Generalized Gradient Approximation Made Thermal

Using the methodology of conditional-probability density functional theory, and several mild assumptions, we calculate the temperature-dependence of the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA). This numerically-defined thermal GGA reduces to the local approximation in the uniform limit and PBE at zero temperature, and can be fit reasonably accurately (within 8%) assuming the temperature-dependent enhancement is independent of the gradient. This locally thermal PBE satisfies both the coordinate-scaled correlation inequality and the concavity condition, which we prove for finite temperatures. The temperature dependence differs markedly from existing thermal GGA's.Comment: 6 pages, 5 figure

Effects of topological solitons on autocorrelation functions for chains of coupled torsional oscillators

©1983 American Institute of PhysicsThe electronic version of this article is the complete one and can be found online at: http://link.aip.org/link/?JCPSA6/78/6914/1DOI:10.1063/1.444640Brownian dynamics computer simulations were performed on chains of coupled torsional oscillators. The purpose was to observe the changes in autocorrelation functions, related to typical experimental measurements, caused by the introduction of topological solitons or kinks into the system. We considered three model systems: a chain of coupled torsional oscillators, a chain of coupled torsional oscillators with additional onefold rotational potentials acting on each oscillator, and a chain of coupled torsional oscillators with additional threefold rotational potentials. These models are of interest because of their application to torsional motions in polymeric systems, and, in particular, the system with onefold rotational potentials has been studied extensively as the sine–Gordon chain. We present simulation results for three autocorrelation functions of these three systems both with and without topological solitons

Correlation energy of the uniform gas determined by ground state conditional probability density functional theory

Conditional-probability density functional theory (CP-DFT) is a formally exact method for finding correlation energies from Kohn-Sham DFT without evaluating an explicit energy functional. We present details on how to generate accurate exchange-correlation energies for the ground-state uniform gas. We also use the exchange hole in a CP antiparallel spin calculation to extract the high-density limit. We give a highly accurate analytic solution to the Thomas-Fermi model for this problem, showing its performance relative to Kohn-Sham and may be useful at high temperatures. We explore several approximations to the CP potential. Results are compared to accurate parameterizations for both exchange-correlation energies and holes.Comment: Corrected typos, minor changes in text and notation for clarity and continuity with other work, results unchange

Topologically Induced Glass Transition in Freely Rotating Rods

We present a simple minimal model which allows numerical and analytical study of a glass transition. This is a model of rigid rods with fixed centers of rotation. The rods can rotate freely but cannot cross each other. The ratio $L$ of the length of the rods to the distance between the centers of rotation is the only parameter of this model. With increasing $L$ we observed a sharp crossover to practically infinite relaxation times in 2D arrays of rods. In 3D we found a real transition to a completely frozen random state at $L_{\rm c}\cong 4.5$

Modeling the Transport of Nanoparticle-Filled Binary Fluids through Micropores

Understanding the transport of multicomponent fluids through porous medium is of great importance for a number of technological applications, ranging from ink jet printing and the production of textiles to enhanced oil recovery. The process of capillary filling is relatively well understood for a single-component fluid; much less attention, however, has been devoted to investigating capillary filling processes that involve multiphase fluids, and especially nanoparticle-filled fluids. Here, we examine the behavior of binary fluids containing nanoparticles that are driven by capillary forces to fill well-defined pores or microchannels. To carry out these studies, we use a hybrid computational approach that combines the lattice Boltzmann model for binary fluids with a Brownian dynamics model for the nanoparticles. This hybrid approach allows us to capture the interactions among the fluids, nanoparticles, and pore walls. We show that the nanoparticles can dynamically alter the interfacial tension between the two fluids and the contact angle at the pore walls; this, in turn, strongly affects the dynamics of the capillary filling. We demonstrate that by tailoring the wetting properties of the nanoparticles, one can effectively control the filling velocities. Our findings provide fundamental insights into the dynamics of this complex multicomponent system, as well as potential guidelines for a number of technological processes that involve capillary filling with nanoparticles in porous media