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

Bose-Einstein condensation (BEC) has been observed in a dilute gas of sodium atoms. A Bose-Einstein condensate consists of a macroscopic population of the ground state of the system, and is a coherent state of matter. In an ideal gas, this phase transition is purely quantum-statistical. The study of BEC in weakly interacting systems which can be controlled and observed with precision holds the promise of revealing new macroscopic quantum phenomena that can be understood from first principles.

Bose-Einstein condensation in a gas of sodium atoms

The heterogeneous multiscale method (HMM), a general framework for designing multiscale algorithms, is reviewed. Emphasis is given to the error analysis that comes naturally with the framework. Examples of finite element and finite difference HMM are presented. Applications to dynamical systems and stochastic simulation algorithms with multiple time scales, spall fracture and heat conduction in microprocessors are discussed.

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The heterogeneous multiscale method

This review is about understanding and controlling organic molecular adsorption on silicon. The goal is to provide a microscopic picture of structure and bonding in covalently attached molecule-silicon surface systems. The bias here is that an unprecedented, detailed understanding of adsorbate-surface structures is required in order to gain the control necessary to incorporate organic function into existing technologies or, eventually, to make new molecule-scale devices. A discussion of recent studies of adsorbate structure is presented. This includes simple alkenes, polyenes, benzene, and carene adsorbed on Si(100). Also included is a discussion of wet chemical procedures for forming alkyl and alkoxy covalently functionalized silicon. These discussions are presented together with comments on the related issues of adsorption dynamics and nano-scale manipulation in an effort to point the way toward principles and procedures that will allow the hybrid properties of organic molecules and surfaces to be harnessed.

Controlled molecular adsorption on silicon: laying a foundation for molecular devices.

The unique plasma-specific features and physical phenomena in the organization of nanoscale soild-state systems in a broad range of elemental composition, structure, and dimensionality are critically reviewed. These effects lead to the possibility to localize and control energy and matter at nanoscales and to produce self-organized nano-solids with highly unusual and superior properties. A unifying conceptual framework based on the control of production, transport, and self-organization of precursor species is introduced and a variety of plasma-specific non-equilibrium and kinetics-driven phenomena across the many temporal and spatial scales is explained. When the plasma is localized to micrometer and nanometer dimensions, new emergent phenomena arise. The examples range from semiconducting quantum dots and nanowires, chirality control of single-walled carbon nanotubes, ultra-fine manipulation of graphenes, nano-diamond, and organic matter to nano-plasma effects and nano-plasmas of different states of matter.

Plasma nanoscience: from nano-solids in plasmas to nano-plasmas in solids

Hydrogenated amorphous and nanocrystalline silicon films manufactured by plasma deposition techniques are used widely in electronic and optoelectronic devices1,2. The crystalline fraction and grain size of these films determines electronic and optical properties; the nanocrystal nucleation mechanism, which dictates the final film structure, is governed by the interactions between the hydrogen atoms of the plasma and the solid silicon matrix. Fundamental understanding of these interactions is important for optimizing the film structure and properties. Here we report the mechanism of hydrogen-induced crystallization of hydrogenated amorphous silicon films during post-deposition treatment with an H2 (or D2) plasma. Using molecular-dynamics simulations3,4 and infrared spectroscopy5, we show that crystallization is mediated by the insertion of H atoms into strained Si–Si bonds as the atoms diffuse through the film. This chemically driven mechanism may be operative in other covalently bonded materials, where the presence of hydrogen leads to disorder-to-order transitions.

Mechanism of hydrogen-induced crystallization of amorphous silicon

Dislocation loop structure, energy and mobility of self-interstitial atom clusters in bcc iron

http://li.mit.edu/Stuff/CNSE/Paper/Wirth97OdetteJNM.pdf

Energetics of formation and migration of self-interstitials and self-interstitial clusters in α-iron

We implement a computer-assisted approach that, under appropriate conditions, allows the bifurcation analysis of the “coarse” dynamic behavior of microscopic simulators without requiring the explicit derivation of closed macroscopic equations for this behavior. The approach is inspired by the so-called time-stepper based numerical bifurcation theory. We illustrate the approach through the computation of both stable and unstable coarsely invariant states for kinetic Monte Carlo models of three simple surface reaction schemes. We quantify the linearized stability of these coarsely invariant states, perform pseudoarclength continuation, detect coarse limit point and coarse Hopf bifurcations, and construct two-parameter bifurcation diagrams.

“Coarse” stability and bifurcation analysis using stochastic simulators: Kinetic Monte Carlo examples

We implement a computer-assisted approach that, under appropriate conditions, allows the bifurcation analysis of the coarse dynamic behavior of microscopic simulators without requiring the explicit derivation of closed macroscopic equations for this behavior. The approach is inspired by the so-called time-step per based numerical bifurcation theory. We illustrate the approach through the computation of both stable and unstable coarsely invariant states for Kinetic Monte Carlo models of three simple surface reaction schemes. We quantify the linearized stability of these coarsely invariant states, perform pseudo-arclength continuation, detect coarse limit point and coarse Hopf bifurcations and construct two-parameter bifurcation diagrams.

Dimitrios Maroudas

Papers

Mechanism of hydrogen-induced crystallization of amorphous silicon

Dislocation loop structure, energy and mobility of self-interstitial atom clusters in bcc iron

Energetics of formation and migration of self-interstitials and self-interstitial clusters in α-iron

“Coarse” stability and bifurcation analysis using stochastic simulators: Kinetic Monte Carlo examples

Coarse Stability and Bifurcation Analysis Using Stochastic Simulators: Kinetic Monte Carlo Examples