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

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

/pdf/community-detection-in-graphs-34rogm3r6p.pdf

Community detection in graphs

https://link.springer.com/content/pdf/10.2307%2F3343168.pdf

Development as Freedom

Investigations over more than a century and the recent availability of electronic databases of income and wealth distribution (ranging from a national sample survey of household assets to the income tax return data available from governmental agencies) have revealed some remarkable features. Irrespective of many differences in culture, history, social structure, indicators of relative prosperity (such as gross domestic product or infant mortality) and, to some extent, the economic policies followed in different countries, the income distribution seems to follow a particular universal pattern, as does the wealth distribution: after an initial rise, the number density of people rapidly decays with their income, the bulk described by a Gibbs or log-normal distribution crossing over at the very high income range (for 5–10% of the richest members of the population) to a power law, as shown in Fig. 1.1. The power law in income and wealth distribution is called the Pareto law , after the Italian sociologist and economist Vilfredo Pareto. The log-normal part is named as the Gibrat law , after the French economist Robert Gibrat. This seems to be a universal feature: from ancient Egyptian society (Abul-Magd 2002) through nineteenth-century Europe (Pareto 1897; Champernowne 1953) to modern Japan (Chatterjee et al. 2005b; Chakrabarti et al. 2006). The same is true across the globe today: from the advanced capitalist economy of USA (Chatterjee et al. 2005b; Kar Gupta 2006a; Richmond et al. 2006) to the developing economy of India (Sinha 2006).

Econophysics of Income and Wealth Distributions: Income and wealth distribution data for different countries

Prices of commodities or assets produce what is called time-series. Different kinds of financial time-series have been recorded and studied for decades. Nowadays, all transactions on a financial market are recorded, leading to a huge amount of data available, either for free in the Internet or commercially. Financial time-series analysis is of great interest to practitioners as well as to theoreticians, for making inferences and predictions. Furthermore, the stochastic uncertainties inherent in financial time-series and the theory needed to deal with them make the subject especially interesting not only to economists, but also to statisticians and physicists. While it would be a formidable task to make an exhaustive review on the topic, with this review we try to give a flavor of some of its aspects.

Financial time-series analysis: A brief overview

In the present paper, we have investigated the effect of plasmonic gold nanoparticles (Au NPs) decoration on the photocatalytic efficiency of molybdenum disulfide (MoS2) nanosheets. The Au NPs are grown on the surfaces of chemically exfoliated MoS2 nanosheets by chemical reduction method with four different concentrations. The resulting Au-MoS2 nanostructures (NSs) are then characterized by X-ray diffractometer, Raman spectrometer, absorption spectrophotometer, field emission scanning electron microscopy, energy dispersive X-ray, and transmission electron microscopy (TEM). Sizes of the exfoliated MoS2 nanosheets are ~ 700 nm. In addition, the sizes of Au nanoparticles increase from 8.02 ± 2.03 nm to 9.81 ± 3.18 nm with the increase in concentrations of Au ions, as revealed by TEM imaging. Exfoliated MoS2 and Au-MoS2 NSs are used to study the photocatalytic degradation of organic dyes, methyl red (MR) and methylene blue (MB). Under UV–Visible light irradiation, pristine MoS2 shows photodegradation efficiencies in the range of 30.0% to 46.9% for MR, and 23.3% to 44.0% for MB, with varying exposure times of 30 to 120 min. However, Au-MoS2 NSs with the sets having maximum Au NPs concentrations, show enhanced degradation efficiencies from 70.2 to 96.7% for MR, and from 65.2 to 94.3% for MB. The degradation rate constants vary from − 0.5660 to − 1.5551 min−1 for MR dye, and vary from − 0.3587 to − 1.2614 min−1 for MB dye. The multi-fold enhancements of degradation efficiencies for both the dyes with Au-MoS2 NSs, can be attributed to the presence of Au NPs acting as charge trapping sites in the NSs. We believe this type of study could provide a way to battle the ill-effects of environmental degradation that pose a major threat to humans as well as biodiversity. This study can be further extended to other semiconducting materials in conjugation with two dimensional materials for photocatalytic treatment of polluted water.

Enhanced photocatalytic activity of plasmonic Au nanoparticles incorporated MoS2 nanosheets for degradation of organic dyes

Many complex systems are characterized by power-law distributions, beginning with the first historical example of the Pareto law for the wealth distribution in economic systems. In the case of the Pareto law and other instances of power-law distributions, the power-law tail can be explained in the framework of canonical statistical mechanics as a statistical mixture of canonical equilibrium probability densities of heterogeneous subsystems at equilibrium. In this picture, each subsystem interacts (weakly) with the others and is characterized at equilibrium by a canonical distribution, but the distribution associated to the whole set of interacting subsystems can in principle be very different. This phenomenon, which is an example of the possible constructive role of the interplay between heterogeneity and noise, was observed in numerical experiments of Kinetic Exchange Models and presented in the conference “Econophys-Kolkata-I”, hold in Kolkata in 2005. The 2015 edition, taking place ten years later and coinciding with the twentieth anniversary of the 1995 conference hold in Kolkata where the term “Econophysics” was introduced, represents an opportunity for an overview in a historical perspective of this mechanism within the framework of heterogeneous kinetic exchange models (see also Kinetic exchange models as D-dimensional systems in this volume). We also propose a generalized framework, in which both quenched heterogeneity and time dependent parameters can comply constructively leading the system toward a more robust and extended power-law distribution.

The Microscopic Origin of the Pareto Law and Other Power-Law Distributions

Many complex systems are characterized by power-law distributions. In this article, we show that for various examples of power-law distributions, including the two probably most popular ones, the Pareto law for the wealth distribution and Zipf’s law for the occurrence frequency of words in a written text, the power-law tails of the probability distributions can be decomposed into a statistical mixture of canonical equilibrium probability densities of the subsystems. While the interacting units or subsystems have canonical distributions at equilibrium, as predicted by canonical statistical mechanics, the heterogeneity of the shapes of their distributions leads to the appearance of a power-law.

Anirban Chakraborti

Papers

Econophysics of Income and Wealth Distributions: Income and wealth distribution data for different countries

Financial time-series analysis: A brief overview

Enhanced photocatalytic activity of plasmonic Au nanoparticles incorporated MoS2 nanosheets for degradation of organic dyes

The Microscopic Origin of the Pareto Law and Other Power-Law Distributions

Power-Laws as Statistical Mixtures.