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Journal ArticleDOI

Fast Calculation of the Density of States of a Fluid by Monte Carlo Simulations

Qiliang Yan, +1 more
- 23 Jan 2003 - 
- Vol. 90, Iss: 3, pp 035701-035701
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TLDR
Two related methods to calculate the density of states of a fluid from Monte Carlo simulations based on evaluation of the instantaneous temperature based on the gradient of the forces are proposed.
Abstract
Two related methods are proposed to calculate the density of states of a fluid from Monte Carlo simulations. In contrast to previous approaches, which require that histograms be accumulated in a stochastic manner, the methods proposed here rely on evaluation of the instantaneous temperature. In the first method, the temperature is calculated from the gradient of the forces. In the second, it is estimated from the kinetic contribution to the total energy. The validity and usefulness of the new approaches are demonstrated by presenting results from simulations of a Lennard-Jones fluid. It is shown that the new methods are considerably faster than previously available techniques.

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Parallel tempering: Theory, applications, and new perspectives

TL;DR: A selected set of the many applications that have become possible with the introduction of parallel tempering are mentioned, and several promising avenues for future research are suggested.
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Effects of confinement on freezing and melting.

TL;DR: Both simple and more complex adsorbates that are confined in various environments (slit or cylindrical pores and also disordered porous materials) are considered and how confinement affects the glass transition is addressed.
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Calculation of free energy through successive umbrella sampling.

TL;DR: An implementation of umbrella sampling in which the pertinent range of states is subdivided into small windows that are sampled consecutively and linked together is considered, which is comparable to a multicanonical simulation with a very good weight function.
Journal ArticleDOI

Optimal allocation of replicas in parallel tempering simulations

TL;DR: A scheme is proposed for the optimal allocation of temperatures in parallel tempering simulations for a variety of systems including a coarse-grained protein, an atomistic model polypeptide, and the Lennard-Jones fluid.
Journal ArticleDOI

Stochastic Approximation in Monte Carlo Computation

TL;DR: The stochastic approximation Monte Carlo (SAMC) algorithm is proposed, which overcomes the shortcomings of the WL algorithm and establishes a theorem concerning its convergence.
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