Algorithms for Brownian dynamics

We derive a class of efficient and stable algorithms of Brownian dynamics using a formula, derived by Suzuki, to express time-ordered operators. These algorithms are simpler than those derived by Helfand from Runge-Kutta algorithms and, like Helfand algorithms, can be combined with SHAKE to describe the Brownian dynamics of constrained systems.     

Brownian dynamics with constraints

Systems possessing degrees of freedom operating on widely separated timescales, where the effects of those operating on the smaller timescales are relatively unimportant, may be modelled by the use of the Langevin equation. In order to study such systems containing complex polyatomic particles, holonomic constraints may be used. Though there is no lack of published algorithms for the numerical solution of the Langevin equation, few of them have been developed with sufficient rigour to ensure their precision, nor to demonstrate their compatibility with constraints. This study recapitulates an approach based upon Runge-Kutta equations which has the advantage of being perfectible to any desired order in the time-step, and shows how it may be combined with the SHAKE method in order to perform constrained Brownian dynamics simulations. Results are presented for some simple systems with a third order algorithm, and it is found that the correct dynamic and statistical behaviour is recovered.     

Modeling an efficient Brownian heat engine

We discuss various properties of a homogeneous random multifractal process, which are related to the issue of scale correlations. By design, the process has no built-in scale correlations. However, when it comes to observables like breakdown coefficients, which are based on a coarse-graining of the multifractal field, scale correlations do appear. In the log-normal limit of the model process, the conditional distributions and moments of breakdown coefficients reproduce the observations made in fully developed small-scale turbulence. These findings help to understand several puzzling empirical details, which have been extracted from turbulent data already some time ago.     

Brownian dynamics of a microswimmer

We report on dynamic properties of a simple model microswimmer composed of three spheres and propelling itself in a viscous fluid by spinning motion of the spheres under zero net torque constraint. At a fixed temperature and increasing the spinning frequency, the swimmer demonstrates a transition from dissipation-dominated to a pumping-dominated motion regime characterized by negative effective friction coefficient. In the limit of high frequencies, the diffusion of the swimmer can be described by a model of an active particle with constant velocity.     

Rotational Brownian dynamics of semiflexible broken rods

Using the Brownian dynamics simulation technique, we study the rotational dynamics of a semiflexible broken rod. We employ a suitable bead model with stiff springs between beads and strong forces opposing to bending, except at the joint where flexibility is variable. We consider mostly broken rods with equal arms. From the simulated Brownian trajectories we obtain the correlation function for the second order Legendre polynomial of the reorientational angle of the end-to-end vector and of the arm vector. These correlation functions are closely related to fluorescence anisotropy decay and electric birefringence decay, respectively. In the first case, the relaxation time for a completely flexible rod agrees with the Harvey-Wegener theory, and in the second, the longest relaxation time agrees well with that obtained from the rigid-body treatment over the whole range of flexibility. Furthermore, we discuss the relative importance of flexibility in both types of decay. Finally, we present results for a case with unequal arms, confirming the validity of the Harvey-Wegener theory and the rigid-body treatment.     

Brownian dynamics simulation of electrostatically interacting proteins

Brownian dynamics simulation software has been developed to study the dynamics of proteins as a whole in solution. The proteins were modelled as spheres with point dipoles embedded in the centre of sphere. A set of Brownian dynamics simulations at different values of the dipole moments, protein concentration and translational diffusion coefficient was performed to investigate the influence of interprotein electrostatic interactions on dynamic protein behaviour in solution. It was shown that these interactions led to the slowing down of protein rotation and a complex non-exponential shape of the rotational correlation function. Analysis of the correlation functions was performed within the frame of the model of electrostatic interprotein interactions advanced earlier on the basis of NMR and dielectric spectroscopy data. This model assumes that, due to electrostatic interactions, protein Brownian rotation becomes anisotropic. The lifetime of this anisotropy is controlled mainly by translational diffusion of proteins. Thus, the correlation function can be decomposed into two components corresponding to anisotropic Brownian rotation and an isotropic motion of an external electric field vector produced by the surrounding proteins.     

Collective dynamics of two-dimensional coupled Brownian motors

The transport properties of coupled Brownian particles in a two-dimensional rocking ratchet are investigated via Langevin simulation. The results show that the average center-of-mass velocity is multi-peaks function of the frequency of the driving force. Furthermore, in the 2D coupled Brownian motor system there are the collective effect and ratchet behavior, which can switch depending on the frequency of driving force. It turns out that the cooperative effect between the interaction of coupled particles and external rocking driving force facilitates collective directional motion and energy conversion.     

Brownian dynamics simulation of linear chain growth

Growth of linear chains is simulated by the technique of Brownian dynamics. The influence of the initial volume fraction of particles on the rate of the process and the structure of the chains is studied. Results show that aggregation is swift at the beginning of the process and very slow at the end. This is due to the decrease of the number of flocs in solution and the decrease in the efficiency of collisions when the degree of aggregation increases. The polydispersity of the system increases during aggregation and reaches a maximum value when the reaction is 96% complete. This increase is greater at higher concentrations. Results indicate that for lower concentrations aggregate growth is principally by reactive collision between aggregates of similar molecular weight, while for higher concentrations there is a relatively high fraction of reactive collisions between aggregates with very different molecular weights. The concentration effect on the structure of the chains is studied, evaluating the fractal dimensions of the chains. When the concentration increases there is a transition from very open structures (fractal dimension 1.76) to very tangled structures, where chains are collapsed (fractal dimension 3).     

Brownian dynamics simulation of polydisperse hard spheres

Standard algorithms used for the numerical integration of the Langevin equation require that interactions should slowly vary during the integration time-step. This in not the case for hard-body systems, where there is no clear-cut between the correlation time of the noise and the time-scale of the interactions. Starting with a short time approximation of the Smoluchowski equation, we introduce an algorithm for the simulation of the over-damped Brownian dynamics of polydisperse hard-spheres in absence of hydrodynamic interactions and briefly discuss the extension to the case of external drifts.     

Brownian dynamics simulation of two confined colloidal particles

The dynamics of two confined colloidal particles is studied by means of Brownian dynamics simulation. The autocorrelation function and cross-correlation function of the two colloidal spheres are computed by utilizing the formulae of hydrodynamic diffusion matrix expanded to different orders, as well as the accurate tensor through numerical algorithm. Furthermore, the numerical results are compared with the experimental results and the theoretical approximation. It is found that the relatively simple theoretical approximation gives good predictions when two spheres are far away from each other, but fails when the two spheres are very close.     

A gas of Brownian particles in statistical dynamics

We consider a large number of particles on a one-dimensional latticel Z in interaction with a heat particle; the latter is located on the bond linking the position of the particle to the point to which it jumps. The energy of a single particle is given by a potentialV(x), x∈Z. In the continuum limit, the classical version leads to Brownian motion with drift. A quantum version leads to a local drift velocity which is independent of the applied force. Both these models obey Einstein's relation between drift, diffusion, and applied force. The system obeys the first and second laws of thermodynamics, with the time evolution given by a pair of coupled non linear heat equations, one for the density of the Brownian particles and one for the heat occupation number; the equation for a tagged Brownian particle can be written as a stochastic differential equation.     

Irreversible Monte Carlo algorithms for efficient sampling

Equilibrium systems evolve according to Detailed Balance (DB). This principle guided the development of Monte Carlo sampling techniques, of which the Metropolis–Hastings (MH) algorithm is the famous representative. It is also known that DB is sufficient but not necessary. We construct irreversible deformation of a given reversible algorithm capable of dramatic improvement of sampling from known distribution. Our transformation modifies transition rates keeping the structure of transitions intact. To illustrate the general scheme we design an Irreversible version of Metropolis–Hastings (IMH) and test it on an example of a spin cluster. Standard MH for the model suffers from critical slowdown, while IMH is free from critical slowdown.     

Tracer dynamics in Dyson's model of interacting Brownian particles

We prove that the mean square displacement of a tracer particle grows as logt for larget. We point out a connection to the low-temperature floating phase of the ANNNI model.     

Memory function approach to the dynamics of interacting Brownian particles

We investigate some of the dynamic properties of diffusing particles described by a many-body Smoluchowski equation. The dynamic structure factor is expressed in terms of a memory function which is evaluated in the cases of i) weak interaction and ii) low particle density, but arbitrary interaction. A one-dimensional system with a hard-core pair potential is treated explicitly. Furthermore, by including a periodic single-particle potential, a model is obtained which has relevance to superionic conductors. For this case we discuss how the frequency-dependent conductivity is affected by the correlated motion of particles.     

DNA与组蛋白相互作用的布朗动力学研究

在真核生物中,DNA按左手手征性的方式,缠绕在组蛋白八聚体的周围,形成稳定的核小体结构.文章作者运用布朗动力学,数值模拟了DNA与组蛋白相互作用最终形成核小体的动力学过程,揭示了DNA与组蛋白相互作用的详细图景,并提出了组蛋白八聚体旋转模型,以解释这一过程.文章作者还计算了组成核小体的DNA在受到拉伸力时,组蛋白被从核小体中剥离下来的动力学过程,得到了组装和剥离过程的详细图像,给出了与前人单分子实验一致的拉伸力与拉伸长度的关系曲线和拉伸台阶.此外,还通过建立的组蛋白手征性模型,模拟了核小体手征性的形成过程,发现DNA的缠绕方向强烈依赖于组蛋白的手征性,显示出环境温度对核小体手征性有重要影响.     

Statistical-temperature Monte Carlo and molecular dynamics algorithms

A simulation method is presented that achieves a flat energy distribution by updating the statistical temperature instead of the density of states in Wang-Landau sampling. A novel molecular dynamics algorithm (STMD) applicable to complex systems and a Monte Carlo algorithm are developed from this point of view. Accelerated convergence for large energy bins, essential for large systems, is demonstrated in tests on the Ising model, the Lennard-Jones fluid, and bead models of proteins. STMD shows a superior ability to find local minima in proteins and new global minima are found for the 55 bead AB model in two and three dimensions. Calculations of the occupation probabilities of individual protein inherent structures provide new insights into folding and misfolding.