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

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

We report the measurement of the thermal conductivity of a suspended single-layer graphene. The room temperature values of the thermal conductivity in the range ∼(4.84 ± 0.44) × 103 to (5.30 ± 0.48) × 103 W/mK were extracted for a single-layer graphene from the dependence of the Raman G peak frequency on the excitation laser power and independently measured G peak temperature coefficient. The extremely high value of the thermal conductivity suggests that graphene can outperform carbon nanotubes in heat conduction. The superb thermal conduction property of graphene is beneficial for the proposed electronic applications and establishes graphene as an excellent material for thermal management.

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Superior Thermal Conductivity of Single-Layer Graphene

There is intense interest in graphene in fields such as physics, chemistry, and materials science, among others. Interest in graphene's exceptional physical properties, chemical tunability, and potential for applications has generated thousands of publications and an accelerating pace of research, making review of such research timely. Here is an overview of the synthesis, properties, and applications of graphene and related materials (primarily, graphite oxide and its colloidal suspensions and materials made from them), from a materials science perspective.

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Graphene and Graphene Oxide: Synthesis, Properties, and Applications

Recent years have seen a rapid growth of interest by the scientific and engineering communities in the thermal properties of materials. Heat removal has become a crucial issue for continuing progress in the electronic industry, and thermal conduction in low-dimensional structures has revealed truly intriguing features. Carbon allotropes and their derivatives occupy a unique place in terms of their ability to conduct heat. The room-temperature thermal conductivity of carbon materials span an extraordinary large range--of over five orders of magnitude--from the lowest in amorphous carbons to the highest in graphene and carbon nanotubes. Here, I review the thermal properties of carbon materials focusing on recent results for graphene, carbon nanotubes and nanostructured carbon materials with different degrees of disorder. Special attention is given to the unusual size dependence of heat conduction in two-dimensional crystals and, specifically, in graphene. I also describe the prospects of applications of graphene and carbon materials for thermal management of electronics.

Thermal properties of graphene and nanostructured carbon materials

Enhancing electron correlation in a weakly interacting topological system has great potential to promote correlated topological states of matter with extraordinary quantum properties. Here, the enhancement of electron correlation in a prototypical topological metal, namely iridium dioxide (IrO2), via doping with 3d transition metal vanadium is demonstrated. Single‐crystalline vanadium‐doped IrO2 nanowires are synthesized through chemical vapor deposition where the nanowire yield and morphology are improved by creating rough surfaces on substrates. Vanadium doping leads to a dramatic decrease in Raman intensity without notable peak broadening, signifying the enhancement of electron correlation. The enhanced electron correlation is further evidenced by transport studies where the electrical resistivity is greatly increased and follows an unusual T$\sqrt T $ dependence on the temperature (T). The lattice thermal conductivity is suppressed by an order of magnitude via doping even at room temperature where phonon‐impurity scattering becomes less important. Density functional theory calculations suggest that the remarkable reduction of thermal conductivity arises from the complex phonon dispersion and reduced energy gap between phonon branches, which greatly enhances phase space for phonon–phonon Umklapp scattering. This work demonstrates a unique system combining 3d and 5d transition metals in isostructural materials to enrich the system with various types of interactions.

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Enhanced Electron Correlation and Significantly Suppressed Thermal Conductivity in Dirac Nodal‐Line Metal Nanowires by Chemical Doping

Thermal Transport: Thermal Properties of Two Dimensional Layered Materials (Adv. Funct. Mater. 19/2017)

Graphene-integrated Microfluidic Platforms for Electrical Probing in Retina

The nonequilibrium molecular dynamics (NEMD) method has been used to calculate the lattice thermal conductivities of Ar and Kr/Ar nanostructures in order to study the effects of interface scattering, boundary scattering, and elastic strain on lattice thermal conductivity. Results show that interface scattering poses significant resistance to phonon transport in superlattices and superlattice nanowires. The thermal conductivity of the Kr/Ar superlattice nanowire is only about 1/3 of that for pure Ar nanowires with the same cross sectional area and total length due to the additional interfacial thermal resistance. It is found that nanowire boundary scattering provides significant resistance to phonon transport. As the cross sectional area increases, the nanowire boundary scattering decreases, which leads to increased nanowire thermal conductivity. The ratio of the interfacial thermal resistance to the total effective thermal resistance increases from 30% for the superlattice nanowire to 42% for the superlattice film. Period length is another important factor affecting the effective thermal conductivity of the nanostructures. Increasing the period length will lead to increased acoustic mismatch between the adjacent layers, and hence increased interfacial thermal resistance. However, if the total length of the superlattice nanowire is fixed, reducing the period length will lead to decreased effective thermal conductivity due to the increased number of interfaces. Finally, it is found that the interfacial thermal resistance decreases as the reference temperature increases, which might be due to the inelastic interface scattering.Copyright © 2004 by ASME

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Interface Effect on Lattice Thermal Conductivities of Superlattice Nanowires

As a well-known multi-label classification method, the performance of ML-KNN may be affected by the uncertainty knowledge from samples. The rough set theory acts as an effective tool for data uncertainty analysis, which can identify the samples easy to cause misclassification in the learning process. In this paper, a hybrid framework by fusing rough sets with ML-KNN for multi-label learning is proposed, whose main idea is to depict easy misclassified samples by rough sets and to measure the discernibility of attributes for such samples. First, a rough set model titled NRFD_RS based on neighborhood relations and fuzzy decisions is proposed for multi-label data to find the heterogeneous sample pairs generated from the boundary regions of each label. Then, the weight of an attribute is defined by evaluating its discernibility to those heterogeneous sample pairs. Finally, a weighted HEOM distance is reconstructed and utilized to ML-KNN. Comprehensive experimental results with fourteen public multi-label data sets, including ten regular-scale and four larger-scale data sets, verify the effectiveness of the proposed framework relative to several state-of-the-art multi-label classification methods. 

Deyu Li

Papers

Enhanced Electron Correlation and Significantly Suppressed Thermal Conductivity in Dirac Nodal‐Line Metal Nanowires by Chemical Doping

Thermal Transport: Thermal Properties of Two Dimensional Layered Materials (Adv. Funct. Mater. 19/2017)

Graphene-integrated Microfluidic Platforms for Electrical Probing in Retina

Interface Effect on Lattice Thermal Conductivities of Superlattice Nanowires

A weighted ML-KNN based on discernibility of attributes to heterogeneous sample pairs