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

The lithium metal battery is strongly considered to be one of the most promising candidates for high-energy-density energy storage devices in our modern and technology-based society. However, uncontrollable lithium dendrite growth induces poor cycling efficiency and severe safety concerns, dragging lithium metal batteries out of practical applications. This review presents a comprehensive overview of the lithium metal anode and its dendritic lithium growth. First, the working principles and technical challenges of a lithium metal anode are underscored. Specific attention is paid to the mechanistic understandings and quantitative models for solid electrolyte interphase (SEI) formation, lithium dendrite nucleation, and growth. On the basis of previous theoretical understanding and analysis, recently proposed strategies to suppress dendrite growth of lithium metal anode and some other metal anodes are reviewed. A section dedicated to the potential of full-cell lithium metal batteries for practical applicatio...

Toward Safe Lithium Metal Anode in Rechargeable Batteries: A Review.

This review covers important advances in recent years in the physics of thin-film ferroelectric oxides, the strongest emphasis being on those aspects particular to ferroelectrics in thin-film form. The authors introduce the current state of development in the application of ferroelectric thin films for electronic devices and discuss the physics relevant for the performance and failure of these devices. Following this the review covers the enormous progress that has been made in the first-principles computational approach to understanding ferroelectrics. The authors then discuss in detail the important role that strain plays in determining the properties of epitaxial thin ferroelectric films. Finally, this review ends with a look at the emerging possibilities for nanoscale ferroelectrics, with particular emphasis on ferroelectrics in nonconventional nanoscale geometries.

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Physics of thin-film ferroelectric oxides

Thermoelectrics have long been recognized as a potentially transformative energy conversion technology due to their ability to convert heat directly into electricity. Despite this potential, thermoelectric devices are not in common use because of their low efficiency, and today they are only used in niche markets where reliability and simplicity are more important than performance. However, the ability to create nanostructured thermoelectric materials has led to remarkable progress in enhancing thermoelectric properties, making it plausible that thermoelectrics could start being used in new settings in the near future. Of the various types of nanostructured materials, bulk nanostructured materials have shown the most promise for commercial use because, unlike many other nanostructured materials, they can be fabricated in large quantities and in a form that is compatible with existing thermoelectric device configurations. The first generation of these materials is currently being developed for commercialization, but creating the second generation will require a fundamental understanding of carrier transport in these complex materials which is presently lacking. In this review we introduce the principles and present status of bulk nanostructured materials, then describe some of the unanswered questions about carrier transport and how current research is addressing these questions. Finally, we discuss several research directions which could lead to the next generation of bulk nanostructured materials.

Bulk nanostructured thermoelectric materials: current research and future prospects

The conversion efficiency of heat to electricity in thermoelectric materials depends on both their thermopower and electrical conductivity. It is now reported that, unlike their inorganic counterparts, organic thermoelectric materials show an improvement in both these parameters when the volume of dopant elements is minimized; furthermore, a high conversion efficiency is achieved in PEDOT:PSS blends.

Engineered doping of organic semiconductors for enhanced thermoelectric efficiency

Relation extraction is a crucial step in the constructions of knowledge graphs (KGs). However, relation extraction is performed manually in the manufacturing field due to the sentence characteristics, which include weak correlation and high entity density. This approach has the disadvantages of low efficiency and high dependence on experts. At present, very few studies have been performed on relation extraction in the manufacturing field, so establishing a relation extraction model with high efficiency is an urgent need. Therefore, in this paper, a relation extraction model is proposed for manufacturing knowledge (MKREM), in which word embedding is obtained by the Bi-LSTM layer to improve robustness, and a Simplified Graph Convolution Network (SGC) layer is applied to quickly mine the entity information. Then, dependency and semantic features are extracted by the multi-head stacked GCN and relation attention mechanism, respectively. Finally, the dependency and semantic features are fused to generate the comprehensive features for relation extraction so that better performance on texts with weak correlation and high entity density can be obtained. The performance of MKREM is tested by experiments on the equipment maintenance dataset and the quality dataset from an automobile enterprise, and its effectiveness is verified in the automobile manufacture filed. The results show that the F1 scores obtained using MKREM are 2% higher than those of the commonly used models on both datasets, and the F1 scores when using Contextualized-MKREM are improved by 3%, so MKREM is very suitable for the automatic relation extraction during establishing manufacturing KGs. 

Relation extraction for manufacturing knowledge graphs based on feature fusion of attention mechanism and graph convolution network

A multi-objective service composition method considering the interests of tri-stakeholders in cloud manufacturing based on an enhanced jellyfish search optimizer

Conventional techniques for characterization of thermoelectric performance require bringing measurement equipment into direct contact with the thermoelectric device, which is increasingly error prone as device size decreases. Therefore, the novel work presented here describes a non-contact technique, capable of accurately measuring the maximum ΔT and maximum heat pumping of mini to micro sized thin film thermoelectric coolers. The non-contact characterization method eliminates the measurement errors associated with using thermocouples and traditional heat flux sensors to test small samples and large heat fluxes. Using the non-contact approach, an infrared camera, rather than thermocouples, measures the temperature of the hot and cold sides of the device to determine the device ΔT and a laser is used to heat to the cold side of the thermoelectric module to characterize its heat pumping capacity. As a demonstration of the general applicability of the non-contact characterization technique, testing of a thin film thermoelectric module is presented and the results agree well with those published in the literature.

Non-contact method for characterization of small size thermoelectric modules

Polyalphaolefins (PAOs) are widely implemented for electronics cooling. However, they have a low thermal conductivity. Adding nanoparticles to a PAO can significantly improve the thermal properties of the coolant. In this paper, the thermal performance of phase change materials (PCM) fluids with reduced supercooling in the phase transition was studied in a microchannel heat sink, and compared with that of the pure PAO. A test loop was designed and fabricated to test the synthesized PCM fluids. The PCM fluid with uncoated PCM particles has a 36% higher heat transfer coefficient than the pure PAO. The heat transfer coefficient of a silver coated PCM coated was also higher than of the pure PAO but lower than of the uncoated PCM. The thermal resistance of the uncoated PCM fluid was about 20% lower that of the PAO fluid at the same pumping power, despite the PCM fluid’s higher viscosity. Pumping tests run for several hours showed no effect on particle accumulation or settling within the heat transfer loop.Copyright © 2012 by ASME

Heat Transfer Performance of a Phase Change Microcapsule Fluid

Previous studies on nanofluids have focused on spherical or long-fiber particles. In this work, a new type of complex nanoparticles—hybrid sphere/carbon nanotube (CNT) particle, consisting of numerous CNTs attached to an alumina/iron oxide sphere—is proposed for applications in nanofluids. In such hybrid nanoparticles, heat is expected to transport rapidly from one CNT to another through the center sphere and thus leading to less thermal-contact-resistance between CNTs when compared to simple CNTs dispersed in fluids. CNTs have an extremely high thermal conductivity, but thermal resistance between the CNTs and the fluid has limited their performance in the nanofluids. The proposed hybrid sphere/CNT particles are synthesized by a spray pyrolysis followed by catalytic growth of CNTs. The spheres are about 70 nm in diameter in average, and the attached CNTs have a length up to 2μm. These hybrid nanoparticles are dispersed to poly-alpha-olefin with sonication and a small amount of surfactants to form stable nanofluids. The thermal conductivity of the fluids has been measured by a 3ω-wire method over a temperature range 10–90°C. The results indicate that the effective thermal conductivity of the fluids is increased by about 21% at room temperature for particle volume fractions of 0.2%.Copyright © 2007 by ASME

Bao Yang

Papers

Relation extraction for manufacturing knowledge graphs based on feature fusion of attention mechanism and graph convolution network

A multi-objective service composition method considering the interests of tri-stakeholders in cloud manufacturing based on an enhanced jellyfish search optimizer

Non-contact method for characterization of small size thermoelectric modules

Heat Transfer Performance of a Phase Change Microcapsule Fluid

Nanofluids Containing Hybrid Sphere/Carbon Nanotube Particles