The net charge of solvated entities, ranging from polyelectrolytes and biomolecules to charged nanoparticles and membranes, depends on the local dissociation equilibrium of individual ionizable groups. Incorporation of this phenomenon, emph{charge regulation}, in theoretical and computational models requires dynamic, configuration-dependent recalculation of surface charges and is therefore typically approximated by assuming constant net charge on particles. Various computational methods exist that address this. We present an alternative, particularly efficient charge regulation Monte Carlo method (CR-MC), which explicitly models the redistribution of individual charges and accurately samples the correct grand-canonical charge distribution. In addition, we provide an open-source implementation in the LAMMPS molecular dynamics (MD) simulation package, resulting in a hybrid MD/CR-MC simulation method. This implementation is designed to handle a wide range of implicit-solvent systems that model discreet ionizable groups or surface sites. The computational cost of the method scales linearly with the number of ionizable groups, thereby allowing accurate simulations of systems containing thousands of individual ionizable sites. By matter of illustration, we use the CR-MC method to quantify the effects of charge regulation on the nature of the polyelectrolyte coil--globule transition and on the effective interaction between oppositely charged nanoparticles.
Nanoparticles in solution acquire charge through dissociation or association of surface groups. Thus, a proper description of their electrostatic interactions requires the use of charge-regulating boundary conditions rather than the commonly employed constant-charge approximation. We implement a hybrid Monte Carlo/Molecular Dynamics scheme that dynamically adjusts the charges of individual surface groups of objects while evolving their trajectories. Charge-regulation effects are shown to qualitatively change self-assembled structures due to global charge redistribution, stabilizing asymmetric constructs. We delineate under which conditions the conventional constant-charge approximation may be employed and clarify the interplay between charge regulation and dielectric polarization.
Ion mobility and ionic conductance in nanodevices are known to deviate from bulk behavior, a phenomenon often attributed to surface effects. We demonstrate that dielectric mismatch between the electrolyte and the surface can qualitatively alter ionic transport in a counterintuitive manner. Instead of following the polarization-induced modulation of the concentration profile, mobility is enhanced or reduced by changes in the ionic atmosphere near the interface and affected by a polarization force parallel to the surface. In addition to revealing this mechanism, we explore the effect of salt concentration and electrostatic coupling.
A variety of electrostatic phenomena, including the structure of electric double layers and the aggregation of charged colloids and proteins, are affected by nonuniform electric permittivity. These effects are frequently ignored in analytical and computational studies, and particularly difficult to handle in situations where multiple dielectric contrasts are present, such as in colloids that are heterogeneous in permittivity. We present an extension to the Iterative Dielectric Solver developed by Barros and Luijten [Phys. Rev. Lett. 113, 017801 (2014)] that makes it possible to accurately compute the polarization of anisotropic particles with multiple dielectric contrasts. This efficient boundary-element method-based approach is applicable to geometries that are not amenable to other solvers, opening the possibility of studying collective phenomena of dielectrically anisotropic particles. We provide insight into the underlying physical reasons for this efficiency.
Anisotropic colloidal particles constitute an important class of building blocks for self-assembly directed by electrical fields. The aggregation of these building blocks is driven by induced dipole moments, which arise from an interplay between dielectric effects and the electric double layer. For particles that are anisotropic in shape, charge distribution, and dielectric properties, calculation of the electric double layer requires coupling of the ionic dynamics to a Poisson solver. We apply recently proposed methods to solve this problem for experimentally employed colloids in static and time-dependent electric fields. This allows us to predict the effects of field strength and frequency on the colloidal properties.
Electrostatic interactions play an important role in numerous self-assembly phenomena, including colloidal aggregation. Although colloids typically have a dielectric constant that differs from the surrounding solvent, the effective interactions that arise from inhomogeneous polarization charge distributions are generally neglected in theoretical and computational studies. We introduce an efficient technique to resolve polarization charges in dynamical dielectric geometries, and demonstrate that dielectric effects emph{qualitatively} alter the predicted self-assembled structures, with surprising colloidal strings arising from many-body effects.
Electrostatic interactions between dielectric objects are complex and of a many-body nature, owing to induced surface bound charge. We present a collection of techniques to simulate dynamical dielectric objects. We calculate the surface bound charge from a matrix equation using the Generalized Minimal Residue method (GMRES). Empirically, we find that GMRES converges very quickly. Indeed, our detailed analysis suggests that the relevant matrix has a very compact spectrum for all non-degenerate dielectric geometries. Each GMRES iteration can be evaluated using a fast Ewald solver with cost that scales linearly or near-linearly in the number of surface charge elements. We analyze several previously proposed methods for calculating the bound charge, and show that our approach compares favorably.
We introduce a new Markov-Chain Monte Carlo (MCMC) approach designed for efficient sampling of highly correlated and multimodal posteriors. Parallel tempering, though effective, is a costly technique for sampling such posteriors. Our approach minimizes the use of parallel tempering, only using it for a short time to tune a new jump proposal. For complex posteriors we find efficiency improvements up to a factor of ~13. The estimation of parameters of gravitational-wave signals measured by ground-based detectors is currently done through Bayesian inference with MCMC one of the leading sampling methods. Posteriors for these signals are typically multimodal with strong non-linear correlations, making sampling difficult. As we enter the advanced-detector era, improved sensitivities and wider bandwidths will drastically increase the computational cost of analyses, demanding more efficient search algorithms to meet these challenges.
