Stochastic boundary conditions for molecular dynamics simulations of ST2 water

By A. Brunger, C. Brooks, Martin Karplus. 1984

Subfields: Computational Chemistry & Drug Discovery, AI for Physical Sciences

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Citations in the corpus

Stochastic Normalizing Flows (2020, neutral): “The forward step ( x t , v t ) → ( x t +1 , v t +1 ) using the leap frog integrator is deﬁned by (Br¨unger et al., 1984): where p ( η t ) = p ( η (cid:48) t ) = N (0 , I ) .”
Scalable molecular dynamics on CPU and GPU architectures with NAMD. (2020, methodology): “59 A less computationally demanding approach incorporated in NAMD and referred to as stochastic velocity rescaling 60 leans on a stochastic process to infer the rescaling parameter.”
A Gentle Stochastic Thermostat for Molecular Dynamics (2009, methodology): “The best known representative of this class of methods is the Langevin-thermostat [1].”
Nonequilibrium candidate Monte Carlo is an efficient tool for equilibrium simulation (2011, unknown): “pðxðnþ1Þ; λðnþ1ÞÞ 1⁄4 πðxðnþ1Þ; λðnþ1ÞÞ: [38]”
A simple and effective Verlet-type algorithm for simulating Langevin dynamics (2012, methodology): “(24) shows that our formulation is also in close proximity to the BBK scheme [6], which is of a positionVerlet-type.”
MULTISCALE FLOW SIMULATIONS USING PARTICLES (2005, background): “Please see Brunger et al. (1984) and Berkowitz & McCammon (1982) for the application of stochastic boundary conditions to a water model.”
Scalable molecular dynamics with NAMD (2005, methodology): “To integrate the Langevin equation, NAMD uses the Brünger-Brooks-Karplus (BBK) method [38], a natural extension of the Verlet method for the Langevin equation.”