A self-learning algorithm for biased molecular dynamics.
A new self-learning algorithm for accelerated dynamics, reconnaissance metadynamics, is proposed that is able to work with a very large number of collective coordinates. Acceleration of the dynamics is achieved by constructing a bias potential in terms of a patchwork of one-dimensional, locally vali...
Auteurs principaux: | Tribello, G, Ceriotti, M, Parrinello, M |
---|---|
Format: | Journal article |
Langue: | English |
Publié: |
2010
|
Documents similaires
-
Using sketch-map coordinates to analyze and bias molecular dynamics simulations.
par: Tribello, G, et autres
Publié: (2012) -
From the Cover: Simplifying the representation of complex free-energy landscapes using sketch-map.
par: Ceriotti, M, et autres
Publié: (2011) -
Demonstrating the Transferability and the Descriptive Power of Sketch-Map
par: Ceriotti, M, et autres
Publié: (2013) -
From the Cover: Simplifying the representation of complex free-energy landscapes using sketch-map.
par: Ceriotti, M, et autres
Publié: (2011) -
Langevin equation with colored noise for constant-temperature molecular dynamics simulations.
par: Ceriotti, M, et autres
Publié: (2009)