Results for “web application” — Page 8

DOE OSTI2011

National Ignition Facility and Managing Location, Component, and State

The National Ignition Facility (NIF) at the Lawrence Livermore National Laboratory is a stadium-sized facility that contains a 192-beam, 1.8-Megajoule, 500-Terawatt, ultraviolet laser system coupled with a 10-meter diameter target chamber. There are over 6,200 Line Replaceable Units (LRUs) comprised of more than 104,000 serialized parts that make up the NIF. Each LRU is a modular unit typically composed of a mechanical housing, laser optics (glass, lenses, or mirrors), and utilities. To date, there are more than 120,000 data sets created to characterize the attributes of these parts. Greater than 51,000 Work Permits have been issued to install, maintain, and troubleshoot the components. One integrated system is used to manage these data, and more. The Location Component and State (LoCoS) system is a web application built using Java Enterprise Edition technologies and is accessed by over 1,200 users. It is either directly or indirectly involved with each aspect of NIF work activity, and interfaces with ten external systems including the Integrated Computer Control System (ICCS) and the Laser Performance Operations Model (LPOM). Besides providing business functionality, LoCoS also acts as the NIF enterprise service bus. In this role, numerous integration approaches had to be adopted including: file exchange, database sharing, queuing, and web services in order to accommodate various business, technical, and security requirements. Architecture and implementation decisions are discussed.

42 ENGINEERING↗

DOE OSTI2019

Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees

Organic solar cells are an inexpensive, flexible alternative to traditional silicon-based solar cells but disadvantaged by low power conversion efficiency due to empirical design and complex manufacturing processes. This process can be accelerated by generating a comprehensive set of potential candidates. However, this would require a laborious trial and error method of modeling all possible polymer configurations. A machine learning model has the potential to accelerate the process of screening potential donor candidates by associating structural features of the compound using molecular fingerprints with their highest occupied molecular orbital energies. In this paper, extremely randomized tree learning models are employed for the prediction of HOMO values for donor compounds, and a web application is developed. Finally, the proposed models outperform neural networks trained on molecular fingerprints as well as SMILES, as well as other state-of-the-art architectures such as Chemception and Molecular Graph Convolution on two datasets of varying sizes.

42 ENGINEERING↗

DOE OSTI2025

Advancing molecular machine learning representations with stereoelectronics-infused molecular graphs

Molecular representation is a critical element in our understanding of the physical world and the foundation for modern molecular machine learning. Previous molecular machine learning models have used strings, fingerprints, global features and simple molecular graphs that are inherently information-sparse representations. However, as the complexity of prediction tasks increases, the molecular representation needs to encode higher fidelity information. This work introduces a new approach to infusing quantum-chemical-rich information into molecular graphs via stereoelectronic effects, enhancing expressivity and interpretability. Learning to predict the stereoelectronics-infused representation with a tailored double graph neural network workflow enables its application to any downstream molecular machine learning task without expensive quantum-chemical calculations. We show that the explicit addition of stereoelectronic information substantially improves the performance of message-passing two-dimensional machine learning models for molecular property prediction. We show that the learned representations trained on small molecules can accurately extrapolate to much larger molecular structures, yielding chemical insight into orbital interactions for previously intractable systems, such as entire proteins, opening new avenues of molecular design. Finally, we have developed a web application (simg.cheme.cmu.edu) where users can rapidly explore stereoelectronic information for their own molecular systems.

Boiko, Daniil A↗

DOE OSTI2017

ProteoSign: an end-user online differential proteomics statistical analysis platform

Profiling of proteome dynamics is crucial for understanding cellular behavior in response to intrinsic and extrinsic stimuli and maintenance of homeostasis. Over the last 20 years, mass spectrometry (MS) has emerged as the most powerful tool for large-scale identification and characterization of proteins. Bottom-up proteomics, the most common MS-based proteomics approach, has always been challenging in terms of data management, processing, analysis and visualization, with modern instruments capable of producing several gigabytes of data out of a single experiment. Here, we present ProteoSign, a freely available web application, dedicated in allowing users to perform proteomics differential expression/abundance analysis in a userfriendly and self-explanatory way. Although several non-commercial standalone tools have been developed for post-quantification statistical analysis of proteomics data, most of them are not end-user appealing as they often require very stringent installation of programming environments, third-party software packages and sometimes further scripting or computer programming. To avoid this bottleneck, we have developed a user-friendly software platform accessible via a web interface in order to enable proteomics laboratories and core facilities to statistically analyse quantitative proteomics data sets in a resource-efficient manner. ProteoSign is available at http://bioinformatics.med.uoc.gr/ProteoSign and the source code at https://github.com/yorgodillo/ ProteoSign.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Safety Analytics Forecasting and Evaluation Reporting (SAFER)

The SAFER Sandbox is a web application for testing various types of data visualization tools that analyze safety data across national laboratories. By enabling the user to visualize this data through visual and textual analyses, the SAFER Sandbox helps to facilitate the research and support for the National Nuclear Security Administration (NNSA) Office of Safety, Infrastructure and Operations (NA-50). Safety data across the national laboratory complex is not currently analyzed in one location. This prohibits administrators from viewing safety data trends and making large scale decisions based on a holistic view of the data. By implementing data analytics web services such as representative text summary, noun phrase extraction and graph exploration, users can view and explore safety data based on search queries. The data visualizations in the SAFER Sandbox reveal trends and point to items of concern based on safety data. The SAFER Sandbox is a testing ground for these visualizations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2014

Modeling CO 2 Sequestration in Saline Aquifer and Depleted Oil Reservoir To Evaluate Regional CO 2 Sequestration Potential of Ozark Plateau Aquifer System, South-Central Kansas

1. Drilled, cored, and logged three wells to the basement and collecting more than 2,700 ft of conventional core; obtained 20 m i2 of multicomponent 3D seismic imaging and merged and reprocessed more than 125 mi 2 of existing 3D seismic data for use in modeling CO 2 - EOR oil recovery and CO 2 storage in five oil fields in southern Kansas. 2. Determined the technical feasibility of injecting and sequestering CO 2 in a set of four depleted oil reservoirs in the Cutter, Pleasant Prairie South, Eubank, and Shuck fields in southwest Kansas; of concurrently recovering oil from those fields; and of quantifying the volumes of CO2 sequestered and oil recovered during the process. 3. Formed a consortium of six oil operating companies, five of which own and operate the four fields. The consortium became part of the Southwest Kansas CO 2 -EOR Initiative for the purpose of sharing data, knowledge, and interest in understanding the potential for CO 2 -EOR in Kansas. 4. Built a regional well database covering 30,000 mi 2 and containing stratigraphic tops from ~90,000 wells; correlated 30 major stratigraphic horizons; digitized key wells, including wireline logs and sample logs; and analyzed more than 3,000 drill stem tests to establish that fluid levels in deep aquifers below the Permian evaporites are not connected to the surface and therefore pressures are not hydrostatic. Connectivity with the surface aquifers is lacking because shale aquitards and impermeable evaporite layers consist of both halite and anhydrite. 5. Developed extensive web applications and an interactive mapping system that do the following: a. Facilitate access to a wide array of data obtained in the study, including core descriptions and analyses, sample logs, digital (LAS) well logs, seismic data, gravity and magnetics maps, structural and stratigraphic maps, inferred fault traces, earthquakes, Class I and II disposal wells, and surface lineaments. b. Provide real-time analysis of the project dataset, including automated integration and viewing of well logs, core, core analyses, brine chemistry, and stratigraphy using the Java Profile app. A cross-section app allows for the display of log data for up to four wells at a time. 6. Integrated interpretations from the project’s interactive web-based mapping system to gain insights to aid in assessing the efficacy of geologic CO 2 storage in Kansas and insights toward understanding recent seismicity to aid in evaluating induced vs. naturally occurring earthquakes. 7. Developed a digital type-log system, including web-based software to modify and refine stratigraphic nomenclature to provide stakeholders a common means for communication about the subsurface. 8. Contracted use of a nuclear magnetic resonance (NMR) log and ran it slowly to capture response and characterize larger pores common for carbonate reservoirs. Used NMR to extend core analyses to apply permeability, relative permeability to CO 2 , and capillary pressure to the major rock types, each uniquely expressed as a reservoir quality index (RQI), present in the Mississippian and Arbuckle rocks. 9. Characterized and evaluated the possible role of microbes in dense brines. Used microbes to compliment H/O stable isotopes to fingerprint brine systems. Used perforation/swabbing to obtain samples from multiple hydrostratigraphic units and confirmed equivalent results using less expensive drill stem tests (DST). 10. Used an integrated approach from whole core, logs, tests, and seismic to verify and quantify properties of vuggy, brecciated, and fractured carbonate intervals. 11. Used complex geocellular static and dynamic models to evaluate regional storage capacity using large parallel processing. 12. Carbonates are complex reservoirs and CO 2 -EOR needs to move to the next generation to increase effectiveness of CO 2 and efficiency and safety of the injection.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Modeling CO 2 Sequestration in Saline Aquifer and Depleted Oil Reservoir To Evaluate Regional CO 2 Sequestration Potential of Ozark Plateau Aquifer System, South-Central Kansas

1. Drilled, cored, and logged three wells to the basement and collecting more than 2,700 ft of conventional core; obtained 20 m i2 of multicomponent 3D seismic imaging and merged and reprocessed more than 125 mi 2 of existing 3D seismic data for use in modeling CO 2 - EOR oil recovery and CO 2 storage in five oil fields in southern Kansas. 2. Determined the technical feasibility of injecting and sequestering CO 2 in a set of four depleted oil reservoirs in the Cutter, Pleasant Prairie South, Eubank, and Shuck fields in southwest Kansas; of concurrently recovering oil from those fields; and of quantifying the volumes of CO2 sequestered and oil recovered during the process. 3. Formed a consortium of six oil operating companies, five of which own and operate the four fields. The consortium became part of the Southwest Kansas CO 2 -EOR Initiative for the purpose of sharing data, knowledge, and interest in understanding the potential for CO 2 -EOR in Kansas. 4. Built a regional well database covering 30,000 mi 2 and containing stratigraphic tops from ~90,000 wells; correlated 30 major stratigraphic horizons; digitized key wells, including wireline logs and sample logs; and analyzed more than 3,000 drill stem tests to establish that fluid levels in deep aquifers below the Permian evaporites are not connected to the surface and therefore pressures are not hydrostatic. Connectivity with the surface aquifers is lacking because shale aquitards and impermeable evaporite layers consist of both halite and anhydrite. 5. Developed extensive web applications and an interactive mapping system that do the following: a. Facilitate access to a wide array of data obtained in the study, including core descriptions and analyses, sample logs, digital (LAS) well logs, seismic data, gravity and magnetics maps, structural and stratigraphic maps, inferred fault traces, earthquakes, Class I and II disposal wells, and surface lineaments. b. Provide real-time analysis of the project dataset, including automated integration and viewing of well logs, core, core analyses, brine chemistry, and stratigraphy using the Java Profile app. A cross-section app allows for the display of log data for up to four wells at a time. 6. Integrated interpretations from the project’s interactive web-based mapping system to gain insights to aid in assessing the efficacy of geologic CO 2 storage in Kansas and insights toward understanding recent seismicity to aid in evaluating induced vs. naturally occurring earthquakes. 7. Developed a digital type-log system, including web-based software to modify and refine stratigraphic nomenclature to provide stakeholders a common means for communication about the subsurface. 8. Contracted use of a nuclear magnetic resonance (NMR) log and ran it slowly to capture response and characterize larger pores common for carbonate reservoirs. Used NMR to extend core analyses to apply permeability, relative permeability to CO 2 , and capillary pressure to the major rock types, each uniquely expressed as a reservoir quality index (RQI), present in the Mississippian and Arbuckle rocks. 9. Characterized and evaluated the possible role of microbes in dense brines. Used microbes to compliment H/O stable isotopes to fingerprint brine systems. Used perforation/swabbing to obtain samples from multiple hydrostratigraphic units and confirmed equivalent results using less expensive drill stem tests (DST). 10. Used an integrated approach from whole core, logs, tests, and seismic to verify and quantify properties of vuggy, brecciated, and fractured carbonate intervals. 11. Used complex geocellular static and dynamic models to evaluate regional storage capacity using large parallel processing. 12. Carbonates are complex reservoirs and CO 2 -EOR needs to move to the next generation to increase effectiveness of CO 2 and efficiency and safety of the injection.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Modeling CO 2 Sequestration in Saline Aquifer and Depleted Oil Reservoir To Evaluate Regional CO 2 Sequestration Potential of Ozark Plateau Aquifer System, South-Central Kansas

1. Drilled, cored, and logged three wells to the basement and collecting more than 2,700 ft of conventional core; obtained 20 m i2 of multicomponent 3D seismic imaging and merged and reprocessed more than 125 mi 2 of existing 3D seismic data for use in modeling CO 2 - EOR oil recovery and CO 2 storage in five oil fields in southern Kansas. 2. Determined the technical feasibility of injecting and sequestering CO 2 in a set of four depleted oil reservoirs in the Cutter, Pleasant Prairie South, Eubank, and Shuck fields in southwest Kansas; of concurrently recovering oil from those fields; and of quantifying the volumes of CO2 sequestered and oil recovered during the process. 3. Formed a consortium of six oil operating companies, five of which own and operate the four fields. The consortium became part of the Southwest Kansas CO 2 -EOR Initiative for the purpose of sharing data, knowledge, and interest in understanding the potential for CO 2 -EOR in Kansas. 4. Built a regional well database covering 30,000 mi 2 and containing stratigraphic tops from ~90,000 wells; correlated 30 major stratigraphic horizons; digitized key wells, including wireline logs and sample logs; and analyzed more than 3,000 drill stem tests to establish that fluid levels in deep aquifers below the Permian evaporites are not connected to the surface and therefore pressures are not hydrostatic. Connectivity with the surface aquifers is lacking because shale aquitards and impermeable evaporite layers consist of both halite and anhydrite. 5. Developed extensive web applications and an interactive mapping system that do the following: a. Facilitate access to a wide array of data obtained in the study, including core descriptions and analyses, sample logs, digital (LAS) well logs, seismic data, gravity and magnetics maps, structural and stratigraphic maps, inferred fault traces, earthquakes, Class I and II disposal wells, and surface lineaments. b. Provide real-time analysis of the project dataset, including automated integration and viewing of well logs, core, core analyses, brine chemistry, and stratigraphy using the Java Profile app. A cross-section app allows for the display of log data for up to four wells at a time. 6. Integrated interpretations from the project’s interactive web-based mapping system to gain insights to aid in assessing the efficacy of geologic CO 2 storage in Kansas and insights toward understanding recent seismicity to aid in evaluating induced vs. naturally occurring earthquakes. 7. Developed a digital type-log system, including web-based software to modify and refine stratigraphic nomenclature to provide stakeholders a common means for communication about the subsurface. 8. Contracted use of a nuclear magnetic resonance (NMR) log and ran it slowly to capture response and characterize larger pores common for carbonate reservoirs. Used NMR to extend core analyses to apply permeability, relative permeability to CO 2 , and capillary pressure to the major rock types, each uniquely expressed as a reservoir quality index (RQI), present in the Mississippian and Arbuckle rocks. 9. Characterized and evaluated the possible role of microbes in dense brines. Used microbes to compliment H/O stable isotopes to fingerprint brine systems. Used perforation/swabbing to obtain samples from multiple hydrostratigraphic units and confirmed equivalent results using less expensive drill stem tests (DST). 10. Used an integrated approach from whole core, logs, tests, and seismic to verify and quantify properties of vuggy, brecciated, and fractured carbonate intervals. 11. Used complex geocellular static and dynamic models to evaluate regional storage capacity using large parallel processing. 12. Carbonates are complex reservoirs and CO 2 -EOR needs to move to the next generation to increase effectiveness of CO 2 and efficiency and safety of the injection.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Modeling CO 2 Sequestration in Saline Aquifer and Depleted Oil Reservoir To Evaluate Regional CO 2 Sequestration Potential of Ozark Plateau Aquifer System, South-Central Kansas

1. Drilled, cored, and logged three wells to the basement and collecting more than 2,700 ft of conventional core; obtained 20 m i2 of multicomponent 3D seismic imaging and merged and reprocessed more than 125 mi 2 of existing 3D seismic data for use in modeling CO 2 - EOR oil recovery and CO 2 storage in five oil fields in southern Kansas. 2. Determined the technical feasibility of injecting and sequestering CO 2 in a set of four depleted oil reservoirs in the Cutter, Pleasant Prairie South, Eubank, and Shuck fields in southwest Kansas; of concurrently recovering oil from those fields; and of quantifying the volumes of CO2 sequestered and oil recovered during the process. 3. Formed a consortium of six oil operating companies, five of which own and operate the four fields. The consortium became part of the Southwest Kansas CO 2 -EOR Initiative for the purpose of sharing data, knowledge, and interest in understanding the potential for CO 2 -EOR in Kansas. 4. Built a regional well database covering 30,000 mi 2 and containing stratigraphic tops from ~90,000 wells; correlated 30 major stratigraphic horizons; digitized key wells, including wireline logs and sample logs; and analyzed more than 3,000 drill stem tests to establish that fluid levels in deep aquifers below the Permian evaporites are not connected to the surface and therefore pressures are not hydrostatic. Connectivity with the surface aquifers is lacking because shale aquitards and impermeable evaporite layers consist of both halite and anhydrite. 5. Developed extensive web applications and an interactive mapping system that do the following: a. Facilitate access to a wide array of data obtained in the study, including core descriptions and analyses, sample logs, digital (LAS) well logs, seismic data, gravity and magnetics maps, structural and stratigraphic maps, inferred fault traces, earthquakes, Class I and II disposal wells, and surface lineaments. b. Provide real-time analysis of the project dataset, including automated integration and viewing of well logs, core, core analyses, brine chemistry, and stratigraphy using the Java Profile app. A cross-section app allows for the display of log data for up to four wells at a time. 6. Integrated interpretations from the project’s interactive web-based mapping system to gain insights to aid in assessing the efficacy of geologic CO 2 storage in Kansas and insights toward understanding recent seismicity to aid in evaluating induced vs. naturally occurring earthquakes. 7. Developed a digital type-log system, including web-based software to modify and refine stratigraphic nomenclature to provide stakeholders a common means for communication about the subsurface. 8. Contracted use of a nuclear magnetic resonance (NMR) log and ran it slowly to capture response and characterize larger pores common for carbonate reservoirs. Used NMR to extend core analyses to apply permeability, relative permeability to CO 2 , and capillary pressure to the major rock types, each uniquely expressed as a reservoir quality index (RQI), present in the Mississippian and Arbuckle rocks. 9. Characterized and evaluated the possible role of microbes in dense brines. Used microbes to compliment H/O stable isotopes to fingerprint brine systems. Used perforation/swabbing to obtain samples from multiple hydrostratigraphic units and confirmed equivalent results using less expensive drill stem tests (DST). 10. Used an integrated approach from whole core, logs, tests, and seismic to verify and quantify properties of vuggy, brecciated, and fractured carbonate intervals. 11. Used complex geocellular static and dynamic models to evaluate regional storage capacity using large parallel processing. 12. Carbonates are complex reservoirs and CO 2 -EOR needs to move to the next generation to increase effectiveness of CO 2 and efficiency and safety of the injection.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory

X-ray micro-computed tomography (X-ray μCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google’s Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Streamlining the Design-to-Build Transition with Build-Optimization Software Tools

Scaling-up capabilities for the design, build, and test of synthetic biology constructs holds great promise for the development of new applications in fuels, chemical production, or cellular-behavior engineering. Construct design is an essential component in this process; however, not every designed DNA sequence can be readily manufactured, even using state-of-the-art DNA synthesis methods. Current biological computer-aided design and manufacture tools (bioCAD/CAM) do not adequately consider the limitations of DNA synthesis technologies when generating their outputs. Designed sequences that violate DNA synthesis constraints may require substantial sequence redesign or lead to price-premiums and temporal delays, which adversely impact the efficiency of the DNA manufacturing process. In this paper, we have developed a suite of build-optimization software tools (BOOST) to streamline the design-build transition in synthetic biology engineering workflows. BOOST incorporates knowledge of DNA synthesis success determinants into the design process to output ready-to-build sequences, preempting the need for sequence redesign. The BOOST web application is available at https://boost.jgi.doe.gov and its Application Program Interfaces (API) enable integration into automated, customized DNA design processes. Finally, the herein presented results highlight the effectiveness of BOOST in reducing DNA synthesis costs and timelines.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States

Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight-forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodological challenges. Here, we address this deficiency by producing a Landsat derived, high resolution (30 m), long-term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary-scale cloud-based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest.

42 ENGINEERING↗

DOE OSTI2022

High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration

Abstract With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for electronic, optoelectronic, and energy storage applications, we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations. Compared to more accurate hybrid functionals, we find that the widely used PBE generalized gradient approximation (GGA) functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character. However, an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3 d transition metal cations. With regards to partial atomic charges, we find that different density functional approximations predict similar charges overall, although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference. Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes. We conclude by using the dataset of computed MOF properties to train machine-learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work, paving the way for future high-throughput screening studies. To encourage exploration and reuse of the theoretical calculations presented in this work, the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project.

36 MATERIALS SCIENCE↗

DOE OSTI2013

PHOTOMETRIC REDSHIFTS FOR QUASARS IN MULTI-BAND SURVEYS

The Multi Layer Perceptron with Quasi Newton Algorithm (MLPQNA) is a machine learning method that can be used to cope with regression and classification problems on complex and massive data sets. In this paper, we give a formal description of the method and present the results of its application to the evaluation of photometric redshifts for quasars. The data set used for the experiment was obtained by merging four different surveys (Sloan Digital Sky Survey, GALEX, UKIDSS, and WISE), thus covering a wide range of wavelengths from the UV to the mid-infrared. The method is able (1) to achieve a very high accuracy, (2) to drastically reduce the number of outliers and catastrophic objects, and (3) to discriminate among parameters (or features) on the basis of their significance, so that the number of features used for training and analysis can be optimized in order to reduce both the computational demands and the effects of degeneracy. The best experiment, which makes use of a selected combination of parameters drawn from the four surveys, leads, in terms of {Delta}z{sub norm} (i.e., (z{sub spec} - z{sub phot})/(1 + z{sub spec})), to an average of {Delta}z{sub norm} = 0.004, a standard deviation of {sigma} = 0.069, and a median absolute deviation, MAD = 0.02, over the whole redshift range (i.e., z{sub spec} {<=} 3.6), defined by the four-survey cross-matched spectroscopic sample. The fraction of catastrophic outliers, i.e., of objects with photo-z deviating more than 2{sigma} from the spectroscopic value, is <3%, leading to {sigma} = 0.035 after their removal, over the same redshift range. The method is made available to the community through the DAMEWARE Web application.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Analyzing Next-Generation Supply Chains Using the Materials Flows through Industry Tool

The Materials Flows through Industry (MFI) tool is a supply chain modeling tool developed at the National Renewable Energy Laboratory (NREL) with funding from the Department of Energy's Advanced Manufacturing Office. MFI was created to identify and analyze opportunities to reduce the energy and carbon intensities of the U.S. industrial sector (Hanes and Carpenter 2017). In this work, we present an overview of the MFI tool's structure and capabilities, as well as the results of using MFI to analyze a novel NREL-developed process to 'upcycle' PET plastic into higher-value composite materials (Rorrer et al. 2019). This process combines recycled PET (rPET) plastic with inputs that can be derived from biomass, including muconic acid, acrylic acid, and ethylene glycol, to chemically break down the plastic back to its monomers in a process called glycolization. Then, repolymerization and the application of fiberglass yields the valuable glass fiber reinforced plastic (GFRP) composite, a performance material used in the manufacture of wind turbine blades, boat hulls, and other applications. This rPET-derived GFRP was found to have superior strength and fiberglass adhesion compared to conventional GFRP formulations. Our findings indicate that this GFRP production process, were it to be widely commercialized beyond its current lab-scale, could potentially reduce the fossil energy intensity of the GFRP supply chain by between 37% and 58% compared to the conventional method of GFRP manufacture from fossil-derived inputs. We also estimate potential supply chain greenhouse gas (GHG) emissions reductions between 30% and 40% from this process. Scaling these GHG offset estimates up to annual GFRP production in the U.S. would be roughly equivalent to removing between 150,000 and 200,000 vehicles from U.S. roads. These ranges of impact estimates represent the range of differences associated with the various economic allocation methods we have assumed for the intensity contributions from the first life of the PET plastic. Following Shen et al. (2010), we derive economic allocation factors from the current price ratios of clear- and green-colored recycled PET plastic to virgin PET resin ('waste-valuation' approach) or assume them to be zero in a more simplistic allocation ('cutoff' approach). Future work involving the MFI modeling tool will also be discussed, including preliminary results comparing the energy intensity of conventional and bio-based nylons manufacturing. Attendees will also be encouraged to conduct MFI supply chain modeling of their own by requesting a user account on the MFI web application, which is freely available to the public at the following address: https://mfitool.nrel.gov.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach

The regression model-based tool is developed for predicting the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. The tool accounts for the single crystal versus polycrystalline nature of the compound, the production method, and properties of the constituent elements in the chemical formula. Here we introduce new descriptive features of crystalline materials relevant for the prediction the Seebeck coefficient. To address off-stoichiometry in materials, the predictive tool is trained on a mix of stoichiometric and nonstoichiometric materials. The tool is implemented into a web application to assist field scientists in the discovery of novel thermoelectric materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Status of genome function annotation in model organisms and crops

Abstract Since the entry into genome‐enabled biology several decades ago, much progress has been made in determining, describing, and disseminating the functions of genes and their products. Yet, this information is still difficult to access for many scientists and for most genomes. To provide easy access and a graphical summary of the status of genome function annotation for model organisms and bioenergy and food crop species, we created a web application ( https://genomeannotation.rheelab.org ) to visualize, search, and download genome annotation data for 28 species. The summary graphics and data tables will be updated semi‐annually, and snapshots will be archived to provide a historical record of the progress of genome function annotation efforts. Clear and simple visualization of up‐to‐date genome function annotation status, including the extent of what is unknown, will help address the grand challenge of elucidating the functions of all genes in organisms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

The MolSSI QCArchive project: An open-source platform to compute, organize, and share quantum chemistry data

The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCArchive) project is an umbrella name that covers both a central server hosted by MolSSI for community data and the Python-based software infrastructure that powers automated computation and storage of quantum chemistry (QC) results. The MolSSI-hosted central server provides the computational molecular sciences community a location to freely access tens of millions of QC computations for machine learning, methodology assessment, force-field fitting, and more through a Python interface. Facile, user-friendly mining of the centrally archived quantum chemical data also can be achieved through web applications found at the website. The software infrastructure can be used as a standalone platform to compute, structure, and distribute hundreds of millions of QC computations for individuals or groups of researchers at any scale. The QCArchiveInfrastructure is open-source (BSD-3C), code repositories can be found at github, and releases can be downloaded via PyPI and Conda. This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Software > Quantum Chemistry Data Science > Computer Algorithms and Programming

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗