods
资金
- Institute for Advanced Study Technical University of Munich
- US Air Force Office of Scientific Research [FA9950-17-1-00114]
- US National Science Foundation [ECCS-1462241]
- Defense Advanced Research Projects Agency
- Italian Ministry of Education through NANO-BRIDGE Project PRIN [2012LHPSJC]
- Max Planck Society
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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