Search

Accurate equations of state (EOS) and plasma transport properties are essential for numerical simulations of warm dense matter encountered in many high-energy-density situations. Molecular dynamics (MD) is a simulation method that generates EOS and transport data using an externally provided potential to dynamically evolve the particles without further reference to the electrons. To minimize computational cost, pair potentials needed in MD may be obtained from the neutral-pseudoatom (NPA) approach, a form of single-ion density functional theory (DFT), where many-ion effects are included via ion-ion correlation functionals. Standard $N$-ion DFT-MD provides pair potentials via the force matching technique but at much greater computational cost. Here we propose a simple analytic model for pair potentials with physically meaningful parameters based on a Yukawa form with a thermally damped Friedel tail (YFT) applicable to systems containing free electrons. The YFT model accurately fits NPA pair potentials or the non-parametric force-matched potentials from $N$-ion DFT-MD, showing excellent agreement for a wide range of conditions. The YFT form provides accurate extrapolations of the NPA or force-matched potentials for small and large particle separations within a physical model. Our method can be adopted to treat plasma mixtures, allowing for large-scale simulations of multi-species warm dense matter., 13 figures

In strongly magnetized neutral plasmas, electron motion is reduced perpendicular to the magnetic field direction. This changes dynamical plasma properties such as temperature equilibration, spatial density evolution, electron pressure, and thermal and electrical conductivity. In this paper we report measurements of free plasma expansion in the presence of a strong magnetic field. We image laser-induced fluorescence from an ultracold neutral Ca^{+} plasma to map the plasma size as a function of time for a range of magnetic field strengths. The asymptotic expansion velocity perpendicular to the magnetic field direction falls rapidly with increasing magnetic field strength. We observe that the initially Gaussian spatial distribution remains Gaussian throughout the expansion in both the parallel and perpendicular directions. We compare these observations with a diffusion model and with a self-similar expansion model and show that neither of these models reproduces the observed behavior over the entire range of magnetic fields used in this study. Modeling the expansion of a magnetized ultracold plasma poses a nontrivial theoretical challenge.

We present a theoretical framework to investigate the microscopic structure of concentrated hard-sphere colloidal suspensions under strong shear flows by fully taking into account the boundary-layer structure of convective diffusion. We solve the pair Smoluchowski equation with shear separately in the compressing and extensional sectors of the solid angle, by means of matched asymptotics. A proper, albeit approximate, treatment of the hydrodynamic interactions in the different sectors allows us to construct a potential of mean force containing the effect of the flow field on pair correlations. We insert the obtained pair potential in the Percus-Yevick relation and use the latter as a closure to solve the Ornstein-Zernike integral equation. For a wide range of either the packing fraction $\eta$ and the P\'eclet ($\textrm{Pe}$) number, we compute the pair correlation function and extract scaling laws for its value at contact. For all the considered value of $\textrm{Pe},$ we observe a very good agreement between theoretical findings and numerical results from literature, up to rather large values of $\eta.$ The theory predicts a consistent enhancement of the structure factor $ S(k)$ at $k \to 0,$ upon increasing the $\textrm{Pe}$ number. We argue this behaviour may signal the onset of a phase transition from the isotropic phase to a non-uniform one, induced by the external shear flow.

We present the results of the first Charged-Particle Transport Coefficient Code Comparison Workshop, which was held in Albuquerque, NM October 4-6, 2016. In this first workshop, scientists from eight institutions and four countries gathered to compare calculations of transport coefficients including thermal and electrical conduction, electron-ion coupling, inter-ion diffusion, ion viscosity, and charged particle stopping powers. Here, we give general background on Coulomb coupling and computational expense, review where some transport coefficients appear in hydrodynamic equations, and present the submitted data. Large variations are found when either the relevant Coulomb coupling parameter is large or computational expense causes difficulties. Understanding the general accuracy and uncertainty associated with such transport coefficients is important for quantifying errors in hydrodynamic simulations of inertial confinement fusion and high-energy density experiments., Comment: 45 pages, 17 figures

Molecular Dynamics (MD) is a powerful tool for simulating complex dynamical systems. MD is used across several disciplines and their sub-fields, e.g. biology, chemistry, material science. As a matter of fact for each of these research fields we can find MD codes, open-source or proprietary, optimized for the simulation of their respective dynamical systems. No such code exists for Non-Ideal Plasmas. This generates several difficulties for researchers. On one hand researchers are forced to modify existing codes and/or write their own. This requires an extensive knowledge of computing as all of the available codes have been developed in low-level languages, e.g. C or Fortran. Furthermore, the codes have been optimized for the simulation of systems other than non-ideal plasmas and as such are often difficult to modify. On the other hand most MD codes provide only a code for the simulation phase and lack pre-processing and/or post-processing analysis tools. Several open-source packages are available for post-processing, but again they are specific to systems other than non-ideal plasmas and are often developed in interpretative languages, e.g. Python.

Relaxation in non-ideal plasmas is a complex process that involves expansions/contractions, thermal conduction, thermalization and temperature equilibration. Unfortunately, very little is known experimentally about these processes because of the challenges associated with isolating them individually. As such, we rely heavily on unvalidated theoretical models. This knowledge gap is particularly large for the very challenging case of ionic relaxation. Current improvements in experimental techniques now allow for the creation of ultracold binary mixtures for the investigation of equilibrium relaxation phenomena dominated by ion-ion collisions. These experiments, when complemented by large-scale molecular dynamics (MD) simulations, promise to provide detailed data on these processes. We have modeled a binary relaxing ultracold neutral plasma with MD, motivated by experiments at Brigham Young University, and compared the results to the most common theoretical models. None of the temperature relaxation rates from current theoretical models is within an order of magnitude of the MD simulations, suggesting serious gaps in our knowledge of ionic relaxation. We argue that such disagreement is due to dynamical correlations neglected by the currently available theoretical models 1 , 2 .

Ultracold neutral plasmas 1 (UNPs) are strongly coupled, non- equilibrium systems created by photoionizing laser cooled atoms in a vacuum. As a result, experimental platforms are developed to elucidate the properties of UNPs such as their spatial expansion and temperature relaxation 2 . Data from these experiments are used to validate theories and computational models that aim to model UNPs. One such computational model derived from a multi-species BGK kinetic theory 3 .

Physical data are typically generated by experiments and computations in limited parameter regimes. When datasets generated using such disparate methods are combined into one dataset, the resulting dataset is typically sparse, with dense "islands" in a potentially high-dimensional parameter space, and predictions must be interpolated among such islands. Using plasma transport data as our example, we propose a multifidelity Gaussian-process regression framework that incorporates physical data from multiple sources at multiple fidelities. The impact of the proposed framework varies from little improvement over simpler approaches to qualitatively changing the prediction with consistently increased confidence in regions lacking high-fidelity data. By varying low- and high-fidelity data sources, we demonstrate an approach for determining when multifidelity Gaussian-process regression adds value over single-fidelity regression and therefore when its additional computational costs are merited. We also examine the case in which the outputs of the low- and high-fidelity models correspond to different physical quantities, one of which may be intrinsically computationally cheaper to compute. We conclude by analyzing strategies for sampling high-fidelity data for use in this framework, and we develop a simple sampling approach for reducing regression error across large gaps in data.

We present an open-source, performant, pure-python molecular dynamics (MD) suite for non-ideal plasmas. The code, Sarkas, aims to accelerate the research process by providing an MD code but also pre- and post-processing tools. Sarkas offers the ease of use of Python while employing the Numba library to obtain execution speeds comparable to that of compiled languages. The available tools in Sarkas include graphical displays of the equilibration process through a Jupyter interface and the ability to compute quantities such as, radial distribution functions, autocorrelation functions and Green-Kubo relations. Many force laws used to simulate plasmas are included in Sarkas, namely, pure Coulomb, Yukawa, and Moli\`ere pair-potentials. Sarkas also contains quantum statistical potentials and fast Ewald methods are included where necessary. An object-oriented approach allows for easy modification of Sarkas, such as adding new time integrators, boundary conditions and force laws.

The ionic dynamic structure factor is examined to assess the relative roles of dissipation and the effective ionic interaction. Two disparate physically based models of dissipation, which can differ numerically by orders of magnitude, are used in molecular dynamics. We find a negligible impact on the amplitudes of the dynamic structure factors for physically realistic parameter values. We then examine the effective ionic interaction by varying its strength, the size of the atomic core (through a pseudopotential), and the screening model. We find that ``diffusive'' peaks in the dynamic structure factor are very sensitive to the form of the ionic interaction, and this sensitivity arises primarily from atomic physics through the pseudopotential. This suggests that it would be useful to employ the measured zero-frequency dynamic structure factor ${S}_{ii}(k,0)$ as a constraint on the effective interaction, which in turn can be used to compute physical properties.

Langevin dynamical simulations are performed to study the depinning dynamics of two-dimensional dusty plasmas on a one-dimensional periodic substrate. From the diagnostics of the sixfold coordinated particles $P_6$ and the collective drift velocity $V_x$, three different states appear, which are the pinning, disordered plastic flow, and moving ordered states. It is found that the depth of the substrate is able to modulate the properties of the depinning phase transition, based on the results of $P_6$ and $V_x$, as well as the observation of hysteresis of $V_x$ while increasing and decreasing the driving force monotonically. When the depth of the substrate is shallow, there are two continuous phase transitions. When the potential well depth slightly increases, the phase transition from the pinned to the disordered plastic flow states is continuous; however, the phase transition from the disordered plastic flow to the moving ordered states is discontinuous. When the substrate is even deeper, the phase transition from the pinned to the disordered plastic flow states changes to discontinuous. When the substrate further increases, as the driving force increases, the pinned state changes to the moving ordered state directly, so that the disordered plastic flow state disappears completely., Comment: 9 pages, 5 postscript figures

The head-on collision of compressional shocks in two-dimensional dusty plasmas is investigated using both molecular dynamical and Langevin simulations. Two compressional shocks are generated from the inward compressional boundaries in simulations. It is found that, during the collision of shocks, there is a generally existing time delay of shocks $\ensuremath{\tau}$, which diminishes monotonically with the increasing compressional speed of boundaries, corresponding to the time resolution of the studied system. Dispersive shock waves (DSWs) are generated around the shock front for some conditions. It is also found that the period of the DSW decreases monotonically with the inward compressional speed of boundaries, more substantially than the time delay of shocks $\ensuremath{\tau}$. When the inward compressional speed of boundaries increases further, the DSWs gradually vanish. We speculate that, for these high compressional speeds of boundaries, the period of the DSW might be reduced to a comparable timescale of the time delay of shocks $\ensuremath{\tau}$, i.e., the time resolution of our studied system, or even shorter, thus the DSW reasonably vanishes.

This paper investigates a long-standing question about the effect of surface roughness on turbulent flow: what is the equivalent roughness sand-grain height for a given roughness topography? Deep Neural Network (DNN) and Gaussian Process Regression (GPR) machine learning approaches are used to develop a high-fidelity prediction approach of the Nikuradse equivalent sand-grain height $k_s$ for turbulent flows over a wide variety of different rough surfaces. To this end, 45 surface geometries were generated and the flow over them simulated at $\hbox{Re}_\tau=1000$ using direct numerical simulations. These surface geometries differed significantly in moments of surface height fluctuations, effective slope, average inclination, porosity and degree of randomness. Thirty of these surfaces were considered fully-rough and they were supplemented with experimental data for fully-rough flows over 15 more surfaces available from previous studies. The DNN and GPR methods predicted $k_s$ with an average error of less than 10% and a maximum error of less than 30%, which appears to be significantly more accurate than existing prediction formulas. They also identified the surface porosity and the effective slope of roughness in the spanwise direction as important factors in drag prediction.

Computational models are formulated in hierarchies of variable fidelity, often with no quantitative rule for defining the fidelity boundaries. We have constructed a dataset from a wide range of atomistic computational models to reveal the accuracy boundary between higher-fidelity models and a simple, lower-fidelity model. The symbolic decision boundary is discovered by optimizing a support vector machine on the data through iterative feature engineering. This data-driven approach reveals two important results: (i) a symbolic rule emerges that is independent of the algorithm, and (ii) the symbolic rule provides a deeper understanding of the fidelity boundary. Specifically, our dataset is composed of radial distribution functions from seven high-fidelity methods that cover wide ranges in the features (element, density, and temperature); high-fidelity results are compared with a simple pair-potential model to discover the nonlinear combination of the features, and the machine learning approach directly reveals the central role of atomic physics in determining accuracy.

The authors study the bump-on tail instability using molecular dynamics to explore the roles of collisionality, correlations and the effective interparticle force law across wide ranges of plasma temperatures, densities and charge states, and show that, even with a small number of point particles, the results are robust.

Molecular dynamical simulations are performed to investigate the evolution of unsupported shocks in a two-dimensional Yukawa solid. When a boundary of the solid moves uniformly inward, a compressional shock is generated with a propagating front. An unsupported shock forms if the moving boundary suddenly stops, and a rarefaction wave (RW) is generated near this boundary. The original shock front propagates more slowly than the head of the new RW; as a result, the shock front is eventually overtaken by the RW and a new front appears. It is found that the speed of this new front can be expressed as the average of the previous shock front speed and the longitudinal sound speed, the latter of which has a transition depending on the initial compressional speed of the moving boundary. It appears that this transition of the new shock front speed probably can be attributed to change of the compressibility properties.

A wide-ranging effective Boltzmann approach, originally intended for ionic transport, is applied to the computation of electronic transport coefficients without modification. Comparisons with datasets that resulted from a recent transport coefficient workshop and molecular dynamics simulations are made. While this model contains correlation information through its effective potential and strong scattering through its use of cross sections, it misses details of attractive, possibly quantum, interactions; comparisons with that dataset reveal the relative importance of these physics inputs. Through comparisons of data for electrical conductivity, thermal conductivity, temperature relaxation, and stopping power (including a new formula for the energy split due to alpha stopping), we find that the sensitivity to the missing physics is minor and often negligible. Thus, we have a single transport model that self-consistently provides all ionic and electronic transport properties in a form with negligible computational cost.

The characteristics of atomic-scale mixing are determined by diffusive processes driven by gradients. One such process is interdiffusion, a process driven by density gradients. We consider the various options for formulating interdiffusion in terms of Green–Kubo autocorrelation functions and the thermodynamic factor. Through models for the direct correlation function, we generalize expressions for the thermodynamic factor to include different electron and ion temperatures, electron degeneracy, finite-temperature exchange, and strong coupling. Additionally, a Gaussian autocorrelation function (GAF) is employed for a binary ionic mixture, yielding a simple analytic transport model for interdiffusion. The GAF model is shown to be accurate for moderately and strongly coupled plasmas.

Dynamic density functional theory (DDFT) is emerging as a useful theoretical technique for modeling the dynamics of correlated systems. We extend DDFT to quantum systems for application to dense plasmas through a quantum hydrodynamics (QHD) approach. The DDFT-based QHD approach includes correlations in the the equation of state self-consistently, satisfies sum rules and includes irreversibility arising from collisions. While QHD can be used generally to model non-equilibrium, heterogeneous plasmas, we employ the DDFT-QHD framework to generate a model for the electronic dynamic structure factor, which offers an avenue for measuring hydrodynamic properties, such as transport coefficients via x-ray Thomson scattering.

We derive a conservative multispecies BGK model that follows the spirit of the original, single species BGK model by making the specific choice to conserve species masses, total momentum, and total kinetic energy and to satisfy Boltzmann’s $$\mathcal {H}$$ -Theorem. The derivation emphasizes the connection to the Boltzmann operator which allows for direct inclusion of information from higher-fidelity collision physics models. We also develop a complete hydrodynamic closure via the Chapman-Enskog expansion, including a general procedure to generate symmetric diffusion coefficients based on this model. We numerically investigate velocity and temperature relaxation in dense plasmas and compare the model with previous multispecies BGK models and discuss the trade-offs that are made in defining and using them. In particular, we demonstrate that the BGK model in the NRL plasma formulary does not conserve momentum or energy in general.

The propagation of compressional shocks in two-dimensional (2D) dusty plasmas is investigated using MD simulations under various conditions. The shock Hugoniot curves of the relationship between the shock front speed D and the mean particle speed v[over ¯] after shocks are obtained and analytically fit to parabolic expressions. As the screening parameter increases, the weaker Yukawa interparticle interaction cause the shock Hugoniot curves to be more linear. Combining the obtained shock Hugoniot curves with the Rankine-Hugoniot jump relations, analytic expressions of pressure and energy after the shocks in 2D Yukawa systems are obtained, which are functions of the observable quantities, like the shock front speed D or the mean particle speed v[over ¯] or the specific volume.

Using molecular dynamical simulations, compressional shocks in two-dimensional (2D) dusty plasmas are quantitatively investigated under various conditions. A universal relationship between the thermal and the drift velocities after shocks is discovered in 2D Yukawa systems. Using the equation of state of 2D Yukawa liquids, and the obtained pressure from the Rankine-Hugoniot relation, an analytical relation between the thermal and the drift velocities is derived, which well agrees with the discovered universal relationship for various conditions.

The dynamic structure factor (DSF) of the Yukawa system is here obtained with highly converged molecular dynamics (MD) over the entire liquid phase. The data provide a rigorous test of theoretical models of ion-acoustic wave-dispersion relations, the intermediate scattering function, and the high-frequency response. We compare our MD results with seven diverse models, finding good agreement among those that enforce the three basic sum rules for dispersion properties, although one of the models has previously unreported spurious peaks. The MD simulations reveal that at intermediate frequencies $\ensuremath{\omega}$, the high-frequency response of the DSF follows a power law, going approximately as ${\ensuremath{\omega}}^{\ensuremath{-}p}$, where $pg0$, and $p$ shows nontrivial dependencies on the wave vector $q$ and the plasma parameters $\ensuremath{\kappa}$ and $\mathrm{\ensuremath{\Gamma}}$. In contrast, among the seven comparison models, the predicted high-frequency response is found to be independent of ${q,\ensuremath{\kappa},\mathrm{\ensuremath{\Gamma}}}$. This high-frequency power suggests a useful fitting form. In addition, these results expose limitations of several models and, moreover, suggest that some approaches are difficult or impossible to extend because of the lack of finite moments. We also find the double-plasmon resonance peak in our MD simulations that none of the theoretical models predicts.

We investigate the depinning dynamics of two-dimensional dusty plasmas (2DDP) driven over one-dimensional periodic substrates (1DPS) using Langevin dynamical simulations. We find that, for a specific range of substrate strengths, as the external driving force increases from zero, there are three different states, which are the pinned, the disordered plastic flow, and the moving ordered states, respectively. These three states are clearly observed using different diagnostics, including the collective drift velocity, static structural measures, the particle trajectories, the mean-squared displacements, and the kinetic temperature. We compare the observed depinning dynamics here with the depinning dynamics in other systems., 8 pages, 6 figures

Nonideal plasmas have nontrivial space and time correlations, which simultaneously impact both the excess thermodynamic quantities as well as the collision processes. However, hydrodynamics models for designing and interpreting nonideal plasma experiments, such as inertial-confinement fusion experiments, typically neglect electrodynamics, although some models include electric fields indirectly through a generalized Fick's law. However, because most transport models are not computed self-consistently with the equation of state, there is double counting of the forces in the excess thermodynamic quantities and the collision terms. Here we employ the statistical mechanical hydrodynamic theory of Irving and Kirkwood [J. Chem. Phys. 18, 817 (1950)JCPSA60021-960610.1063/1.1747782] to examine inhomogeneous, nonideal plasmas that contain electric fields. We show that it is not possible to simultaneously separate terms that correspond to electric fields and excess pressure; rather, these quantities arise from the same interparticle Coulomb forces. Moreover, new terms associated with nonlocality appear in the presence of strong inhomogeneities.

Ultracold neutral plasma (UNP) experiments allow for careful control of plasma properties across Coulomb coupling regimes. Here, we examine how UNPs can be used to study heterogeneous, nonequilibrium phenomena, including nonlinear waves, transport, hydrodynamics, kinetics, stopping power, and instabilities. Through a series of molecular dynamics simulations, we have explored UNPs formed with spatially modulated ionizing radiation. We have developed a computational model for such sculpted UNPs that includes an ionic screened Coulomb interaction with a spatiotemporal screening length, and Langevin-based spatial ion-electron and ion-neutral collisions. We have also developed a hydrodynamics model and have extracted its field quantities (density, flow velocity, and temperature) from the molecular dynamics simulation data, allowing us to investigate kinetics by examining moment ratios and phase-space dynamics; we find that it is possible to create UNPs that vary from nearly perfect fluids (Euler limit) to highly kinetic plasmas. We have examined plasmas in three geometries: a solid rod, a hollow rod, and a gapped slab; we have studied basic properties of these plasmas, including the spatial Coulomb coupling parameter. By varying the initial conditions, we find that we can design experimental plasmas that would allow the exploration of a wide range of phenomena, including shock and blast waves, stopping power, two-stream instabilities, and much more. Using an evaporative cooling geometry, our results suggest that much larger Coulomb couplings can be achieved, possibly in excess of 10.

Transport properties of high-energy-density plasmas are influenced by the ion collision rate. Traditionally, this rate involves the Coulomb logarithm, $\ln\Lambda$. Typical values of $\ln\Lambda$ are $\approx 10~\mbox{to}~20$ in kinetic theories where transport properties are dominated by weak-scattering events caused by long-range forces. The validity of these theories breaks down for strongly-coupled plasmas, when $\ln\Lambda$ is of order one. We present measurements and simulations of collision data in strongly-coupled plasmas when $\ln\Lambda$ is small. Experiments are carried out in the first dual-species ultracold neutral plasma (UNP), using Ca$^+$ and Yb$^+$ ions. We find strong collisional coupling between the different ion species in the bulk of the plasma. We simulate the plasma using a two-species fluid code that includes Coulomb logarithms derived from either a screened Coulomb potential or a the potential of mean force. We find generally good agreement between the experimental measurements and the simulations. With some improvements, the mixed Ca$^+$ and Yb$^+$ dual-species UNP will be a promising platform for testing theoretical expressions for $\ln\Lambda$ and collision cross-sections from kinetic theories through measurements of energy relaxation, stopping power, two-stream instabilities, and the evolution of sculpted distribution functions in an idealized environment in which the initial temperatures, densities, and charge states are accurately known., Comment: 8 pages, 4 figures, 75 references

Charged particle transport plays a critical role in the evolution of high energy-density plasmas. As high-fidelity plasma models continue to incorporate new micro-physics, understanding multi-species plasma transport becomes increasingly important. We briefly outline theoretical challenges of going beyond single-component systems and binary mixtures as well as emphasize the roles experiment, simulation, theory, and modeling can play in advancing this field. The 2020 Division of Plasma Physics mini-conference on transport in Transport in Non-Ideal, Multi-Species Plasmas was organized to bring together a broad community focused on modeling plasmas with many species. This special topics issue of Physics of Plasmas touches on aspects of ion transport presented at that mini-conference. This special topics issue will provide some context for future growth in this field.

In the presence of the substrate, various structural and dynamical properties of two-dimensional dusty plasma (2DDP) were investigated using Langevin dynamical simulations. This paper reviews a series of results of the structural and dynamical properties of 2DDP modified by one-dimensional periodic substrates (1DPSs) as follows. First, when the depth of the 1DPS increases gradually, it is found that the static structural order of 2DDP along each potential well of the 1DPS increases first and then decreases gradually. When the width of the 1DPS increases gradually, the particle arrangement in each potential well of the 1DPS changes from one straight row gradually to two rows, including the stable zigzag structure. Second, when there are two rows in each potential well of the 1DPS, the phonon spectra are split into two branches, corresponding to the breathing and sloshing modes, respectively. In addition, due to the pure repulsion between dust particles, from the obtained phonon spectra, the sloshing wave propagates backward at small wave numbers. Third, the calculated mean square displacement shown that, at the intermediate timescale between the initial ballistic and final diffusive motions, the particle motion tends to be more subdiffusive while the depth of the 1DPS increases. While the width of the 1DPS increases gradually, the long-time diffusive motion first increases, then decreases, and finally increases again, exhibiting the oscillation-like diffusion, due to the stable zigzag structure. Finally, when an external direct-current driving force is applied on all particles of 2DDP, three different depinning phases are discovered, which are the pinned, disordered plastic flow, and moving ordered states, respectively, as the driving force increases from zero. In addition, the continuous/discontinuous property of the phase transition between these different depinning states is investigated, showing that the transition property is modulated by the depth of the applied 1DPS.

Macroscopic simulations of dense plasmas rely on detailed microscopic information that can be computationally expensive and is difficult to verify experimentally. In this work, we delineate the accuracy boundary between microscale simulation methods by comparing Kohn-Sham density functional theory molecular dynamics (KS-MD) and radial pair potential molecular dynamics (RPP- MD) for a range of elements, temperature, and density. By extracting the optimal RPP from KS-MD data using force-matching, we constrain its functional form and dismiss classes of potentials that assume a constant power law for small interparticle distances. Our results show excellent agreement between RPP-MD and KS-MD for multiple metrics of accuracy at temperatures of only a few electron volts. The use of RPPs offers orders of magnitude decrease in computational cost and indicates that three-body potentials are not required beyond temperatures of a few eV. Due to its efficiency, the validated RPP-MD provides an avenue for reducing errors due to finite-size effects that can be on the order of $\sim20\%$.

The long-time diffusion of two-dimensional dusty plasmas on a one-dimensional periodic substrate with varied widths is investigated using Langevin dynamical simulations. When the substrate is narrow and the dust particles form a single row, the diffusion is the smallest in both directions. We find that as the substrate width gradually increases to twice its initial value, the long-time diffusion of the two-dimensional dusty plasmas first increases, then decreases, and finally increases again, giving an oscillation-like diffusion with varied substrate width. When the width increases to a specific value, the dust particles within each potential well arrange themselves in a stable zigzag pattern, greatly reducing the diffusion, and leading to the observed oscillation in the diffusion with the increasing width. In addition, the long-time oscillation-like diffusion is consistent with the number of dust particles that are hopping across the potential wells of the substrate., 7 pages, 5 figures