Abstract: Information exchange is critical to the execution and effectiveness of natural and artificial collective behaviors: fish schooling, birds flocking, amoebae aggregating or robots swarming. In particular, the emergence of dynamic collective responses in swarms confronted to complex environments underscore the central role played by social transmission of information. Here, the different possible origins of information flow bottlenecks are identified, and the associated effects on dynamic collective behaviors revealed using a combination of network-, control- and information-theoretic elements applied to a group of interacting self-propelled particles (SPPs). Specifically, we consider a minimalistic agent-based model consisting of N topologically interacting SPPs moving at constant speed through a domain having periodic boundaries. Each individual agent is characterized by its direction of travel and a canonical swarming behavior of the consensus type is examined. To account for the finiteness of the bandwidth, we consider synchronous information exchanges occurring every T = 1/2B, where the unit interval T is the minimum time interval between condition changes of data transmission signal. The agents move synchronously at discrete time steps T by a fixed distance upon receiving informational signals from their neighbors as per a linear update rule involving. We find a sufficient condition on the agents’ bandwidth B that guarantees the effectiveness of swarming while also highlighting the profound connection with the topology of the underlying interaction network. We also show that when decreasing B, the swarming behavior invariably vanishes following a second-order phase transition irrespectively of the intrinsic noise level.

Abstract: Modelling is essential for development of organic electronics, such as organic light emitting diodes (OLEDs), organic field-effect transistors (OFETs) and organic photovoltaics (OPV). OLEDs have currently most applications, as they are already used in super-thin energy-efficient displays for television sets and smartphones, and in future will be used for lighting applications exploiting a world market worth tens of billions Euro. OLEDs should be further developed to increase their performance and durability, and reduce the currently high production costs. The conventional development process is very costly and time-demanding due to the large number of possible materials which have to be synthesized for the production and characterization of prototypes. Deeper understanding of the relationship between OLED device properties and materials structure allows for high-throughput materials screening and thus a tremendous reduction of development costs. In simulations, the properties of various materials one can be virtually and cost-effectively explored and compared to measurements. Based on these results, material composition, morphology and manufacturing processes can be systematically optimized. A typical OLED consists of a stack of multiple crystalline or amorphous organic layers. To compute electronic transport properties, e.g. charge mobilities, a quantum mechanical model, in particular the density functional theory (DFT) is commonly employed. Recently, we performed simulations of electronic processes in OLED materials achieved by multiscale modelling, i.e. by integrating sub-models on different length scales to investigate charge transport in thin films based on the experimentally characterized semi-conducting small molecules [1]. Here, we present a novel scale-out computational strategy to for a tightly coupled multiscale model consisting of a core region with 500 molecules (5000 pairs) of charge hopping sites and a embedding region, containing about 10000 electrostatically interacting molecules. The energy levels of each site depend on the local electrostatic environment yielding a significant contribution to the energy disor-der. This effect is explicitly taken into account in the quantum mechanical sub-model in a self-consistent manner, which represents however, a considerable computational challenge. Thus the total number of DFT calculations needed is of the order of 10^5-10^6. DFT models scale mostly as N^3, where N is the number of basis functions which is strongly related to the number of electrons. While DFT is implemented in a number of efficiently parallelized electronic structure codes, the computational scaling of a single DFT calculation applied for amorphous organic materials is naturally limited by the molecule size. After every iteration cycle, data are exchanged between all contained molecules of the self-consistence loop to update the electrostatic environment of each site. This requires that the DFT sub-model is executed employing a second-level parallelisation with a special scheduling strategy. The realisation of this model on high performance computer (HPC) systems has several issues: i) The DFT sub-models, which are stand-alone applications (such as NWChem or TURBOMOLE), have to be spawned at run time via process forking; ii) Large amounts of input and output data have to be transferred to and from the DFT sub-models though the cluster file system. These two requirements limit the computational performance and often conflict with the usage policies of common HPC environments. In addition, sub-model scheduling and DFT data pre-/post-processing have severe impact on the overall performance. To this end, we designed a DFT application programming interface (API) with different language bindings, such as Python and C++, allowing linking of DFT sub-models, independent of the concrete DFT implementation, to multiscale models. In addition, we propose solutions for in-core handling large input and output data as well as efficient scheduling algorithms. In this contribution, we will describe the architecture and outline the technical implementation of a framework for nesting DFT sub-models. We will demonstrate the use and analyse the performance of the framework for multiscale modelling of OLED materials. The framework provides an API which can be used to integrate DFT sub-models in other applications. [1] P. Friederich, F. Symalla, V. Meded, T. Neumann and W. Wenzel, “Ab Initio Treatment of Disorder Effects in Amorphous Organic Materials: Toward Parameter Free Materials Simulation”, Journal of Chemical Theory and Computation 10, 3720–3725 (2014).

Abstract: Motivation: Horizontally transferred genomic islands (islands, GIs) have been referred to as important factors which contribute towards the emergences of pathogens and outbreak instances. The development of tools towards the identification of such elements and retracing their distribution patterns will help to understand how such cases arise. Sequence composition has been used to identify islands, infer their phylogeny; and determine their relative times of insertions. The collection and curation of known islands will enhance insight into island ontology and flow. Results: This paper introduces the merger of SeqWord Genomic Islands Sniffer (SWGIS) which utilizes composition based approaches for identification of islands in bacterial genomic sequences and the Predicted Genomic Islands (Pre_GI) database which houses 26,744 islands found in 2,407 bacterial plasmids and chromosomes. SWGIS is a standalone program that detects genomic islands using a set of optimized parametric measures with estimates of acceptable false positive and false negative rates. Pre_GI is novel repository that includes island ontology and flux. This study furthermore illustrates the need for parametric optimization towards the prediction of islands to minimize false negative and false positive predictions. In addition Pre_GI emphasizes the practicality of compounded knowledge a database affords in the detection and visualization of ontological links between islands. Availability: SWGIS is freely available on the web at http://www.bi.up.ac.za/SeqWord/sniffer/index.html. Pre_GI is freely accessible at http://pregi.bi.up.ac.za/index.php.