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Eirik G. Flekkoy; Peter V. Coveney; Gianni De Fabritiis
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We derive a mesoscopic modeling and simulation technique that is very close to the technique known as dissipative particle dynamics. The model is derived from molecular dynamics by means of a systematic coarse-graining procedure. Thus the rules governing our new form of dissipative particle dynamics reflect the underlying molecular dynamics; in particular all the underlying conservation laws carry over from the microscopic to the mesoscopic descriptions. Whereas previously the dissipative...
Source: http://arxiv.org/abs/cond-mat/0002174v1
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Rudolf Gorenflo; Gianni De Fabritiis; Francesco Mainardi
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We propose a variety of models of random walk, discrete in space and time, suitable for simulating stable random variables of arbitrary index $\alpha$ ($0 < \alpha \le 2$), in the symmetric case. We show that by properly scaled transition to vanishing space and time steps our random walk models converge to the corresponding continuous Markovian stochastic processes, that we refer to as Levy-Feller diffusion processes.
Source: http://arxiv.org/abs/cond-mat/9903264v1
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Gianni De Fabritiis; Rafael Delgado-Buscalioni; Peter V. Coveney
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We present a method to search low energy configurations of polar molecules in the complex potential energy surfaces associated with dense fluids. The search is done in the configurational space of the translational and rotational degrees of freedom of the molecule, combining steepest-descent and Newton-Raphson steps which embed information on the average sizes of the potential energy wells obtained from prior inspection of the liquid structure. We perform a molecular dynamics simulation of a...
Source: http://arxiv.org/abs/physics/0411027v1
The initial structure of Chignolin was generated starting from the cln025 peptide, with sequence TYR-TYR-ASP-PRO-GLU-THR-GLY-THR-TRP-TYR. The structure was solvated in a cubic box of 40A, containing 1881 water molecules and two Na+ ions to neutralize the peptide's negative charge. MD simulations were performed with ACEMD, using CHARMM22* force field and TIP3P water model at 350K temperature. A Langevin integrator was used with a damping constant of 0.1 1/ps. Integration time step was set to 4...
Source: https://figshare.com/articles/dataset/Chignolin_Simulations/13858898/1
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Guillermo Perez-Hernandez; Fabian Paul; Toni Giorgino; Gianni de Fabritiis; Frank Noé
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A goal in the kinetic characterization of a macromolecular system is the description of its slow relaxation processes, involving (i) identification of the structural changes involved in these processes, and (ii) estimation of the rates or timescales at which these slow processes occur. Most of the approaches to this task, including Markov models, Master-equation models, and kinetic network models, start by discretizing the high-dimensional state space and then characterize relaxation processes...
Source: http://arxiv.org/abs/1302.6614v1
