There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The term photonic crystals appears because of the analogy between electron waves in crystals and the light waves in artificial periodic dielectric structures. During the recent years the investigation of one-, two-and three-dimensional periodic structures has attracted a widespread attention of the world optics community because of great potentiality of such structures in advanced applied optical fields. The interest in periodic structures has been stimulated by the fast development of semiconductor technology that now allows the fabrication of artificial structures, whose period is comparable with the wavelength of light in the visible and infrared ranges.

http://ieeexplore.ieee.org/iel5/6354/16969/00781867.pdf

Photonic crystals

The science and technology of ultracapacitors are reviewed for a number of electrode materials, including carbon, mixed metal oxides, and conducting polymers. More work has been done using microporous carbons than with the other materials and most of the commercially available devices use carbon electrodes and an organic electrolytes. The energy density of these devices is 3¯5 Wh/kg with a power density of 300¯500 W/kg for high efficiency (90¯95%) charge/discharges. Projections of future developments using carbon indicate that energy densities of 10 Wh/kg or higher are likely with power densities of 1¯2 kW/kg. A key problem in the fabrication of these advanced devices is the bonding of the thin electrodes to a current collector such the contact resistance is less than 0.1 cm2. Special attention is given in the paper to comparing the power density characteristics of ultracapacitors and batteries. The comparisons should be made at the same charge/discharge efficiency.

Ultracapacitors: Why, How, and Where is the Technology

A subwavelength seismic metamaterial (SM) consisting of a three-component SM plate (SMP) and a half space is proposed to attenuate ultra-low frequency seismic surface waves. The design concept and models are verified first by lab-scale experiments on the SM consisting of a two-component SMP and a half space. Then, we calculate the band structures of one-dimensional and two-dimensional subwavelength SMs and evaluate their ability to attenuate Rayleigh waves. A wide ultra-low frequency bandgap can be found, and the Rayleigh waves are deflected by the subwavelength SM and converted into bulk waves in the frequency range of this bandgap. When the number of unit cells of the subwavelength SM is sufficient, the transmission distance and deflection angle of the Rayleigh waves are constant at the same frequency. This discovery is expected to open up the possibility of pragmatic seismic protection for large nuclear power plants, ancient buildings, and metropolitan areas.

/pdf/subwavelength-seismic-metamaterial-with-an-ultra-low-ndbvxbd6xf.pdf

Subwavelength seismic metamaterial with an ultra-low frequency bandgap

https://iopscience.iop.org/article/10.1088/1361-6463/ab7327/pdf

An arbitrarily curved acoustic metasurface for three-dimensional reflected wave-front modulation

Propagation of elastic waves along a direction has special interests in practical applications. These concerns generate the design of an elastic wave metamaterial with electrically switchable properties, which is studied in this work. The structure contains a T-shaped waveguide in a plate with the 3D printing technology; and the active control system is used to tune the propagation direction of the flexural wave. The piezoelectric patches which are connected by the negative capacitance circuits are applied to behave as the active control system. The finite element simulation is performed to give the theoretical prediction of the switchable waveguide and the tunable equivalent parameters are achieved by the electrical circuits. The active control experiments are finally carried out to support the numerical design.

/pdf/active-control-on-switchable-waveguide-of-elastic-wave-3dpvfuf6v3.pdf

Active control on switchable waveguide of elastic wave metamaterials with the 3D printing technology.

The propagation of the elastic waves in one-dimensional (1D) solid–fluid quasi-periodic phononic crystals is studied by employing the concept of the localization factor, which is calculated by the transfer matrix method. The solid–fluid interaction effect at the interfaces between the solid and the fluid components is considered. For comparison, the periodic systems and aperiodic Thue–Morse sequence are also analyzed in this paper. The splitting phenomenon of the pass bands and bandgaps are discussed for these 1D solid–fluid systems. At last the influences of the material impedance ratios on the band structures of the 1D solid–fluid quasi-periodic phononic crystals arranged as Fibonacci sequence are discussed.

Wave propagation in one-dimensional solid–fluid quasi-periodic and aperiodic phononic crystals

The phoxonic crystal sensor is theoretically investigated in this paper. Based on the phoxonic crystal with dual surface mode bandgaps, both optical and acoustic waves can be confined near the surface. Thus dual cavities, one optical cavity and one acoustic cavity, are designed and integrated in the same phoxonic device. The sensor aims at determining the refractive index and sound velocity of different liquids by utilizing surface optical and acoustic waves. The sensitivities can be estimated by the variations of the cavity resonant frequencies in the response spectra. The relation between the frequency and the measuring physical parameters, i.e., the refractive index or the sound velocity, can be approximated by a linear function. By optimizing each cavity the photonic and phononic sensitivities can reach 199 nm/RIU and 2.79 MHz/ms −1 , respectively. This work can serve as a reference for the design of novel phoxonic sensors.

Yue-Sheng Wang

Papers

Subwavelength seismic metamaterial with an ultra-low frequency bandgap

An arbitrarily curved acoustic metasurface for three-dimensional reflected wave-front modulation

Active control on switchable waveguide of elastic wave metamaterials with the 3D printing technology.

Wave propagation in one-dimensional solid–fluid quasi-periodic and aperiodic phononic crystals

Theoretical research on a two-dimensional phoxonic crystal liquid sensor by utilizing surface optical and acoustic waves