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DOE OSTI2017

EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

Computational epidemiology seeks to develop computational methods to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Recent advances in computing and data sciences have led to the development of innovative modeling environments to support this important goal. The datasets used to drive the dynamic models as well as the data produced by these models presents unique challenges owing to their size, heterogeneity and diversity. These datasets form the basis of effective and easy to use decision support and analytical environments. As a result, it is important to develop scalable data management systems to store, manage and integrate these datasets. In this paper, we develop EpiK —a knowledge base that facilitates the development of decision support and analytical environments to support epidemic science. An important goal is to develop a framework that links the input as well as output datasets to facilitate effective spatio-temporal and social reasoning that is critical in planning and intervention analysis before and during an epidemic. The data management framework links modeling workflow data and its metadata using a controlled vocabulary. The metadata captures information about storage, the mapping between the linked model and the physical layout, and relationships to support services. EpiK is designed to support agent-based modeling and analytics frameworks—aggregate models can be seen as special cases and are thus supported. We use semantic web technologies to create a representation of the datasets that encapsulates both the location and the schema heterogeneity. The choice of RDF as a representation language is motivated by the diversity and growth of the datasets that need to be integrated. A query bank is developed—the queries capture a broad range of questions that can be posed and answered during a typical case study pertaining to disease outbreaks. The queries are constructed using SPARQL Protocol and RDF Query Language (SPARQL) over the EpiK. EpiK can hide schema and location heterogeneity while efficiently supporting queries that span the computational epidemiology modeling pipeline: from model construction to simulation output. As a result, we show that the performance of benchmark queries varies significantly with respect to the choice of hardware underlying the database and resource description framework (RDF) engine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Reservoir Computing as a Tool for Climate Predictability Studies

Reduced-order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate models. In this context, the linear inverse modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that reservoir computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea surface temperature in the North Atlantic in the preindustrial control simulation of a popular earth system model, the Community Earth System Model so that we can compare the performance of the new RC-based approach with the traditional LIM approach both when learning data are plentiful and when such data are more limited. The improved predictive skill of the RC approach over a wide range of conditions—larger number of retained EOF coefficients, extending well into the limited data regime, etc.—suggests that this machine-learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator—the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory—is demonstrated in the Lorenz-63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Biologically inspired approaches for biosurveillance anomaly detection and data fusion

This study developed and tested biologically inspired computational methods to detect anomalous signals in data streams that could indicate a pending outbreak or bio-weapon attack. Current large-scale biosurveillance systems are plagued by two principal deficiencies: (1) timely detection of disease-indicating signals in noisy data and (2) anomaly detection across multiple channels. Anomaly detectors and data fusion components modeled after human immune system processes were tested against a variety of natural and synthetic surveillance datasets. A pilot scale immune-system-based biosurveillance system performed at least as well as traditional statistical anomaly detection data fusion approaches. Machine learning approaches leveraging Deep Learning recurrent neural networks were developed and applied to challenging unstructured and multimodal health surveillance data. Within the limits imposed of data availability, both immune systems and deep learning methods were found to improve anomaly detection and data fusion performance for particularly challenging data subsets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Assessing Mg–Sc–(rare earth) ternary phase stability via constituent binary cluster expansions

The disordered Mg–Sc body-centered cubic (bcc) phase is both lightweight and strong; however, the system is impractical for general industrial use due to the high cost of scandium. Here we propose a computationally efficient metric that assesses ternary rare earth element additions that may stabilize the bcc phase at lower Sc concentrations. We find that the bcc phase is stabilized by the ternary addition of Y or Er, but not by La, Ce, or Nd, and we validate these predictions by experimental production and characterization of Mg–Sc–(Y,Er,Nd) alloys. The results suggest a computationally efficient method to anticipate integration of ternary elements into binary systems using cluster expansions of constituent binaries.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Quantum Griffiths singularities in TiO superconducting thin films with insulating normal states

A superconductor–metal transition (SMT) with an unconventional diverging dynamic critical exponent was recently discovered, and it drew tremendous attention because this signature of a quantum Griffiths singularity (QGS) was thought to be a common characteristic of low-disorder crystalline superconductors. However, because the QGS was observed only in limited materials with metallic normal states, the question of whether the QGS exists in other superconducting systems is still unanswered. In this paper, a superconductor–insulator transition (SIT) is observed in TiO thin films with insulating normal states, which offers a more universal platform for investigating the QGS. A thickness-tuned SIT is obtained when the magnetic field is zero. Importantly, a magnetic field-tuned SIT with a diverging dynamic critical exponent, which is direct evidence of a QGS, is observed in TiO thin films with different thicknesses. By constructing a comprehensive phase diagram, it is demonstrated that the critical magnetic field H c tends to saturate as the temperature approaches 0 K, which is different from the upturn trend of H c observed in SMT systems and probably due to the weaker Josephson coupling of the locally ordered superconducting islands (rare regions) in a weakly insulating normal state background. The results extend the QGS scenario from only SMT systems to SIT systems, and they provide vital evidence that QGSs are common in crystalline superconducting thin films, which has possible applications in quantum-computing devices.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Putting Regional Climate Prediction in Reach

Developing the capability to project how climate change will affect a particular region--any region--on Earth is a high-performance computing problem with enormous economic and health impacts. Techniques to resolve regional-scale features in global climate models are being developed and validated on leadership computing systems. In time, these regional-scale-resolving global climate models will arm local and state decision makers with the information needed to prepare for both short- and long-term impacts of climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Fast nonadiabatic dynamics of many-body quantum systems

Modeling many-body quantum systems with strong interactions is one of the core challenges of modern physics. A range of methods has been developed to approach this task, each with its own idiosyncrasies, approximations, and realm of applicability. However, there remain many problems that are intractable for existing methods. In particular, many approaches face a huge computational barrier when modeling large numbers of coupled electrons and ions at finite temperature. Here, we address this shortfall with a new approach to modeling many-body quantum systems. On the basis of the Bohmian trajectory formalism, our new method treats the full particle dynamics with a considerable increase in computational speed. As a result, we are able to perform large-scale simulations of coupled electron-ion systems without using the adiabatic Born-Oppenheimer approximation.

Science & Technology - Other Topics↗
DOE OSTI2017

Concrete Materials with Ultra-High Damage Resistance and Self- Sensing Capacity for Extended Nuclear Fuel Storage Systems

The objective of this project is to develop a new class of multifunctional concrete materials (MSCs) for extended spent nuclear fuel (SNF) storage systems, which combine ultra-high damage resistance through strain-hardening behavior with distributed multi-dimensional damage self-sensing capacity. The beauty of multifunctional concrete materials is two-fold: First, it serves as a major material component for the SNF pool, dry cask shielding and foundation pad with greatly improved resistance to cracking, reinforcement corrosion, and other common deterioration mechanisms under service conditions, and prevention from fracture failure under extreme events (e.g. impact, earthquake). This will be achieved by designing multiple levels of protection mechanisms into the material (i.e., ultrahigh ductility that provides thousands of times greater fracture energy than concrete and normal fiber reinforced concrete; intrinsic cracking control, electrochemical properties modification, reduced chemical and radionuclide transport properties, and crack-healing properties). Second, it offers capacity for distributed and direct sensing of cracking, strain, and corrosion wherever the material is located. This will be achieved by establishing the changes in electrical properties due to mechanical and electrochemical stimulus. The project will combine nano-, micro- and composite technologies, computational mechanics, durability characterization, and structural health monitoring methods, to realize new MSCs for very long-term (greater than 120 years) SNF storage systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Predicting magnetic properties of single-molecule magnets from self-interaction-free density-functional theory (Final Report)

In this project we investigated electronic structure of an intermediate-sized copper-based molecule and magnetic and hyperfine properties of several small non-magnetic and magnetic molecules including transition-metal elements by applying self-interaction corrections to density functional theory (DFT). In a sufficient number of cases, DFT-calculated electronic structure and magnetic properties of single-molecule magnets qualitatively differ from corresponding experimental data. This is partly due to self-interacting electrons within the DFT formalism. Recently, an efficient method to correct the self-interactions was proposed, i.e., Fermi-Lowdin prbital (FLO) based self-interaction corrected (SIC) methodology, within DFT. Henceforth, this method is referred to as FLO-SIC method which exists in FLOSIC code. We used this FLO-SIC method for our studies of electronic structure and magnetic and hyperfine properties of small magnetic molecules and non-magnetic molecules. Our study will provide insight into predictions of magnetic properties of single-molecule magnets where self-interaction corrections play a critical role. There are two components of this project. In the first work, we studied the electronic structure of a planar mononuclear Cu-based molecule in two oxidation states, using DFT with the FLO-SIC method. We chose this system because it is small enough and it includes a transition metal element. We found that the standard FLO-SIC method takes too much compute time even for the small transition-metal molecule and so we slightly modified the method in order to expedite the process. In the dianionic state, we found that the FLO-SIC spin density agrees quantitatively with accurate quantum chemistry methods, while DFT spin density without self-interaction corrections are severely deviated from the quantum chemistry methods. We also showed that the energy gap between the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) of the dianionic state is larger than that of the monoanionic state. This result is consistent with experimental data. In the second work, we investigated how the interaction between the electron spin and nuclear spin is affected by electron self-interactions within small non-magnetic and magnetic molecules. Such an interaction is called hyperfine interaction. For molecules without significant orbital angular momentum, the hyperfine interaction consists of Fermi contact and dipolar interaction terms. Since the Fermi contact term depends on electron spin density at the nuclear site, it would be highly affected by self-interaction corrections. Therefore, we calculated the hyperfine interaction for the small molecules using DFT with the slightly modified expedited FLO-SIC method which was obtained in the first work, and compared the results to experimental data and DFT calculations without self-interaction corrections. We found significant improvement of the Fermi contact term computed using the FLO-SIC method for small magnetic molecules. Overall, the first and second work provided positive outlook of application of the FLO-SIC method to magnetic molecules and systems including transition-metal elements.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Computational Studies of Thermal Conductivity of Multiphase Ceramics for Inert Matrix Fuel

Nuclear energy is a major contributor to base load electrical power around the world and is predicted to continue to expand in the coming years to meet the increasing global demand for electricity. As new reactors are being constructed to meet these demands, it is imperative to make power plants intrinsically safer and more cost efficient. A major consideration to improving safety and performance is the enhancement of current ceramic nuclear fuels. By using an inert matrix fuel, additional phases can be added to the fuel to increase both the thermal and mechanical properties of the fuel at room and reactor conditions. In this study, multiphase ceramics and the individual single-phase components, will be investigated to understand the effects of multiple phases, various grain sizes, and interfaces on the temperature dependent thermal conductivity. A coupled approach involving both experimental measurements and computational modeling will give insight into the development of future models for understanding the role interfaces play in thermal transport within multiphase systems. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Creating Training Data for Scientific Named Entity Recognition with Minimal Human Effort

Scientific Named Entity Referent Extraction is often more complicated than traditional Named Entity Recognition (NER). For example, in polymer science, chemical structure may be encoded in a variety of nonstandard naming conventions, and authors may refer to polymers with conventional names, commonly used names, labels (in lieu of longer names), synonyms, and acronyms. As a result, accurate scientific NER methods are often based on task-specific rules, which are difficult to develop and maintain, and are not easily generalized to other tasks and fields. Machine learning models require substantial expert-annotated data for training. Here we propose polyNER: a semi-automated system for efficient identification of scientific entities in text. PolyNER applies word embedding models to generate entity-rich corpora for productive expert labeling, and then uses the resulting labeled data to bootstrap a context-based word vector classifier. Evaluation on materials science publications shows that the polyNER approach enables improved precision or recall relative to a state-of-the-art chemical entity extraction system at a dramatically lower cost: it required just two hours of expert time, rather than extensive and expensive rule engineering, to achieve that result. This result highlights the potential for human-computer partnership for constructing domain-specific scientific NER systems.

Crowdsourcing↗
DOE OSTI2020

Self-consistent GW method: O(N) algorithm for polarizability and self energy

In this work, an efficient implementation of the self-consistent GW method in the FlapwMBPT code [1] is presented. It features the evaluation of polarizability and self-energy which scales linearly with respect to the system size. Altogether the computational time scaling was measured to be be-tween linear and quadratic in the applications to silicon supercells with up to 72 atoms. Application to such materials as paracostibite CoSbS, supercells of La 2 CuO 4 (up to 56 atoms) and SmB 6 , illustrate the potential of the approach in computational material science.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Microscopic Behaviors of Tri-n-Butyl Phosphate, n-Dodecane and Their Mixtures at Air/Liquid and Liquid/Liquid Interfaces: An AMBER Polarizable Force Field Study

In solvent extraction processes for recovering metal ions from used nuclear fuel, as well as other industrial applications, a better understanding of the metal complex phase transfer phenomenon would greatly aid ligand design and process optimization. We have approached this challenge by utilizing classical molecular dynamics simulations technique to gain visual appreciation of the vapor/liquid and liquid/liquid interface between tri-n-butyl phosphate (TBP) and n-dodecane with air and water. In this study, we successfully reparameterized polarizable force fields for TBP and n-dodecane that accurately reproduced several of their thermophysical properties such as: density, heat of vaporization, and dipole moment. Our models were able to predict the surface and interfacial tension of different systems when compared to experimental results also performed by us. Through this study, we gained numerous atomistic understanding of the behaviors of TBP and n-dodecane at the interface against air and water, useful in further computational studies of such systems. Lastly, our studies indicate that the initial configuration of a simulation may have a large effect on the final result.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

The quest for solvable multistate Landau-Zener models

Recently, integrability conditions (ICs) in mutistate Landau-Zener (MLZ) theory were proposed. They describe common properties of all known solved systems with linearly time-dependent Hamiltonians. Here we show that ICs enable efficient computer assisted search for new solvable MLZ models that span complexity range from several interacting states to mesoscopic systems with many-body dynamics and combinatorially large phase space. This diversity suggests that nontrivial solvable MLZ models are numerous. Additionally, we refine the formulation of ICs and extend the class of solvable systems to models with points of multiple diabatic level crossing.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Reflections on the Shifting Experiences of Scientific Infrastructure

Infrastructure of all types is fundamental to modern work and life. Computing for scientific work, especially, extends from distributed local research sites, often at the edges of other major systems, outward into globally connected high-performance facilities and infrastructures. This commentary reviews longstanding research on the social characteristics of infrastructure. We reflect on social concerns that affect the ongoing development, use, and maintenance of a wide range of scientific computing and data resources. Reflecting on the social nature of infrastructure is timely for Computing in Science & Engineering readers, given continued emphasis on developing even more expansive platforms for data and artificial intelligence work in science (e.g., the United States’ Genesis Mission). We assert that, regardless of technological advances, the complex nature of scientific research and data will require continued understanding of longstanding and nascent social practices across varied communities. This is fundamentally necessary to build and sustain usable infrastructure or platforms that can productively advance scientific research.

Paine, Drew [Lawrence Berkeley National Laboratory↗
DOE OSTI2014

SU-C-9A-07: Fabrication and Calibration of a Novel High-Sensitivity Collimator for Brain SPECT Imaging

Purpose: We have designed a novel collimator for brain SPECT imaging that yields greatly increased sensitivity near the center of the brain without loss of resolution. The collimator was manufactured and initial evaluation has been completed. Methods: The collimator was time-consuming and challenging to build. Because our desired hole pattern required substantial variations in hole angle, we designed two supporting plates to securely position about 34,000 hexagonal, slightly tapered, 75-mm long steel pins. The holes in the plates were modeled to yield the desired focal length, hole length and septal thickness. Molten lead was poured in between the plates, and all pins were removed after cooling. The sensitivity gain compared to a fan-beam collimator was measured using a point source placed along the central ray at several distances from the collimator face. Visual inspection of the holes was not possible as the collimator was sealed so it could be safely mounted on a SPECT system. Therefore, we prepared a 2D array of 768, ∼48μCi Tc-99m point sources, separated by 1.6 cm. The array was imaged for 10 minutes at 4 shifted locations to reduce sampling distance to 8 mm. Results: The sensitivity of the novel cone-beam collimator varied with distance from the detector face; it was higher than that of the fan-beam collimator by factors ranging from 3 to 176. Examination of the projections of the 4×768 point sources revealed that fewer than 2% of the holes were fully or partially blocked, which indicates that the intensive manual fabrication process was very successful. Conclusion: We have designed and manufactured a novel collimator for brain SPECT imaging. As expected, the sensitivity is much higher than that of a fan-beam collimator. Because of differences between the manufactured collimator and its design, reconstruction of the data will require a measured system function.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-T-27: A Tool for Routine Quality Assurance of Radiotherapy Dose Calculation Software

Purpose: Dose calculation software is thoroughly evaluated when it is commissioned; however, evaluation of periodic software updates is typically limited in scope due to staffing constraints and the need to quickly return the treatment planning system to clinical service. We developed a tool for quickly and comprehensively testing and documenting dose calculation software against measured data. Methods: A tool was developed using MatLab (The MathWorks, Natick, MA) for evaluation of dose calculation algorithms against measured data. Inputs to the tool are measured data, reference DICOM RT PLAN files describing the measurements, and dose calculations in DICOM format. The tool consists of a collection of extensible modules that can perform analysis of point dose, depth dose curves, and profiles using dose difference, distance-to-agreement, and the gamma-index. Each module generates a report subsection that is incorporated into a master template, which is converted to final form in portable document format (PDF). Results: After each change to the treatment planning system, a report can be generated in approximately 90 minutes. The tool has been in use for more than 5 years, spanning 5 versions of the eMC and 4 versions of the AAA. We have detected changes to the algorithms that affected clinical practice once during this period. Conclusion: Our tool provides an efficient method for quality assurance of dose calculation software, providing a complete set of tests for an update. Future work includes the addition of plan level tests, allowing incorporation of, for example, the TG-119 test suite for IMRT, and integration with the treatment planning system via an application programming interface. Integration with the planning system will permit fully-automated testing and reporting at scheduled intervals.

07 ISOTOPE AND RADIATION SOURCES↗