QSATS: MPI-driven quantum simulations of atomic solids at zero temperature

We describe QSATS, a parallel code for performing variational path integral simulations of the quantum mechanical ground state of monatomic solids. QSATS is designed to treat Boltzmann quantum solids, in which individual atoms are permanently associated with distinguishable crystal lattice sites and undergo large-amplitude zero-point motions around these sites. We demonstrate the capabilities of QSATS by using it to compute the total energy and potential energy of hexagonal close packed solid ⁴He at the density .

Program summary

Program title:QSATSCatalogue identifier: AEJE_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJE_v1_0.htmlProgram obtainable from: CPC Program Library, Queen?s University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 7329No. of bytes in distributed program, including test data, etc.: 61 685Distribution format: tar.gzProgramming language: Fortran 77.Computer: QSATS should execute on any distributed parallel computing system that has the Message Passing Interface (MPI) [1] libraries installed.Operating system: Unix or Linux.Has the code been vectorized or parallelized?: Yes, parallelized using MPI [1].RAM: The memory requirements of QSATS depend on both the number of atoms in the crystal and the number of replicas in the variational path integral chain. For parameter sets A and C (described in the long write-up), approximately 4.5 Mbytes and 12 Mbytes, respectively, are required for data storage by QSATS (exclusive of the executable code).Classification: 7.7, 16.13.External routines: Message Passing Interface (MPI) [1]Nature of problem: QSATS simulates the quantum mechanical ground state for a monatomic crystal characterized by large-amplitude zero-point motions of individual (distinguishable) atoms around their nominal lattice sites.Solution method: QSATS employs variational path integral quantum Monte Carlo techniques to project the system?s ground state wave function out of a suitably-chosen trial wave function.Restrictions: QSATS neglects quantum statistical effects associated with the exchange of identical particles. As distributed, QSATS assumes that the potential energy function for the crystal is a pairwise additive sum of atom–atom interactions.Additional comments: An auxiliary program, ELOC, is provided that uses the output generated by QSATS to compute both the crystal?s ground state energy and the expectation value of the crystal?s potential energy. End users can modify ELOC as needed to compute the expectation value of other coordinate-space observables.Running time: QSATS requires roughly 3 hours to run a simulation using parameter set A on a cluster of 12 Xeon processors with clock speed 2.8 GHz. Roughly 15 hours are needed to run a simulation using parameter set C on the same cluster.References:

• [1] 

  For information about MPI, visit http://www.mcs.anl.gov/mpi/.

     

Accurate Computation of Electric Field Enhancement Factors for Metallic Nanoparticles Using the Discrete Dipole Approximation

We model the response of nanoscale Ag prolate spheroids to an external uniform static electric field using simulations based on the discrete dipole approximation, in which the spheroid is represented as a collection of polarizable subunits. We compare the results of simulations that employ subunit polarizabilities derived from the Clausius–Mossotti relation with those of simulations that employ polarizabilities that include a local environmental correction for subunits near the spheroid’s surface [Rahmani et al. Opt Lett 27: 2118 (2002)]. The simulations that employ corrected polarizabilities give predictions in very good agreement with exact results obtained by solving Laplace’s equation. In contrast, simulations that employ uncorrected Clausius–Mossotti polarizabilities substantially underestimate the extent of the electric field “hot spot” near the spheroid’s sharp tip, and give predictions for the field enhancement factor near the tip that are 30 to 50% too small.     

Pattern-based adaptive process control

A controller that has an adaptation strategy based on the patterns exhibited in the recent history of the controller error and manipulated input variable is detailed. The method of approach is to analyze all controller error transients to extract features including overshoot and damping. The deviation of these features from desired values is then used to update the parameters of an internal controller model. When the error transient is caused by a disturbance, the patterns exhibited in the manipulated input variable are also considered to determine whether the disturbance has actually changed the character of the process, or if the error patterns are simply reflecting the nature of the disturbance itself. The strategy is general to a number of model-based controllers and is demonstrated here using both a long range predictive controller and a PI controller with Smith predictor. Details of the strategy are presented along with a demonstration of the method on process simulations constructed to present challenging applications.     

Dynamics and Potential Surfaces of Small Clusters

Some of the tantalizing puzzles regarding clusters are described. Some specific results concerning the dynamical behavior of clusters of particular sizes and having various potentials are described, to give some insight into the relationship between the form of a potential and the kind of behavior it yields. Solid-liquid, solid-soft solid, surface melting, and a remarkable single “subsurface layer mobility” all can be found in particular instances.     

An initial data model,using the object-oriented paradigm,for sculptured-feature-based design

This paper presents an initial object [1] model based on entity/relationship [2] structures for sculptured-feature-based design. The model provides the basis for a system that will allow designers to define their products in terms of interacting features they recognise, and categories that are meaningful to their organisation. Design solutions can also be explored and ultimately defined by means of parametric shape algorithms and values relevant to a particular product domain. Finally, the three-dimensional geometry can be evaluated throughout the design activity for visualisation, analysis, and ultimately manufacture.