arXiv subjects

A. C. Maggs

Publications and source records attributed to A. C. Maggs.

At least 19 recordsLinked to original sources

arXiv2025

Dynamics of a bricklayer model: multi-walker realizations of true self-avoiding motion

We consider a multi-walker generalization of the true self-avoiding walk: the bricklayer model. We perform stochastic simulations, and solve the partial differential equations that describe the collective motion of $N$ bricklayers/walkers coupled to the contour of an expanding wall. In the large-$N$ limit, the results from simulation agree with the solution of the partial differential equations.

cond-mat.stat-mech↗

arXiv2024

Nonlinear conductivity of aqueous electrolytes: beyond the first Wien effect

The conductivity of strong electrolytes increases under high electric fields, a nonlinear response known as the first Wien effect. Here, using molecular dynamics simulations we show that this increase is almost suppressed in moderately concentrated aqueous electrolytes due to the alignment of the water molecules by the electric field. As a consequence of this alignement, the permittivity of water decreases and becomes anisotropic, an effect which can be measured in simulations and reproduced by a model of water molecules as dipoles. We incorporate the resulting anisotropic interactions between the ions into a Stochastic Density Field Theory and calculate ionic correlations as well as corrections to the Nernst-Einstein conductivity, which are in qualitative agreement with the numerical simulations.

cond-mat.soft↗

arXiv2024

Event-chain Monte Carlo and the true self-avoiding walk

We study the large-scale dynamics of event chain Monte Carlo algorithms in one dimension, and their relation to the true self-avoiding walk. In particular, we study the influence of stress, and different forms of interaction on the equilibration and sampling properties of algorithms with global balance, but no local balance.

physics.comp-ph↗

arXiv2024

Non-reversible Monte Carlo: an example of 'true' self-repelling motion

We link the large-scale dynamics of non-reversible Monte Carlo algorithms as well as a lifted TASEP to an exactly soluble model of self-repelling motion. We present arguments for the connection between the problems and perform simulations, where we show that the empirical distribution functions generated from Monte Carlo are well described by the analytic solution of self-repelling motion.

cond-mat.stat-mech↗

arXiv2023

Molecular simulation from modern statistics: Continuous-time, continuous-space, exact

In a world made of atoms, the computer simulation of molecular systems, such as proteins in water, plays an enormous role in science. Software packages that perform these computations have been developed for decades. In molecular simulation, Newton's equations of motion are discretized and long-range potentials are treated through cutoffs or spacial discretization, which all introduce approximations and artifacts that must be controlled algorithmically. Here, we introduce a paradigm for molecular simulation that is based on modern concepts in statistics and is rigorously free of discretizations, approximations, and cutoffs. Our demonstration software reaches a break-even point with traditional molecular simulation at high precision. We stress the promise of our paradigm as a gold standard for critical applications and as a future competitive approach to molecular simulation.

physics.chem-ph↗

arXiv2023

The Liquid--Hexatic Transition for Soft Disks

We study the liquid--hexatic transition of soft disks with massively parallel simulations and determine the equation of state as a function of system size. For systems with interactions decaying as the inverse $m$th power of the separation, the liquid--hexatic phase transition is continuous for $m = 12$ and $m=8$, while it is of first order for $m = 24$. The critical power $m$ for the transition between continuous and first-order behavior is larger than previously reported. The continuous transition for $ m=12 $ implies that the two-dimensional Lennard-Jones model has a continuous liquid--hexatic transition at high temperatures. We also study the Weeks--Chandler--Andersen model and find a continuous transition at high temperatures, that is consistent with the soft-disk case for $m=12$. Pressure data as well as our implementation are available from an open-source repository.

cond-mat.soft↗

arXiv2023

The virial theorem with periodic boundary conditions

The virial theorem relates averages of kinetic energy and forces in confined systems. It is widely used to relate stresses in molecular simulation as measured at a boundary and in the interior of a system. In periodic systems, the theorem must be modified in order to produce useful results. We formulate the theorem so that it is valid for both confined and for periodic systems. We cross-check our formulation in a study of a small system using both Monte Carlo and molecular dynamics simulations.

physics.chem-ph↗

arXiv2022

Hard-disk computer simulations -- a historic perspective

We discuss historic pressure computations for the hard-disk model performed since 1953, and compare them to results that we obtain with a powerful event-chain Monte Carlo and a massively parallel Metropolis algorithm. Like other simple models in the sciences, such as the Drosophila model of biology, the hard-disk model has needed monumental effort to be understood. In particular, we argue that the difficulty of estimating the pressure has not been fully realized in the decades-long controversy over the hard-disk phase-transition scenario. We present the physics of the hard-disk model, the definition of the pressure and its unbiased estimators, several of which are new. We further treat different sampling algorithms and crucial criteria for bounding mixing times in the absence of analytical predictions. Our definite results for the pressure, for up to one million disks, may serve as benchmarks for future sampling algorithms. A synopsis of hard-disk pressure data as well as different versions of the sampling algorithms and pressure estimators are made available in an open-source repository.

cond-mat.stat-mech↗

arXiv2021

Hard-disk dipoles and non-reversible Markov chains

We benchmark event-chain Monte Carlo (ECMC) algorithms for tethered hard-disk dipoles in two dimensions in view of application of ECMC to water models in molecular simulation. We characterize the rotation dynamics of dipoles through the integrated autocorrelation times of the polarization. The non-reversible straight, reflective, forward, and Newtonian ECMC algorithms are all event-driven, and they differ only in their update rules at event times. They realize considerable speedups with respect to the local reversible Metropolis algorithm. We also find significant speed differences among the ECMC variants. Newtonian ECMC appears particularly well-suited for overcoming the dynamical arrest that has plagued straight ECMC for three-dimensional dipolar models with Coulomb interactions.

cond-mat.stat-mech↗

arXiv2021

Large-scale dynamics of event-chain Monte Carlo

Event-chain Monte Carlo (ECMC) accelerates the sampling of hard-sphere systems, and has been generalized to the potentials used in classical molecular simulation. Rather than imposing detailed balance on transition probabilities, the method enforces a weaker global-balance condition in order to guarantee convergence to equilibrium. In this paper we generalize the factor-field variant of ECMC to higher space dimensions. In the two-dimensional fluid phase, factor-field ECMC saturates the lower bound $z=0$ for the dynamical scaling exponent for local dynamics, whereas molecular dynamics is characterized by $z=1$ and local Metropolis Monte Carlo by $z=2$. In the presence of hexatic order, factor fields are not found to speed up the convergence. We indicate applications to the physics of glasses, and note that generalizations of factor fields could couple to orientational order.

cond-mat.stat-mech↗

arXiv2021

Sparse hard-disk packings and local Markov chains

We propose locally stable sparse hard-disk packings, as introduced by Böröczky, as a model for the analysis and benchmarking of Markov-chain Monte Carlo (MCMC) algorithms. We first generate such packings in a square box with periodic boundary conditions and analyze their properties. We then study how local MCMC algorithms, namely the Metropolis algorithm and several versions of event-chain Monte Carlo (ECMC), escape from configurations that are obtained by slightly reducing all disk radii by a relaxation parameter. A scaling analysis is confirmed by simulation results. We obtain two classes of ECMC, one in which the escape time varies algebraically with the relaxation parameter (as for the local Metropolis algorithm) and another in which the escape time scales as the logarithm of the relaxation parameter. We discuss the connectivity of the hard-disk sample space, the ergodicity of local MCMC algorithms, as well as the meaning of packings in the context of the NPT ensemble. Our work is accompanied by open-source, arbitrary-precision software for Böröczky packings (in Python) and for straight, reflective, forward, and Newtonian ECMC (in Go).

cond-mat.stat-mech↗

arXiv2020

Phase Separation by Entanglement of Active Polymerlike Worms

We investigate the aggregation and phase separation of thin, living T. tubifex worms that behave as active polymers. Randomly dispersed active worms spontaneously aggregate to form compact, highly entangled blobs, a process similar to polymer phase separation, and for which we observe power-law growth kinetics. We find that the phase separation of active polymerlike worms does not occur through Ostwald ripening, but through active motion and coalescence of the phase domains. Interestingly, the growth mechanism differs from conventional growth by droplet coalescence: the diffusion constant characterizing the random motion of a worm blob is independent of its size, a phenomenon that can be explained from the fact that the active random motion arises from the worms at the surface of the blob. This leads to a fundamentally different phase-separation mechanism that may be unique to active polymers.

cond-mat.soft↗

arXiv2020

Multithreaded event-chain Monte Carlo with local times

We present a multithreaded event-chain Monte Carlo algorithm (ECMC) for hard spheres. Threads synchronize at infrequent breakpoints and otherwise scan for local horizon violations. Using a mapping onto absorbing Markov chains, we rigorously prove the correctness of a sequential-consistency implementation for small test suites. On x86 and ARM processors, a C++ (OpenMP) implementation that uses compare-and-swap primitives for data access achieves considerable speed-up with respect to single-threaded code. The generalized birthday problem suggests that for the number of threads scaling as the square root of the number of spheres, the horizon-violation probability remains small for a fixed simulation time. We provide C++ and Python open-source code that reproduces all our results.

physics.comp-ph↗

arXiv2019

JeLLyFysh-Version1.0 -- a Python application for all-atom event-chain Monte Carlo

We present JeLLyFysh-Version1.0, an open-source Python application for event-chain Monte Carlo (ECMC), an event-driven irreversible Markov-chain Monte Carlo algorithm for classical N-body simulations in statistical mechanics, biophysics and electrochemistry. The application's architecture closely mirrors the mathematical formulation of ECMC. Local potentials, long-ranged Coulomb interactions and multi-body bending potentials are covered, as well as bounding potentials and cell systems including the cell-veto algorithm. Configuration files illustrate a number of specific implementations for interacting atoms, dipoles, and water molecules.

physics.comp-ph↗

arXiv2018

Event-chain Monte Carlo with factor fields

We study the dynamics of one-dimensional (1D) interacting particles simulated with the event-chain Monte Carlo algorithm (ECMC). We argue that previous versions of the algorithm suffer from a mismatch in the factor potential between different particle pairs (factors) and show that in 1D models, this mismatch is overcome by factor fields. ECMC with factor fields is motivated, in 1D, for the harmonic model, and validated for the Lennard-Jones model as well as for hard spheres. In 1D particle systems with short-range interactions, autocorrelation times generally scale with the second power of the system size for reversible Monte Carlo dynamics, and with its first power for regular ECMC and for molecular-dynamics. We show, using numerical simulations, that they grow only with the square root of the systems size for ECMC with factor fields. Mixing times, which bound the time to reach equilibrium from an arbitrary initial configuration, grow with the first power of the system size.

cond-mat.stat-mech↗

arXiv2018

Dual free energies in Poisson-Boltzmann theory

Poisson-Boltzmann theory allows one to study soft matter and biophysical systems involving point-like charges of low valencies. The inclusion of fluctuation corrections beyond the mean-field approach typically requires the application of loop expansions around a mean-field solution for the electrostatic potential $ϕ({\bf r})$, or sophisticated variational approaches. Recently, Poisson-Boltzmann theory has been recast, via a Legendre transform, as a mean-field theory involving the dielectric displacement field ${\bf D}({\bf r})$. In this paper we consider the path integral formulation of the dual theory. Exploiting the transformation between $ϕ$ and ${\bf D}$, we formulate a dual Sine-Gordon field theory in terms of the displacement field and provide a strategy for precise numerical computations of free energies beyond the leading order.

cond-mat.soft↗

arXiv2018

All-atom computations with irreversible Markov chains

The event-chain Monte Carlo (ECMC) method is an irreversible Markov process based on the factorized Metropolis filter and the concept of lifted Markov chains. Here, ECMC is applied to all-atom models of multi-particle interactions that include the long-ranged Coulomb potential. We discuss a line-charge model for the Coulomb potential and demonstrate its equivalence with the standard Coulomb model with tin-foil boundary conditions. Efficient factorization schemes for the potentials used in all-atom water models are presented, before we discuss the best choice for lifting schemes for factors of more than three particles. The factorization and lifting schemes are then applied to simulations of point-charge and charged-dipole Coulomb gases, as well as to small systems of liquid water. For a locally charge-neutral system in three dimensions, the algorithmic complexity is O(N log N) in the number N of particles. In ECMC, a Particle-Particle method, it is achieved without the interpolating mesh required for the efficient implementation of other modern Coulomb algorithms. An event-driven, cell-veto-based implementation samples the equilibrium Boltzmann distribution using neither time-step approximations nor spatial cutoffs on the range of the interaction potentials. We discuss prospects and challenges for ECMC in soft condensed-matter and biological physics.

cond-mat.stat-mech↗

arXiv2017

Structural interactions in ionic liquids linked to higher order Poisson-Boltzmann equations

We present a derivation of generalized Poisson-Boltzmann equations starting {from} classical theories of binary fluid mixtures, employing an approach based on the Legendre transform as recently applied to the case of local descriptions of the fluid free energy. Under specific symmetry assumptions, and in the linearized regime, the Poisson-Boltzmann equation reduces to a phenomenological equation introduced by (Bazant et al., 2011), whereby the structuring near the surface is determined by bulk coefficients.

cond-mat.stat-mech↗