30,253 research outputs found
Automated computation of materials properties
Materials informatics offers a promising pathway towards rational materials
design, replacing the current trial-and-error approach and accelerating the
development of new functional materials. Through the use of sophisticated data
analysis techniques, underlying property trends can be identified, facilitating
the formulation of new design rules. Such methods require large sets of
consistently generated, programmatically accessible materials data.
Computational materials design frameworks using standardized parameter sets are
the ideal tools for producing such data. This work reviews the state-of-the-art
in computational materials design, with a focus on these automated
frameworks. Features such as structural prototyping and
automated error correction that enable rapid generation of large datasets are
discussed, and the way in which integrated workflows can simplify the
calculation of complex properties, such as thermal conductivity and mechanical
stability, is demonstrated. The organization of large datasets composed of
calculations, and the tools that render them
programmatically accessible for use in statistical learning applications, are
also described. Finally, recent advances in leveraging existing data to predict
novel functional materials, such as entropy stabilized ceramics, bulk metallic
glasses, thermoelectrics, superalloys, and magnets, are surveyed.Comment: 25 pages, 7 figures, chapter in a boo
Analytical maximum-likelihood method to detect patterns in real networks
In order to detect patterns in real networks, randomized graph ensembles that
preserve only part of the topology of an observed network are systematically
used as fundamental null models. However, their generation is still
problematic. The existing approaches are either computationally demanding and
beyond analytic control, or analytically accessible but highly approximate.
Here we propose a solution to this long-standing problem by introducing an
exact and fast method that allows to obtain expectation values and standard
deviations of any topological property analytically, for any binary, weighted,
directed or undirected network. Remarkably, the time required to obtain the
expectation value of any property is as short as that required to compute the
same property on the single original network. Our method reveals that the null
behavior of various correlation properties is different from what previously
believed, and highly sensitive to the particular network considered. Moreover,
our approach shows that important structural properties (such as the modularity
used in community detection problems) are currently based on incorrect
expressions, and provides the exact quantities that should replace them.Comment: 26 pages, 10 figure
A network model for field and quenched disorder effects in artificial spin ice
We have performed a systematic study of the effects of field strength and
quenched disorder on the driven dynamics of square artificial spin ice. We
construct a network representation of the configurational phase space, where
nodes represent the microscopic configurations and a directed link between node
i and node j means that the field may induce a transition between the
corresponding configurations. In this way, we are able to quantitatively
describe how the field and the disorder affect the connectedness of states and
the reversibility of dynamics. In particular, we have shown that for optimal
field strengths, a substantial fraction of all states can be accessed using
external driving fields, and this fraction is increased by disorder. We discuss
how this relates to control and potential information storage applications for
artificial spin ices
ATLANTIDES: Automatic Configuration for Alert Verification in Network Intrusion Detection Systems
We present an architecture designed for alert verification (i.e., to reduce false positives) in network intrusion-detection systems. Our technique is based on a systematic (and automatic) anomaly-based analysis of the system output, which provides useful context information regarding the network services. The false positives raised by the NIDS analyzing the incoming traffic (which can be either signature- or anomaly-based) are reduced by correlating them with the output anomalies. We designed our architecture for TCP-based network services which have a client/server architecture (such as HTTP). Benchmarks show a substantial reduction of false positives between 50% and 100%
Generalized Bose-Fermi statistics and structural correlations in weighted networks
We derive a class of generalized statistics, unifying the Bose and Fermi
ones, that describe any system where the first-occupation energies or
probabilities are different from subsequent ones, as in presence of thresholds,
saturation, or aging. The statistics completely describe the structural
correlations of weighted networks, which turn out to be stronger than expected
and to determine significant topological biases. Our results show that the null
behavior of weighted networks is different from what previously believed, and
that a systematic redefinition of weighted properties is necessary.Comment: Final version accepted for publication on Physical Review Letter
- …