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Saito, T.; Oikawa, S.; Shoda, K.; Sugawara, M.; Miyase, H.; Suzuki, A.

doi:10.1103/physrevc.16.958

Cited by 12 publications

(18 citation statements)

References 24 publications

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“…Our results for anomalous diffusion is consistent with a pattern that the dynamics of magnetisation at the critical temperature in spin models is anomalous [21][22][23]. Importantly, the anomalous diffusion is described by the Generalised Langevin Equation (GLE) [22,23] (and bears strong resemblance to anomalous diffusion in polymeric and membrane systems under a variety of circumstances [3,5,12,[24][25][26][27][28][29][30][31][32][33][34][35][36][37]), which we verify in Sec. IV.…”

Section: Introductionsupporting

confidence: 87%

Approximate dynamical eigenmodes of the Ising model with local spin-exchange moves

2019

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We establish that the Fourier modes of the magnetisation serve as the dynamical eigenmodes for the two-dimensional Ising model at the critical temperature with local spin-exchange moves, i.e., Kawasaki dynamics. We obtain the dynamical scaling properties for these modes, and use them to calculate the time evolution of two dynamical quantities for the system, namely the autocorrelation function and the mean-square deviation of the line magnetisations. At intermediate times 1 t L zc , where z c = 4 − η = 15/4 is the dynamical critical exponent of the model, we find that the line magnetisation undergoes anomalous diffusion. Following our recent work on anomalous diffusion in spin models, we demonstrate that the Generalized Langevin Equation (GLE) with a memory kernel consistently describes the anomalous diffusion, verifying the corresponding fluctuation-dissipation theorem with the calculation of the force autocorrelation function.

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Section: Introductionsupporting

confidence: 87%

Approximate dynamical eigenmodes of the Ising model with local spin-exchange moves

2019

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“…Polymers have a memory kernel K(t) that, for t < τ R , can be well approximated by a power law. 30,31 This power law behavior is a characteristic of systems with a broad spectrum of relaxation times. The generalized Langevin equation (6) with the kernel (8) is a linear equation which can be solved using Laplace transforms.…”

Section: Memory Effects In Transition Path Timesmentioning

confidence: 99%

Effect of Memory and Active Forces on Transition Path Time Distributions

et al. 2018

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An analytical expression is derived for the transition path time distribution for a one-dimensional particle crossing of a parabolic barrier. Two cases are analyzed: (i) a non-Markovian process described by a generalized Langevin equation with a power-law memory kernel and (ii) a Markovian process with a noise violating the fluctuation-dissipation theorem, modeling the stochastic dynamics generated by active forces. In case i, we show that the anomalous dynamics strongly affect the short time behavior of the distributions, but this happens only for very rare events not influencing the overall statistics. At long times the decay is always exponential, in disagreement with a recent study suggesting a stretched exponential decay. In case ii, the active forces do not substantially modify the short time behavior of the distribution but do lead to an overall decrease of the average transition path time. These findings offer some novel insights, useful for the analysis of experiments of transition path times in (bio)molecular systems.

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“…As the polymer is pulled through the pore the net force is simply the pulling force F d applied to the first monomer. However, the net drag on the polymer is proportional to the number of monomers that have been set into motion; monomers outside the range of the tension front do not contribute to the drag [11][12][13][14][15]. WhenF s is low and the polymer is not in a stretched conformation, the number of monomers being dragged increases as translocation proceeds, but it is initially quite low as the pulling force is pulling out the slack in the coil rather than dragging the entire coil.…”

Section: Increasingmentioning

confidence: 99%

“…Applications such as nanopore-based sequencing technologies have led to numerous experimental [3][4][5][6][7][8], theoretical [9][10][11][12][13][14][15][16][17], and computational [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] studies of polymer translocation.…”

Section: Introductionmentioning

confidence: 99%

Reducing the variance in the translocation times by prestretching the polymer

2018

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Langevin dynamics simulations of polymer translocation are performed where the polymer is stretched via two opposing forces applied on the first and last monomer before and during translocation. In this setup, polymer translocation is achieved by imposing a bias between the two pulling forces such that there is net displacement towards the trans side. Under the influence of stretching forces, the elongated polymer ensemble contains less variations in conformations compared to an unstretched ensemble. Simulations demonstrate that this reduced spread in initial conformations yields a reduced variation in translocation times relative to the mean translocation time. This effect is explored for different ratios of the amplitude of thermal fluctuations to driving forces to control for the relative influence of the thermal path sampled by the polymer. Since the variance in translocation times is due to contributions coming from sampling both thermal noise and initial conformations, our simulations offer independent control over the two main sources of noise and allow us to shed light on how they both contribute to translocation dynamics. Simulation parameter space corresponding to experimentally relevant conditions is highlighted and shown to correspond to a significant decrease in the spread of translocation times, thus indicating that stretching DNA prior to translocation could assist nanopore-based sequencing and sizing applications.

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T>giant resonance inNd142

Cited by 12 publications

References 24 publications

Approximate dynamical eigenmodes of the Ising model with local spin-exchange moves

Approximate dynamical eigenmodes of the Ising model with local spin-exchange moves

Effect of Memory and Active Forces on Transition Path Time Distributions

Reducing the variance in the translocation times by prestretching the polymer

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