The thermal conductivities of common water models are compared using equilibrium (EMD) and non-equilibrium molecular dynamics (NEMD) simulation. A complete accounting for electrostatic contributions to the heat flux was found to resolve the previously reported differing results of NEMD and EMD Green-Kubo measurements for the extended simple point-charge (SPC/E) model. Accordingly, we demonstrate the influence of long-range electrostatics on the thermal conductivity with a simple coulomb cutoff, Ewald summation, and by an extended particle-particle particle-mesh method. For each water model, the thermal conductivity is computed and decomposed in terms of frequency-dependent thermodynamic and topological contributions. The rigid, three-site SPC, SPC/E, and transferable intermolecular potential (TIP3P-Ew) water models are shown to have similar thermal conductivity values at standard conditions, whereas models that include bond stretching and angle bending have higher thermal conductivities.
Kirkwood-Buff (KB) solution theory is a means to obtain certain thermodynamic derivatives from knowledge of molecular distributions. In actual practice the required integrals over radial distribution functions suffer inaccuracies due to finite-distance truncation effects and their use in closed systems. In this work we discuss how best to minimize these inaccuracies under traditional KB theory. In addition we implement a method for calculating KB quantities in molecular simulations with periodic boundary conditions and particularly within the canonical ensemble. The method is based on a finite-Fourier-series expansion of molecular concentration fluctuations and leads to more reliable results for a given computational effort. The procedure is validated and compared to the original method for a nonideal liquid mixture of Lennard-Jones particles intended to imitate a real system, carbon tetrafluoride, and methane.
Reactive force fields provide an affordable model for simulating chemical reactions at a fraction of the cost of quantum mechanical approaches. However classically accounting for chemical reactivity often comes at the expense of accuracy and transferability, while computational cost is still large relative to non-reactive force fields. In this Perspective we summarize recent efforts for improving the performance of reactive force fields in these three areas with a focus on the ReaxFF theoretical model. To improve accuracy we describe recent reformulations of charge equilibration schemes to overcome unphysical long-range charge transfer, new ReaxFF models that account for explicit electrons, and corrections for energy conservation issues of the ReaxFF model. To enhance transferability we also highlight new advances to include explicit treatment of electrons in the ReaxFF and hybrid non-reactive/reactive simulations that make it possible to model charge transfer, redox chemistry, and large systems such as reverse micelles within the framework of a reactive force field. To address the computational cost we review recent work in extended Lagrangian schemes and matrix preconditioners for accelerating the charge equilibration method component of ReaxFF and improvements in its software performance in LAMMPS.
The gold-standard definition of the Direct Simulation Monte Carlo (DSMC) method is given in the 1994 book by Bird [Molecular Gas Dynamics and the Direct Simulation of Gas Flows (Clarendon Press, Oxford, UK, 1994)], which refined his pioneering earlier papers in which he first formulated the method. In the intervening 25 years, DSMC has become the method of choice for modeling rarefied gas dynamics in a variety of scenarios. The chief barrier to applying DSMC to more dense or even continuum flows is its computational expense compared to continuum computational fluid dynamics methods. The dramatic (nearly billion-fold) increase in speed of the largest supercomputers over the last 30 years has thus been a key enabling factor in using DSMC to model a richer variety of flows, due to the method’s inherent parallelism. We have developed the open-source SPARTA DSMC code with the goal of running DSMC efficiently on the largest machines, both current and future. It is largely an implementation of Bird’s 1994 formulation. Here, we describe algorithms used in SPARTA to enable DSMC to operate in parallel at the scale of many billions of particles or grid cells, or with billions of surface elements. We give a few examples of the kinds of fundamental physics questions and engineering applications that DSMC can address at these scales.
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