Machine learning for crystal identification and discovery

As computers get faster, researchers -- not hardware or algorithms -- become the bottleneck in scientific discovery. Computational study of colloidal self-assembly is one area that is keenly affected: even after computers generate massive amounts of raw data, performing an exhaustive search to determine what (if any) ordered structures occur in a large parameter space of many simulations can be excruciating. We demonstrate how machine learning can be applied to discover interesting areas of parameter space in colloidal self assembly. We create numerical fingerprints -- inspired by bond orientational order diagrams -- of structures found in self-assembly studies and use these descriptors to both find interesting regions in a phase diagram and identify characteristic local environments in simulations in an automated manner for simple and complex crystal structures. Utilizing these methods allows analysis methods to keep up with the data generation ability of modern high-throughput computing environments.Comment: Fixed typo, added missing acknowledgment, added supplementary informatio

Refining self-propelled particle models for collective behaviour

Swarming, schooling, flocking and herding are all names given to the wide variety of collective behaviours exhibited by groups of animals, bacteria and even individual cells. More generally, the term swarming describes the behaviour of an aggregate of agents (not necessarily biological) of similar size and shape which exhibit some emergent property such as directed migration or group cohesion. In this paper we review various individual-based models of collective behaviour and discuss their merits and drawbacks. We further analyse some one-dimensional models in the context of locust swarming. In specific models, in both one and two dimensions, we demonstrate how varying the parameters relating to how much attention individuals pay to their neighbours can dramatically change the behaviour of the group. We also introduce leader individuals to these models with the ability to guide the swarm to a greater or lesser degree as we vary the parameters of the model. We consider evolutionary scenarios for models with leaders in which individuals are allowed to evolve the degree of influence neighbouring individuals have on their subsequent motion

Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories. We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that proposes a new form of interaction between users and digital libraries, where the latter are adapted to users and their surroundings

State of the Art in Parallel Computing with R

R is a mature open-source programming language for statistical computing and graphics. Many areas of statistical research are experiencing rapid growth in the size of data sets. Methodological advances drive increased use of simulations. A common approach is to use parallel computing. This paper presents an overview of techniques for parallel computing with R on computer clusters, on multi-core systems, and in grid computing. It reviews sixteen different packages, comparing them on their state of development, the parallel technology used, as well as on usability, acceptance, and performance. Two packages (snow, Rmpi) stand out as particularly suited to general use on computer clusters. Packages for grid computing are still in development, with only one package currently available to the end user. For multi-core systems five different packages exist, but a number of issues pose challenges to early adopters. The paper concludes with ideas for further developments in high performance computing with R. Example code is available in the appendix.

Molecular dynamics simulation of the order-disorder phase transition in solid NaNO2_2

We present molecular dynamics simulations of solid NaNO$_2$ using pair potentials with the rigid-ion model. The crystal potential surface is calculated by using an \emph{a priori} method which integrates the \emph{ab initio} calculations with the Gordon-Kim electron gas theory. This approach is carefully examined by using different population analysis methods and comparing the intermolecular interactions resulting from this approach with those from the \emph{ab initio} Hartree-Fock calculations. Our numerics shows that the ferroelectric-paraelectric phase transition in solid NaNO$_2$ is triggered by rotation of the nitrite ions around the crystallographical c axis, in agreement with recent X-ray experiments [Gohda \textit{et al.}, Phys. Rev. B \textbf{63}, 14101 (2000)]. The crystal-field effects on the nitrite ion are also addressed. Remarkable internal charge-transfer effect is found.Comment: RevTeX 4.0, 11 figure

An Analytical Model for the Triaxial Collapse of Cosmological Perturbations

We present an analytical model for the non-spherical collapse of overdense regions out of a Gaussian random field of initial cosmological perturbations. The collapsing region is treated as an ellipsoid of constant density, acted upon by the quadrupole tidal shear from the surrounding matter. The dynamics of the ellipsoid is set by the ellipsoid self-gravity and the external quadrupole shear. Both forces are linear in the coordinates and therefore maintain homogeneity of the ellipsoid at all times. The amplitude of the external shear is evolved into the non-linear regime in thin spherical shells that are allowed to move only radially according to the mass interior to them. We describe how the initial conditions can be drawn in the appropriate correlated way from a random field of initial density perturbations. By considering many random realizations of the initial conditions, we calculate the distribution of shapes and angular momenta acquired by objects through the coupling of their quadrupole moment to the tidal shear. The average value of the spin parameter, 0.04, is found to be only weakly dependent on the system mass, the mean cosmological density, or the initial power spectrum of perturbations, in agreement with N-body simulations. For the cold dark matter power spectrum, most objects evolve from a quasi-spherical initial state to a pancake or filament and then to complete virialization. Low-spin objects tend to be more spherical. The evolution history of shapes is primarily induced by the external shear and not by the initial triaxiality of the objects. The statistical distribution of the triaxial shapes of collapsing regions can be used to test cosmological models against galaxy surveys on large scales.Comment: 42 pages, Tex, followed by 10 uuencoded figure