most citedCanonical sampling through velocity-rescaling

18.8k citations · 23.3k across the 7 of their papers we have counts for

collaborators

7 papers

cond-mat.stat-mech200844 cited

Optimal Langevin modelling of out-of-equilibrium molecular dynamics simulations

Cristian Micheletti, Giovanni Bussi, Alessandro Laio

We introduce a scheme for deriving an optimally-parametrised Langevin dynamics of few collective variables from data generated in molecular dynamics simulations. The drift and the…

cond-mat.stat-mech2008437 cited

Equilibrium free energies from non-equilibrium metadynamics

Giovanni Bussi, Alessandro Laio, Michele Parrinello

In this paper we propose a new formalism to map history-dependent metadynamics in a Markovian process. We apply this formalism to a model Langevin dynamics and determine the equili…

physics.comp-ph20082 cited

Conjugate gradient heatbath for ill-conditioned actions

Michele Ceriotti, Giovanni Bussi, Michele Parrinello

We present a method for performing sampling from a Boltzmann distribution of an ill-conditioned quadratic action. This method is based on heatbath thermalization along a set of con…

physics.comp-ph2008151 cited

Stochastic thermostats: comparison of local and global schemes

Giovanni Bussi, Michele Parrinello

We show that a recently introduced stochastic thermostat [J. Chem. Phys. 126 (2007) 014101] can be considered as a global version of the Langevin thermostat. We compare the global…

physics.comp-ph2008462 cited

Accurate sampling using Langevin dynamics

Giovanni Bussi, Michele Parrinello

We show how to derive a simple integrator for the Langevin equation and illustrate how it is possible to check the accuracy of the obtained distribution on the fly, using the conce…

cond-mat.stat-mech200818.8k cited

Canonical sampling through velocity-rescaling

Giovanni Bussi, Davide Donadio, Michele Parrinello

We present a new molecular dynamics algorithm for sampling the canonical distribution. In this approach the velocities of all the particles are rescaled by a properly chosen random…