Rubber friction on smooth surfaces

We study the sliding friction for viscoelastic solids, e.g., rubber, on hard flat substrate surfaces. We consider first the fluctuating shear stress inside a viscoelastic solid which results from the thermal motion of the atoms or molecules in the solid. At the nanoscale the thermal fluctuations are very strong and give rise to stress fluctuations in the MPa-range, which is similar to the depinning stresses which typically occur at solid-rubber interfaces, indicating the crucial importance of thermal fluctuations for rubber friction on smooth surfaces. We develop a detailed model which takes into account the influence of thermal fluctuations on the depinning of small contact patches (stress domains) at the rubber-substrate interface. The theory predicts that the velocity dependence of the macroscopic shear stress has a bell-shaped f orm, and that the low-velocity side exhibits the same temperature dependence as the bulk viscoelastic modulus, in qualitative agreement with experimental data. Finally, we discuss the influence of small-amplitude substrate roughness on rubber sliding friction.Comment: 14 pages, 16 figure

Modification of the Gay-Berne potential for improved accuracy and speed

A modification of the Gay-Berne potential is proposed which is about 10% to 20% more speed efficient (that is, the original potential runs 15% to 25% slower, depending on architecture) and statistically more accurate in reproducing the energy of interaction of two linear Lennard-Jones tetratomics when averaged over all orientations. For the special cases of end-to-end and side-by-side configurations, the new potential is equivalent to the Gay-Berne one.Comment: 5 pages (incl. title page), [preprint,aip,jcp]{RevTEX-4.1}, 1 figure, 1 table. Revised version fixes mathematical typos and adds short paragraph on a natural generalization to dissimilar particle

Note on the physical basis of spatially resolved thermodynamic functions

The spatial resolution of thermodynamic functions, exemplified by the entropy, is discussed. A physical definition of the spatial resolution based on a spatial analogy of the partial molar entropy is advocated. It is shown that neither the grid cell theory (Gerogiokas et al., J. Chem. Theory Comput., 10, 35 [2014]), nor the first-order grid inhomogeneous solvation theory (Nguyen et al. J. Chem. Phys., 137, 044101 [2012]), of spatially resolved hydration entropies satisfies the definition.Comment: Essentially 2 double-column pages, no figure

Comment on "Calculation of microcanonical entropy differences from configurational averages"

We introduce a simple improvement on the method to calculate equilibrium entropy differences between classical energy levels proposed by Davis (S. Davis, Phys. Rev. E, 050101, 2011). We demonstrate that the modification is superior to the original whenever the energy levels are sufficiently closely spaced or whenever the microcanonical averaging needed in the method is carried out by importance sampling Monte Carlo. We also point out the necessary adjustments if Davis's method (improved or not) is to be used with molecular dynamics simulations.Comment: 5 pages, 1 figure, completely rewritte

Computational predictions of energy materials using density functional theory

In the search for new functional materials, quantum mechanics is an exciting starting point. The fundamental laws that govern the behaviour of electrons have the possibility, at the other end of the scale, to predict the performance of a material for a targeted application. In some cases, this is achievable using density functional theory (DFT). In this Review, we highlight DFT studies predicting energy-related materials that were subsequently confirmed experimentally. The attributes and limitations of DFT for the computational design of materials for lithium-ion batteries, hydrogen production and storage materials, superconductors, photovoltaics and thermoelectric materials are discussed. In the future, we expect that the accuracy of DFT-based methods will continue to improve and that growth in computing power will enable millions of materials to be virtually screened for specific applications. Thus, these examples represent a first glimpse of what may become a routine and integral step in materials discovery