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 organization of behavior: A neuropsychological theory

The blackbody radiation left over from the Big Bang has been transformed by the expansion of the Universe into the nearly isotropic 2.73 K cosmic microwave background. Tiny inhomogeneities in the early Universe left their imprint on the microwave background in the form of small anisotropies in its temperature. These anisotropies contain information about basic cosmological parameters, particularly the total energy density and curvature of the Universe. Here we report the first images of resolved structure in the microwave background anisotropies over a significant part of the sky. Maps at four frequencies clearly distinguish the microwave background from foreground emission. We compute the angular power spectrum of the microwave background, and find a peak at Legendre multipole l_(peak) = (197 ± 6), with an amplitude ΔT_(200) = (69 ± 8) µK. This is consistent with that expected for cold dark matter models in a flat (euclidean) Universe, as favoured by standard inflationary models.

/pdf/a-flat-universe-from-high-resolution-maps-of-the-cosmic-2iwpcg70nt.pdf

A flat Universe from high-resolution maps of the cosmic microwave background radiation

This is a review of the physics and cosmology of the cosmological constant. Focusing on recent developments, I present a pedagogical overview of cosmology in the presence of a cosmological constant, observational constraints on its magnitude, and the physics of a small (and potentially nonzero) vacuum energy.

/pdf/the-cosmological-constant-4ljm1nl1cq.pdf

The Cosmological Constant

A review of some properties of chalcogenide glasses and the current status of their applications is given. Techniques to characterize the linear and non-linear properties of these glasses are introduced and used to measure the optical constants of chalcogenide glasses in the form of bulk, thin film and fiber. Different techniques for the fabrication of gratings and waveguides in these glasses are described. Achievable efficiencies of gratings, as well as propagation losses of fabricated waveguides, are presented. The possibilities of fabricating active devices, such as fiber amplifiers and lasers, are presented. Finally, a novel application of chalcogenide glasses, namely all-optical switching for the fabrication of efficient femtosecond switches, is introduced.

Optical properties and applications of chalcogenide glasses: a review

Phase-change random-access memory (PCRAM) is one of the leading candidates for next-generation data-storage devices, but the trade-off between crystallization (writing) speed and amorphous-phase stability (data retention) presents a key challenge. We control the crystallization kinetics of a phase-change material by applying a constant low voltage via prestructural ordering (incubation) effects. A crystallization speed of 500 picoseconds was achieved, as well as high-speed reversible switching using 500-picosecond pulses. Ab initio molecular dynamics simulations reveal the phase-change kinetics in PCRAM devices and the structural origin of the incubation-assisted increase in crystallization speed. This paves the way for achieving a broadly applicable memory device, capable of nonvolatile operations beyond gigahertz data-transfer rates.

https://science.sciencemag.org/content/sci/336/6088/1566.full.pdf

Breaking the Speed Limits of Phase-Change Memory

Despite their lack of long-range translational and orientational order, covalent amorphous solids can exhibit structural order over both short and medium length scales, the latter reaching to 20 A or so. Medium-range order is difficult to measure experimentally and to interpret unambiguously, but a variety of techniques have allowed several types of characteristic structural ordering to be identified and their origin elucidated.

Medium-range structural order in covalent amorphous solids

Amorphous semiconductors, being intrinsically metastable in nature, exhibit a wide variety of changes in their physical properties, particularly when photoinduced using bandgap illumination. This article reviews the photoinduced phenomena exhibited by amorphous semiconductors such as amorphous hydrogenated silicon (and other tetrahedrally coordinated materials) and chalcogenide glasses. Features exhibited in common by all types of amorphous semiconductors, whether in the experimentally observed photoinduced metastability or the theoretical models used to account for such behaviour, are stressed.

Photoinduced effects and metastability in amorphous semiconductors and insulators

Ge-Sb-Te materials are used in optical DVDs and non-volatile electronic memories (phase-change random-access memory). In both, data storage is effected by fast, reversible phase changes between crystalline and amorphous states. Despite much experimental and theoretical effort to understand the phase-change mechanism, the detailed atomistic changes involved are still unknown. Here, we describe for the first time how the entire write/erase cycle for the Ge(2)Sb(2)Te(5) composition can be reproduced using ab initio molecular-dynamics simulations. Deep insight is gained into the phase-change process; very high densities of connected square rings, characteristic of the metastable rocksalt structure, form during melt cooling and are also quenched into the amorphous phase. Their presence strongly facilitates the homogeneous crystal nucleation of Ge(2)Sb(2)Te(5). As this simulation procedure is general, the microscopic insight provided on crystal nucleation should open up new ways to develop superior phase-change memory materials, for example, faster nucleation, different compositions, doping levels and so on.

Stephen R. Elliott

Papers

Optical properties and applications of chalcogenide glasses: a review

Breaking the Speed Limits of Phase-Change Memory

Medium-range structural order in covalent amorphous solids

Photoinduced effects and metastability in amorphous semiconductors and insulators

Microscopic origin of the fast crystallization ability of Ge–Sb–Te phase-change memory materials