Neural networks as tools for predicting materials properties

Materials science is of fundamental significance to science and technology because our industrial base and society depend upon our ability to develop advanced materials. Materials and materials processing cuts across almost every sector of industry. The key in all of these areas is the ability to rapidly screen possible designs which will have significant impact. However up to now materials design and processing have been to a large extent empirical sciences. In addition we are still unable to design new alloys and polymers to meet application specific requirements. Being able to do so quickly and at minimum cost would provide an incredible advantage. Obviously, the ability to predict physical, chemical, or mechanical properties of compounds prior to their synthesis is of great technological value in optimizing their design, processing, or recycling. In addition, in order to realize the ultimate goal of materials by computational design, the reverse problem, prediction of chemical structure based on desired properties, has to be resolved. Research at ORNL has lead to the development of a novel computational paradigm (coupling computational neural networks with graph theory, genetic algorithms, wavelet theory, fuzzy logic, molecular dynamics, and quantum chemistry) capable of performing accurate computational synthesis (both predictions of properties or the design of compounds that have specified performance criteria). The computational paradigm represents a hybrid of a number of emerging technologies and has proven to work very well for test compounds ranging from small organic molecules to polymeric materials. Fundamental to the method is the neural network-based formulation of the correlations between structure and properties. The advantages of this method is in its ease of use, speed, accuracy, and that it can be used to predict both properties from structure, and also structure from properties

Configuration development study of the X-24C hypersonic research airplane

Bottom line results were made of a three-phase study to determine the feasibility of designing, building, and operating, and maintaining an air-launched high performance aircraft capable of cruising at speeds up to Mach 8 for short durations. The results show that Lockalloy heat-sink structure affords the capability for a 'work-horse' vehicle which can serve as an excellent platform for this research. It was further concluded that the performance of a blended wing body configuration surpassed that of a lifting body design for typical X-24C missions. The cost of a two vehicle program, less engines, B-52 modification and contractor support after delivery, can be kept within $70M (in Jan. 1976 dollars)

Analysis of parameter mismatches in the master stability function for network synchronization

In this letter, we perform a sensitivity analysis on the master stability function approach for the synchronization of networks of coupled dynamical systems. More specifically, we analyze the linear stability of a nearly synchronized solution for a network of coupled dynamical systems, for which the individual dynamics and output functions of each unit are approximately identical and the sums of the entries in the rows of the coupling matrix slightly deviate from zero. The motivation for this parametric study comes from experimental instances of synchronization in human-made or natural settings, where ideal conditions are difficult to observe.Comment: Accepted for publication in EuroPhysics Letter

Individual rules for trail pattern formation in Argentine ants (Linepithema humile)

We studied the formation of trail patterns by Argentine ants exploring an empty arena. Using a novel imaging and analysis technique we estimated pheromone concentrations at all spatial positions in the experimental arena and at different times. Then we derived the response function of individual ants to pheromone concentrations by looking at correlations between concentrations and changes in speed or direction of the ants. Ants were found to turn in response to local pheromone concentrations, while their speed was largely unaffected by these concentrations. Ants did not integrate pheromone concentrations over time, with the concentration of pheromone in a 1 cm radius in front of the ant determining the turning angle. The response to pheromone was found to follow a Weber's Law, such that the difference between quantities of pheromone on the two sides of the ant divided by their sum determines the magnitude of the turning angle. This proportional response is in apparent contradiction with the well-established non-linear choice function used in the literature to model the results of binary bridge experiments in ant colonies (Deneubourg et al. 1990). However, agent based simulations implementing the Weber's Law response function led to the formation of trails and reproduced results reported in the literature. We show analytically that a sigmoidal response, analogous to that in the classical Deneubourg model for collective decision making, can be derived from the individual Weber-type response to pheromone concentrations that we have established in our experiments when directional noise around the preferred direction of movement of the ants is assumed.Comment: final version, 9 figures, submitted to Plos Computational Biology (accepted

Symmetry restoring bifurcation in collective decision-making.

How social groups and organisms decide between alternative feeding sites or shelters has been extensively studied both experimentally and theoretically. One key result is the existence of a symmetry-breaking bifurcation at a critical system size, where there is a switch from evenly distributed exploitation of all options to a focussed exploitation of just one. Here we present a decision-making model in which symmetry-breaking is followed by a symmetry restoring bifurcation, whereby very large systems return to an even distribution of exploitation amongst options. The model assumes local positive feedback, coupled with a negative feedback regulating the flow toward the feeding sites. We show that the model is consistent with three different strains of the slime mold Physarum polycephalum, choosing between two feeding sites. We argue that this combination of feedbacks could allow collective foraging organisms to react flexibly in a dynamic environment

Collective Animal Behavior from Bayesian Estimation and Probability Matching

Animals living in groups make movement decisions that depend, among other factors, on social interactions with other group members. Our present understanding of social rules in animal collectives is based on empirical fits to observations and we lack first-principles approaches that allow their derivation. Here we show that patterns of collective decisions can be derived from the basic ability of animals to make probabilistic estimations in the presence of uncertainty. We build a decision-making model with two stages: Bayesian estimation and probabilistic matching.
In the first stage, each animal makes a Bayesian estimation of which behavior is best to perform taking into account personal information about the environment and social information collected by observing the behaviors of other animals. In the probability matching stage, each animal chooses a behavior with a probability given by the Bayesian estimation that this behavior is the most appropriate one. This model derives very simple rules of interaction in animal collectives that depend only on two types of reliability parameters, one that each animal assigns to the other animals and another given by the quality of the non-social information. We test our model by obtaining theoretically a rich set of observed collective patterns of decisions in three-spined sticklebacks, Gasterosteus aculeatus, a shoaling fish species. The quantitative link shown between probabilistic estimation and collective rules of behavior allows a better contact with other fields such as foraging, mate selection, neurobiology and psychology, and gives predictions for experiments directly testing the relationship between estimation and collective behavior