Inherent Structures for Soft Long-Range Interactions in Two-Dimensional Many-Particle Systems

We generate inherent structures, local potential-energy minima, of the "$k$-space overlap potential" in two-dimensional many-particle systems using a cooling and quenching simulation technique. The ground states associated with the $k$-space overlap potential are stealthy ({\it i.e.,} completely suppress single scattering of radiation for a range of wavelengths) and hyperuniform ({\it i.e.,} infinite wavelength density fluctuations vanish). However, we show via quantitative metrics that the inherent structures exhibit a range of stealthiness and hyperuniformity depending on the fraction of degrees of freedom that are constrained. Inherent structures in two dimensions typically contain five-particle rings, wavy grain boundaries, and vacancy-interstitial defects. The structural and thermodynamic properties of inherent structures are relatively insensitive to the temperature from which they are sampled, signifying that the energy landscape is relatively flat and devoid of deep wells. Using the nudged-elastic-band algorithm, we construct paths from ground-state configurations to inherent structures and identify the transition points between them. In addition, we use point patterns generated from a random sequential addition (RSA) of hard disks, which are nearly stealthy, and examine the particle rearrangements necessary to make the configurations absolutely stealthy. We introduce a configurational proximity metric to show that only small local, but collective, particle rearrangements are needed to drive initial RSA configurations to stealthy disordered ground states. These results lead to a more complete understanding of the unusual behaviors exhibited by the family of "collective-coordinate" potentials to which the $k$-space overlap potential belongs.Comment: 36 pages, 16 figure

Tetratic Order in the Phase Behavior of a Hard-Rectangle System

Previous Monte Carlo investigations by Wojciechowski \emph{et al.} have found two unusual phases in two-dimensional systems of anisotropic hard particles: a tetratic phase of four-fold symmetry for hard squares [Comp. Methods in Science and Tech., 10: 235-255, 2004], and a nonperiodic degenerate solid phase for hard-disk dimers [Phys. Rev. Lett., 66: 3168-3171, 1991]. In this work, we study a system of hard rectangles of aspect ratio two, i.e., hard-square dimers (or dominos), and demonstrate that it exhibits a solid phase with both of these unusual properties. The solid shows tetratic, but not nematic, order, and it is nonperiodic having the structure of a random tiling of the square lattice with dominos. We obtain similar results with both a classical Monte Carlo method using true rectangles and a novel molecular dynamics algorithm employing rectangles with rounded corners. It is remarkable that such simple convex two-dimensional shapes can produce such rich phase behavior. Although we have not performed exact free-energy calculations, we expect that the random domino tiling is thermodynamically stabilized by its degeneracy entropy, well-known to be $1.79k_{B}$ per particle from previous studies of the dimer problem on the square lattice. Our observations are consistent with a KTHNY two-stage phase transition scenario with two continuous phase transitions, the first from isotropic to tetratic liquid, and the second from tetratic liquid to solid.Comment: Submitted for publicatio

Classical Disordered Ground States: Super-Ideal Gases, and Stealth and Equi-Luminous Materials

Using a collective coordinate numerical optimization procedure, we construct ground-state configurations of interacting particle systems in various space dimensions so that the scattering of radiation exactly matches a prescribed pattern for a set of wave vectors. We show that the constructed ground states are, counterintuitively, disordered (i.e., possess no long-range order) in the infinite-volume limit. We focus on three classes of configurations with unique radiation scattering characteristics: (i)``stealth'' materials, which are transparent to incident radiation at certain wavelengths; (ii)``super-ideal'' gases, which scatter radiation identically to that of an ensemble of ideal gas configurations for a selected set of wave vectors; and (iii)``equi-luminous'' materials, which scatter radiation equally intensely for a selected set of wave vectors. We find that ground-state configurations have an increased tendency to contain clusters of particles as one increases the prescribed luminosity. Limitations and consequences of this procedure are detailed.Comment: 44 pages, 16 figures, revtek

Self-assembly of the simple cubic lattice with an isotropic potential

Conventional wisdom presumes that low-coordinated crystal ground states require directional interactions. Using our recently introduced optimization procedure to achieve self-assembly of targeted structures (Phys. Rev. Lett. 95, 228301 (2005), Phys. Rev. E 73, 011406 (2006)), we present an isotropic pair potential $V(r)$ for a three-dimensional many-particle system whose classical ground state is the low-coordinated simple cubic (SC) lattice. This result is part of an ongoing pursuit by the authors to develop analytical and computational tools to solve statistical-mechanical inverse problems for the purpose of achieving targeted self-assembly. The purpose of these methods is to design interparticle interactions that cause self-assembly of technologically important target structures for applications in photonics, catalysis, separation, sensors and electronics. We also show that standard approximate integral-equation theories of the liquid state that utilize pair correlation function information cannot be used in the reverse mode to predict the correct simple cubic potential. We report in passing optimized isotropic potentials that yield the body-centered cubic and simple hexagonal lattices, which provide other examples of non-close-packed structures that can be assembled using isotropic pair interactions.Comment: 16 pages, 12 figures. Accepted for publication in Physical Review

Quantum phase excitations in Ginzburg-Landau superconductors

We give a straightforward generalization of the Ginzburg-Landau theory for superconductors where the scalar phase field is replaced by an antisymmetric Kalb-Ramond field. We predict that at very low temperatures, where quantum phase effects are expected to play a significant role, the presence of vortices destroys superconductivity.Comment: revtex, 4 pages, no figure

Mining Audit Data to Build Intrusion Detection Models

In this paper we discuss a data mining framework for constructing intrusion detection models. The key ideas are to mine system audit data for consistent and useful patterns of program and user behavior, and use the set of relevant system features presented in the patterns to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Our past experiments showed that classifiers can be used to detect intrusions, provided that sufficient audit data is available for training and the right set of system features are selected. We propose to use the association rules and frequent episodes computed from audit data as the basis for guiding the audit data gathering and feature selection processes. We modify these two basic algorithms to use axis attribute(s) as a form of item constraints to compute only the relevant ("useful") patterns, and an iterative level-wise approximate mining procedure to uncover the low frequency (but important) patterns. We report our experiments in using these algorithms on real-world audit data

Mining in a Data-flow Environment: Experience in Network Intrusion Detection

We discuss the KDD process in "data-flow" environments, where unstructured and time dependent data can be processed into various levels of structured and semantically-rich forms for analysis tasks. Using network intrusion detection as a concrete application example, we describe how to construct models that are both accurate in describing the underlying concepts, and efficient when used to analyze data in real-time. We present procedures for analyzing frequent patterns from lower level data and constructing appropriate features to formulate higher level data. The features generated from various levels of data have different computational costs (in time and space). We show that in order to minimize the time required in using the classification models in a real-time environment, we can exploit the "necessary conditions" associated with the low-cost features to determine whether some high-cost features need to be computed and the corresponding classification rules need to be checked. We have applied our tools to the problem of building network intrusion detection models. We report our experiments using the network data provided as part of the 1998 DARPA Intrusion Detection Evaluation program. We also discuss our experience in using the mined models in NFR, a real-time network intrusion detection system