Cluster sizes in a classical Lennard-Jones chain

Lee-Dadswell, G. R.; Barrett, Nicholas C.; Power, Michael

doi:10.1103/physreve.96.032144

Cited by 4 publications

(3 citation statements)

References 46 publications

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“…7(a-c). These peaks are similar to those seen in passive diffusing systems with attractive interactions [38][39][40][41] . Notice that the range of this effective attractive interaction spans several particle sizes.…”

Section: Dynamics Of Tagged Particlessupporting

confidence: 77%

Universal scaling in active single-file dynamics

Dolai,

Das,

Kundu

et al. 2020

Preprint

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We study the single-file dynamics of three classes of active particles: run-and-tumble particles, active Brownian particles and active Ornstein-Uhlenbeck particles. At high activity values, the particles, interacting via purely repulsive forces, aggregate into several motile and dynamical clusters of comparable size. We find that the cluster size distribution of these aggregates is a scaled function of the density and activity parameters across the three models of active particles with the same scaling function. The velocity distribution of these motile clusters is non-Gaussian. We show that the effective dynamics of these clusters can explain the observed emergent scaling of the mean-squared displacement of tagged particles for all the three models with identical scaling exponents and functions. Concomitant with the clustering seen at high activities, we observe that the static density correlation function displays rich structures, including multiple peaks that are reminiscent of particle clustering induced by effective attractive interactions, while the dynamical variant shows non-diffusive scaling. Our study reveals a universal scaling behavior in the single-file dynamics of interacting active particles.

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Section: Dynamics Of Tagged Particlessupporting

confidence: 77%

Universal scaling in active single-file dynamics

Dolai,

Das,

Kundu

et al. 2020

Preprint

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“…where M (si) is a masked transfer matrix with only nonzero entries for a given nearest-neighbor distance s i . From the cluster definition and the relationship between the total number of clusters and the number of unbound nearest neighbors [51], the cumulative GDF can be related to the cluster density…”

Section: Ii2 Gap Distribution Function (Gdf)mentioning

confidence: 99%

Clustering and assembly dynamics of a one-dimensional microphase former

2018

Soft Matter

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Both ordered and disordered microphases ubiquitously form in suspensions of particles that interact through competing short-range attraction and long-range repulsion (SALR). While ordered microphases are more appealing materials targets, understanding the rich structural and dynamical properties of their disordered counterparts is essential to controlling their mesoscale assembly. Here, we study the disordered regime of a one-dimensional (1D) SALR model, whose simplicity enables detailed analysis by transfer matrices and Monte Carlo simulations. We first characterize the signature of the clustering process on macroscopic observables, and then assess the equilibration dynamics of various simulation algorithms. We notably find that cluster moves markedly accelerate the mixing time, but that event chains are of limited help in the clustering regime. These insights will inspire further study of three-dimensional microphase formers.

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“…The non-convexity allows the potential to model fractured states of the material, where two portions of the chain are sufficiently separated and have very weak interactions, as is done in Γ-convergence approaches to the continuum limit of such 1D chains [8; 9]. Among the numerous and diverse topics considered for such LJ lattices, for example the dynamics and mean length of clusters at finite temperature [10], the (homoclinic to exponentially small periodic oscillations) subsonic, as well as (periodic) supersonic lattice traveling waves [11], the potential for chaotic motion through the maximum Lyapunov exponent [12], and as a model for superheated and stretched liquids [13] (whereby the role of the different dynamical configurations must be assessed in the calculation of thermodynamic quantities). A linear approximation of the chain and its solutions for nearest-neighbor (NN) and next-near neighbor (NNN) interactions was explored in [14].…”

Section: Introductionmentioning

confidence: 99%

N -break states in a chain of nonlinear oscillators

Rodrigues

Dobson

2019

Phys. Rev. E

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In the present work we explore a pre-stretched oscillator chain where the nodes interact via a pairwise Lennard-Jones potential. In addition to a homogeneous solution, we identify solutions with one or more (so-called) "breaks", i.e., jumps. As a function of the canonical parameter of the system, namely the precompression strain d, we find that the most fundamental one break solution changes stability when the monotonicity of the Hamiltonian changes with d. We provide a proof for this (motivated by numerical computations) observation. This critical point separates stable and unstable segments of the one break branch of solutions. We find similar branches for 2 through 5 break branches of solutions. Each of these higher "excited state" solutions possesses an additional unstable pair of eigenvalues. We thus conjecture that k break solutions will possess at least k − 1 (and at most k) pairs of unstable eigenvalues. Our stability analysis is corroborated by direct numerical computations of the evolutionary dynamics.

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Cluster sizes in a classical Lennard-Jones chain

Cited by 4 publications

References 46 publications

Universal scaling in active single-file dynamics

Universal scaling in active single-file dynamics

Clustering and assembly dynamics of a one-dimensional microphase former

N -break states in a chain of nonlinear oscillators

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