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Modeling and enhanced sampling of molecular systems with smooth and nonlinear data-driven collective variables

Author
Hashemian, B.; Millán, D.; Arroyo, M.
Type of activity
Journal article
Journal
Journal of chemical physics
Date of publication
2013
Volume
139
First page
214101
Last page
214101-12
DOI
https://doi.org/10.1063/1.4830403 Open in new window
Repository
http://hdl.handle.net/2117/20940 Open in new window
URL
http://scitation.aip.org/content/aip/journal/jcp/139/21/10.1063/1.4830403 Open in new window
Abstract
Collective variables (CVs) are low-dimensional representations of the state of a complex system, which help us rationalize molecular conformations and sample free energy landscapes with molecular dynamics simulations. Given their importance, there is need for systematic methods that effectively identify CVs for complex systems. In recent years, nonlinear manifold learning has shown its ability to automatically characterize molecular collective behavior. Unfortunately, these methods fail to provi...
Citation
Hashemian, B.; Millán, D.; Arroyo, M. Modeling and enhanced sampling of molecular systems with smooth and nonlinear data-driven collective variables. "Journal of chemical physics", 2013, vol. 139, p. 214101-214101-12.
Group of research
LACÀN - Numerical Methods for Applied Sciences and Engineering

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