4,606 research outputs found

    Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual Phase Materials

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    Integrated Computational Materials Engineering (ICME) calls for the integration of computational tools into the materials and parts development cycle, while the Materials Genome Initiative (MGI) calls for the acceleration of the materials development cycle through the combination of experiments, simulation, and data. As they stand, both ICME and MGI do not prescribe how to achieve the necessary tool integration or how to efficiently exploit the computational tools, in combination with experiments, to accelerate the development of new materials and materials systems. This paper addresses the first issue by putting forward a framework for the fusion of information that exploits correlations among sources/models and between the sources and `ground truth'. The second issue is addressed through a multi-information source optimization framework that identifies, given current knowledge, the next best information source to query and where in the input space to query it via a novel value-gradient policy. The querying decision takes into account the ability to learn correlations between information sources, the resource cost of querying an information source, and what a query is expected to provide in terms of improvement over the current state. The framework is demonstrated on the optimization of a dual-phase steel to maximize its strength-normalized strain hardening rate. The ground truth is represented by a microstructure-based finite element model while three low fidelity information sources---i.e. reduced order models---based on different homogenization assumptions---isostrain, isostress and isowork---are used to efficiently and optimally query the materials design space.Comment: 19 pages, 11 figures, 5 table

    Big-Data-Driven Materials Science and its FAIR Data Infrastructure

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    This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an all-embracing sharing, an efficient data infrastructure, and the rich ecosystem of computer codes used in the community are of critical importance. For shaping this forth paradigm and contributing to the development or discovery of improved and novel materials, data must be what is now called FAIR -- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets the stage for advances of methods from artificial intelligence that operate on large data sets to find trends and patterns that cannot be obtained from individual calculations and not even directly from high-throughput studies. Recent progress is reviewed and demonstrated, and the chapter is concluded by a forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer 2018/201

    Automated X-ray image analysis for cargo security: Critical review and future promise

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    We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs- and security-related threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo

    Roadmap on Machine learning in electronic structure

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    AbstractIn recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century

    A Review of Automated Image Understanding within 3D Baggage Computed Tomography Security Screening

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    Baggage inspection is the principal safeguard against the transportation of prohibited and potentially dangerous materials at airport security checkpoints. Although traditionally performed by 2D X-ray based scanning, increasingly stringent security regulations have led to a growing demand for more advanced imaging technologies. The role of X-ray Computed Tomography is thus rapidly expanding beyond the traditional materials-based detection of explosives. The development of computer vision and image processing techniques for the automated understanding of 3D baggage-CT imagery is however, complicated by poor image resolutions, image clutter and high levels of noise and artefacts. We discuss the recent and most pertinent advancements and identify topics for future research within the challenging domain of automated image understanding for baggage security screening CT

    Multivariate Analysis Applications in X-ray Diffraction

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    : Multivariate analysis (MA) is becoming a fundamental tool for processing in an efficient way the large amount of data collected in X-ray diffraction experiments. Multi-wedge data collections can increase the data quality in case of tiny protein crystals; in situ or operando setups allow investigating changes on powder samples occurring during repeated fast measurements; pump and probe experiments at X-ray free-electron laser (XFEL) sources supply structural characterization of fast photo-excitation processes. In all these cases, MA can facilitate the extraction of relevant information hidden in data, disclosing the possibility of automatic data processing even in absence of a priori structural knowledge. MA methods recently used in the field of X-ray diffraction are here reviewed and described, giving hints about theoretical background and possible applications. The use of MA in the framework of the modulated enhanced diffraction technique is described in detail

    High throughput procedure utilising chlorophyll fluorescence imaging to phenotype dynamic photosynthesis and photoprotection in leaves under controlled gaseous conditions

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    © 2019 The Author(s). Background: As yields of major crops such as wheat (T. aestivum) have begun to plateau in recent years, there is growing pressure to efficiently phenotype large populations for traits associated with genetic advancement in yield. Photosynthesis encompasses a range of steady state and dynamic traits that are key targets for raising Radiation Use Efficiency (RUE), biomass production and grain yield in crops. Traditional methodologies to assess the full range of responses of photosynthesis, such a leaf gas exchange, are slow and limited to one leaf (or part of a leaf) per instrument. Due to constraints imposed by time, equipment and plant size, photosynthetic data is often collected at one or two phenological stages and in response to limited environmental conditions. Results: Here we describe a high throughput procedure utilising chlorophyll fluorescence imaging to phenotype dynamic photosynthesis and photoprotection in excised leaves under controlled gaseous conditions. When measured throughout the day, no significant differences (P > 0.081) were observed between the responses of excised and intact leaves. Using excised leaves, the response of three cultivars of T. aestivum to a user - defined dynamic lighting regime was examined. Cultivar specific differences were observed for maximum PSII efficiency (F v′/F m′ - P 130 μmol m-2 s-1 photosynthetic photon flux density (PPFD). Conclusions: Here we demonstrate the development of a high-throughput (> 500 samples day-1) method for phenotyping photosynthetic and photo-protective parameters in a dynamic light environment. The technique exploits chlorophyll fluorescence imaging in a specifically designed chamber, enabling controlled gaseous environment around leaf sections. In addition, we have demonstrated that leaf sections do not different from intact plant material even > 3 h after sampling, thus enabling transportation of material of interest from the field to this laboratory based platform. The methodologies described here allow rapid, custom screening of field material for variation in photosynthetic processes

    Magnified image spatial spectrum (MISS) microscopy for nanometer and millisecond scale label-free imaging

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    Label-free imaging of rapidly moving, sub-diffraction sized structures has important applications in both biology and material science, as it removes the limitations associated with fluorescence tagging. However, unlabeled nanoscale particles in suspension are difficult to image due to their transparency and fast Brownian motion. Here we describe a novel interferometric imaging technique referred to as Magnified Image Spatial Spectrum (MISS) microscopy, which overcomes these challenges. The MISS microscope provides quantitative phase information and enables dynamic light scattering investigations with an overall optical path length sensitivity of 0.95 nm at 833 frames per second acquisition rate. Using spatiotemporal filtering, we find that the sensitivity can be further pushed down to 10−3-10−2 nm. We demonstrate the instrument???s capability through colloidal nanoparticle sizing down to 20 nm diameter and measurements of live neuron membrane dynamics. MISS microscopy is implemented as an upgrade module to an existing microscope, which converts it into a powerful light scattering instrument. Thus, we anticipate that MISS will be adopted broadly for both material and life sciences applications

    Designing Metabolite Biosensors and Engineering Genetic Circuits to Regulate Metabolic Pathways

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    Microbial production of chemicals has provided an attractive alternative to chemical synthesis. A key to make this technology economically viable is to improve titers, productivities, and strain robustness. However, pathway productivities and yields are often limited by metabolic imbalances that inhibit cell growth and chemical production. In contrast, natural metabolic pathways are dynamically regulated according to cellular metabolic status. Dynamic regulation allows cells to adjust metabolite concentrations to optimal levels and avoid wasting carbon and energy. Inspired by nature, synthetic regulatory circuits have shown great promise in improving titers and productivities, because they can balance the metabolism by dynamically adjusting enzyme expression levels according to cellular metabolic status. To engineer synthetic regulatory circuits to improve production, we must design and tune metabolite biosensors, and also understand the metabolic dynamics to identify the optimal regulatory architecture. The research presented here addresses both these key aspects and demonstrates an application of genetic circuits to improve pathway production. Specifically, we develop theories that predict and experimentally validate a coupling between dynamic range and response threshold in transcription factor-based biosensors, and provide design guidelines to orthogonally control the biosensor output and its sensitivity. Next, we develop a malonyl-CoA sensor-actuator and demonstrate its application to engineering a negative feedback circuit to improve fatty acid production. Finally, genetic circuits with various architectures are constructed to study metabolic dynamics, which reveal that negative feedback circuits can dramatically accelerate metabolic dynamics. The findings of this dissertation provide rational design principles for transcription factor-based metabolite biosensors and a systematic understanding of metabolic dynamics under various regulation architectures. They provide valuable tools and knowledge to engineer metabolic circuits to regulate various metabolic pathways, increasing titers, productivities, and yields
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