Time Quantified Monte Carlo Algorithm for Interacting Spin Array Micromagnetic Dynamics

In this paper, we reexamine the validity of using time quantified Monte Carlo (TQMC) method [Phys. Rev. Lett. 84, 163 (2000); Phys. Rev. Lett. 96, 067208 (2006)] in simulating the stochastic dynamics of interacting magnetic nanoparticles. The Fokker-Planck coefficients corresponding to both TQMC and Langevin dynamical equation (Landau-Lifshitz-Gilbert, LLG) are derived and compared in the presence of interparticle interactions. The time quantification factor is obtained and justified. Numerical verification is shown by using TQMC and Langevin methods in analyzing spin-wave dispersion in a linear array of magnetic nanoparticles.Comment: Accepted for publication in Phys. Rev.

Multispin Coding Technique for Nonequilibrium Reweighting

We present the multispin coding for the nonequlibrium reweighting method of the Monte Carlo simulation, that was developed by the present authors. As an illustration, we treat the driven diffusive lattice gas model. We use the multispin coding technique both for the spin update and for the calculation of the histogram of incremental weights, which is needed in the calculation of nonequlibrium reweighting. All the operations are executed by the bitwise logical commands.Comment: accepted for publication in Int. J. Mod. Phys.

Solving the Master Equation for Extremely Long Time Scale Calculations

The dynamics of magnetic reversal process plays an important role in the design of the magnetic recording devices in the long time scale limit. In addition to long time scale, microscopic effects such as the entropic effect become important in magnetic nano-scale systems. Many advanced simulation methods have been developed, but few have the ability to simulate the long time scale limit and to accurately model the microscopic effects of nano-scale systems at the same time. We develop a new Monte Carlo method for calculating the dynamics of magnetic reversal at arbitrary long time. For example, actual calculations were performed up to 1e50 Monte Carlo steps. This method is based on microscopic interactions of many constituents and the master equation for magnetic probability distribution function is solved symbolically.Comment: accepted for publication in Computer Physics and Communication

Reduced representation of protein structure: implications on efficiency and scope of detection of structural similarity

<p>Abstract</p> <p>Background</p> <p>Computational comparison of two protein structures is the starting point of many methods that build on existing knowledge, such as structure modeling (including modeling of protein complexes and conformational changes), molecular replacement, or annotation by structural similarity. In a commonly used strategy, significant effort is invested in matching two sets of atoms. In a complementary approach, a global descriptor is assigned to the overall structure, thus losing track of the substructures within.</p> <p>Results</p> <p>Using a small set of geometric features, we define a reduced representation of protein structure, together with an optimizing function for matching two representations, to provide a pre-filtering stage in a database search. We show that, in a straightforward implementation, the representation performs well in terms of resolution in the space of protein structures, and its ability to make new predictions.</p> <p>Conclusions</p> <p>Perhaps unexpectedly, a substantial discriminating power already exists at the level of main features of protein structure, such as directions of secondary structural elements, possibly constrained by their sequential order. This can be used toward efficient comparison of protein (sub)structures, allowing for various degrees of conformational flexibility within the compared pair, which in turn can be used for modeling by homology of protein structure and dynamics.</p

Reweighting for Nonequilibrium Markov Processes Using Sequential Importance Sampling Methods

We present a generic reweighting method for nonequilibrium Markov processes. With nonequilibrium Monte Carlo simulations at a single temperature, one calculates the time evolution of physical quantities at different temperatures, which greatly saves the computational time. Using the dynamical finite-size scaling analysis for the nonequilibrium relaxation, one can study the dynamical properties of phase transitions together with the equilibrium ones. We demonstrate the procedure for the Ising model with the Metropolis algorithm, but the present formalism is general and can be applied to a variety of systems as well as with different Monte Carlo update schemes.Comment: accepted for publication in Phys. Rev. E (Rapid Communications

Analytical Solution to Transport in Brownian Ratchets via Gambler's Ruin Model

We present an analogy between the classic Gambler's Ruin problem and the thermally-activated dynamics in periodic Brownian ratchets. By considering each periodic unit of the ratchet as a site chain, we calculated the transition probabilities and mean first passage time for transitions between energy minima of adjacent units. We consider the specific case of Brownian ratchets driven by Markov dichotomous noise. The explicit solution for the current is derived for any arbitrary temperature, and is verified numerically by Langevin simulations. The conditions for vanishing current and current reversal in the ratchet are obtained and discussed.Comment: 4 pages, 3 figure