Single-chain dynamics in a homogeneous melt and a lamellar microphase: A comparison between Smart Monte Carlo dynamics, slithering-snake dynamics, and slip-link dynamics

2008 ◽  
Vol 129 (16) ◽  
pp. 164906 ◽  
Author(s):  
Marcus Müller ◽  
Kostas Ch. Daoulas
Keyword(s):  
Monte Carlo ◽  
Single Chain ◽  
Chain Dynamics ◽  
Monte Carlo Dynamics

scholarly journals Unfolding of Globular Proteins: Monte Carlo Dynamics of a Realistic Reduced Model

2003 ◽  
Vol 85 (5) ◽  
pp. 3271-3278 ◽  
Author(s):  
Andrzej Kolinski ◽  
Piotr Klein ◽  
Piotr Romiszowski ◽  
Jeffrey Skolnick
Keyword(s):  
Monte Carlo ◽  
Globular Proteins ◽  
Reduced Model ◽  
Monte Carlo Dynamics

A Distributed Computer System for Parallel Markov Chain Monte Carlo (MCMC)

2018 ◽  
Author(s):  
Michael Hynes
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Parameter Space ◽  
Heterogeneous Computing ◽  
Peer To Peer ◽  
Parallel Tempering ◽  
Single Chain ◽  
Distributed Computing Systems ◽  
Mcmc Algorithms

A ubiquitous problem in physics is to determine expectation values of observables associated with a system. This problem is typically formulated as an integration of some likelihood over a multidimensional parameter space. In Bayesian analysis, numerical Markov Chain Monte Carlo (MCMC) algorithms are employed to solve such integrals using a fixed number of samples in the Markov Chain. In general, MCMC algorithms are computationally expensive for large datasets and have difficulties sampling from multimodal parameter spaces. An MCMC implementation that is robust and inexpensive for researchers is desired. Distributed computing systems have shown the potential to act as virtual supercomputers, such as in the SETI@home project in which millions of private computers participate. We propose that a clustered peer-to-peer (P2P) computer network serves as an ideal structure to run Markovian state exchange algorithms such as Parallel Tempering (PT). PT overcomes the difficulty in sampling from multimodal distributions by running multiple chains in parallel with different target distributions andexchanging their states in a Markovian manner. To demonstrate the feasibility of peer-to-peer Parallel Tempering (P2P PT), a simple two-dimensional dataset consisting of two Gaussian signals separated by a region of low probability was used in a Bayesian parameter fitting algorithm. A small connected peer-to-peer network was constructed using separate processes on a linux kernel, and P2P PT was applied to the dataset. These sampling results were compared with those obtained from sampling the parameter space with a single chain. It was found that the single chain was unable to sample both modes effectively, while the P2P PT method explored the target distribution well, visiting both modes approximately equally. Future work will involve scaling to many dimensions and large networks, and convergence conditions with highly heterogeneous computing capabilities of members within the network.

scholarly journals On the Difference between Self-Assembling Process of Monomeric and Dimeric Surfactants with the Same Head to Tail Ratio: A Lattice Monte Carlo Simulation

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Reza Behjatmanesh-Ardakani ◽  
Maryam Farsad
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Gemini Surfactants ◽  
Chain Structure ◽  
Single Chain ◽  
Lattice Monte Carlo ◽  
Self Assembling ◽  
Spacer Group ◽  
The Difference ◽  
Dimeric Surfactants

Experimental data show that gemini surfactants have critical micelle concentrations that are almost tenfold lower than the CMCs of single chain ones. It is believed that the spacer groups play an important role in this subject. Short hydrophilic or long hydrophobic spacers can reduce CMC dramatically. In this paper, self-assembling processes of double-chain and one-chain surfactants with the same head to tail ratio are compared. Dimeric chain structure is exactly double of single chain. In other words, hydrophilic-lyophilic balances of two chain models are the same. Two single chains are connected head-to-head to form a dimeric chain, without introducing extra head or tail beads as a spacer group. Premicellar, micellar, and shape/phase transition ranges of both models are investigated. To do this, lattice Monte Carlo simulation in canonical ensemble has been used. Results show that without introducing extra beads as spacer group, the CMC of (H3T3)2as a dimeric surfactant is much lower than the CMC of its similar single chain, H3T3. For dimeric case of study, it is shown that bolaform aggregates are formed.

scholarly journals Weak Convergence Rates of Population Versus Single-Chain Stochastic Approximation MCMC Algorithms

2014 ◽  
Vol 46 (04) ◽  
pp. 1059-1083 ◽  
Author(s):  
Qifan Song ◽  
Mingqi Wu ◽  
Faming Liang
Keyword(s):  
Monte Carlo ◽  
Weak Convergence ◽  
Stochastic Approximation ◽  
Convergence Rates ◽  
Gain Factor ◽  
Single Chain ◽  
Practical Applications ◽  
Mcmc Algorithms ◽  
Factor Sequence ◽  
Number Of Iterations

In this paper we establish the theory of weak convergence (toward a normal distribution) for both single-chain and population stochastic approximation Markov chain Monte Carlo (MCMC) algorithms (SAMCMC algorithms). Based on the theory, we give an explicit ratio of convergence rates for the population SAMCMC algorithm and the single-chain SAMCMC algorithm. Our results provide a theoretic guarantee that the population SAMCMC algorithms are asymptotically more efficient than the single-chain SAMCMC algorithms when the gain factor sequence decreases slower than O(1 / t), where t indexes the number of iterations. This is of interest for practical applications.

scholarly journals Single cluster Monte Carlo dynamics for the Ising model

1990 ◽  
Vol 60 (5-6) ◽  
pp. 889-889 ◽  
Author(s):  
P. Tamayo ◽  
R. C. Brower ◽  
W. Klein
Keyword(s):  
Monte Carlo ◽  
Ising Model ◽  
Monte Carlo Dynamics ◽  
Cluster Monte Carlo ◽  
Single Cluster
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