Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Although gold is the subject of one of the most ancient themes of investigation in science, its renaissance now leads to an exponentially increasing number of publications, especially in the context of emerging nanoscience and nanotechnology with nanoparticles and self-assembled monolayers (SAMs). We will limit the present review to gold nanoparticles (AuNPs), also called gold colloids. AuNPs are the most stable metal nanoparticles, and they present fascinating aspects such as their assembly of multiple types involving materials science, the behavior of the individual particles, size-related electronic, magnetic and optical properties (quantum size effect), and their applications to catalysis and biology. Their promises are in these fields as well as in the bottom-up approach of nanotechnology, and they will be key materials and building block in the 21st century. Whereas the extraction of gold started in the 5th millennium B.C. near Varna (Bulgaria) and reached 10 tons per year in Egypt around 1200-1300 B.C. when the marvelous statue of Touthankamon was constructed, it is probable that “soluble” gold appeared around the 5th or 4th century B.C. in Egypt and China. In antiquity, materials were used in an ecological sense for both aesthetic and curative purposes. Colloidal gold was used to make ruby glass 293 Chem. Rev. 2004, 104, 293−346

Gold nanoparticles: assembly, supramolecular chemistry, quantum-size-related properties, and applications toward biology, catalysis, and nanotechnology.

The optical properties of metal nanoparticles have long been of interest in physical chemistry, starting with Faraday's investigations of colloidal gold in the middle 1800s. More recently, new lithographic techniques as well as improvements to classical wet chemistry methods have made it possible to synthesize noble metal nanoparticles with a wide range of sizes, shapes, and dielectric environments. In this feature article, we describe recent progress in the theory of nanoparticle optical properties, particularly methods for solving Maxwell's equations for light scattering from particles of arbitrary shape in a complex environment. Included is a description of the qualitative features of dipole and quadrupole plasmon resonances for spherical particles; a discussion of analytical and numerical methods for calculating extinction and scattering cross-sections, local fields, and other optical properties for nonspherical particles; and a survey of applications to problems of recent interest involving triangula...

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The Optical Properties of Metal Nanoparticles: The Influence of Size, Shape, and Dielectric Environment

The surface science of titanium dioxide

The richness of optical and electronic properties of graphene attracts enormous interest. Graphene has high mobility and optical transparency, in addition to flexibility, robustness and environmental stability. So far, the main focus has been on fundamental physics and electronic devices. However, we believe its true potential lies in photonics and optoelectronics, where the combination of its unique optical and electronic properties can be fully exploited, even in the absence of a bandgap, and the linear dispersion of the Dirac electrons enables ultrawideband tunability. The rise of graphene in photonics and optoelectronics is shown by several recent results, ranging from solar cells and light-emitting devices to touch screens, photodetectors and ultrafast lasers. Here we review the state-of-the-art in this emerging field.

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Graphene photonics and optoelectronics

Electrical poling induces polar ordering of molecules in a grating that has been holographically inscribed on a thin film of polymer functionalized with azobenzene side chains. Depending on the surface relief amplitude, the resulting χ (2) grating, seen by second harmonic generation (SHG) near-field scanning optical microscopy (NSOM), can have a periodic structure significantly different from the topographical image. The far-field linear and SHG diffraction patterns correlate well with the grating structures. Poling of the thin-film grating, which presumably has photo-driven non-uniform material properties within each period, leads to the more complex structure of the χ (2) grating.

Poled polymer thin film gratings studied by near-field second harmonic optical microscopy and far-field optical diffraction

Sum-frequency vibrational spectroscopic studies of water interfaces

The bifunctional 3D-on-2D FeCo/Ni(OH)2 hierarchical nanocatalyst for industrial-level electrocatalytic water splitting

The observation of a number of surface-enhanced nonlinear optical effects is discussed. The feasibility of using second-harmonic generation to detect the adsorption of molecular monolayers on a metal surface in an electrolytic solution is shown.

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Surface-enhanced nonlinear optical effects and detection of adsorbed molecular monolayers

Adaptive graph-based representation and learning methods have received extensive attention due to their good performance in supervised and unsupervised learning tasks. These methods often involve probability constraint, i.e., the sum-to-one constraint, when learning a similarity graph. Whereas this constraint may degrade the ability to precisely measure the similarity between samples. In terms of clustering tasks, limited by the sum-to-one constraint, the similarity between some noisy points may tend to be large, which causes these abnormal points to be grouped into a separate cluster, and other close clusters with normal points are merged. To address this problem, this study proposes a novel notion of possibilistic neighbor graph (PNG). In PNG, the possibility of two samples being neighbors can be measured adaptively and precisely by removing the sum-to-one constraint and incorporating a new regularization term. By using the possibility rather than the probability in the similarity graph learning, the solution space is enlarged without increasing the complexity. The mathematical properties of PNG are discussed in detail, and a new graph-based clustering method (CPNG) is also developed based on the learned PNG. Extensive experimental results on several benchmark datasets demonstrate the superiority of CPNG in comparison with some state-of-the-art methods. 

Y. R. Shen

Papers

Poled polymer thin film gratings studied by near-field second harmonic optical microscopy and far-field optical diffraction

Sum-frequency vibrational spectroscopic studies of water interfaces

The bifunctional 3D-on-2D FeCo/Ni(OH)2 hierarchical nanocatalyst for industrial-level electrocatalytic water splitting

Surface-enhanced nonlinear optical effects and detection of adsorbed molecular monolayers

Possibilistic Neighborhood Graph: A New Concept of Similarity Graph Learning