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

Autor
Hashemian, B.; Millán, D.; Arroyo, M.
Tipus d'activitat
Article en revista
Revista
Journal of chemical physics
Data de publicació
2013
Volum
139
Pàgina inicial
214101
Pàgina final
214101-12
DOI
https://doi.org/10.1063/1.4830403 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/20940 Obrir en finestra nova
URL
http://scitation.aip.org/content/aip/journal/jcp/139/21/10.1063/1.4830403 Obrir en finestra nova
Resum
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...
Citació
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.
Grup de recerca
LACÀN - Mètodes Numèrics en Ciències Aplicades i Enginyeria

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