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DOE OSTI2020

Striped Data Analysis Framework

A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the [LDRD Project], we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. The representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. The Striped Data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped Data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. We have been testing this architecture with a 2TB dataset from a CMS dark matter search and plan to expand it to multiple 100 TB or even PB scale. We will present the striped format, Striped Data Server architecture and performance test results.

Gutsche, Oliver↗

DOE OSTI2018

Striped Data Server for Scalable Parallel Data Analysis

A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the [LDRD Project], we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. The representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. The Striped Data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped Data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. As a result, we have been testing this architecture with a 2TB dataset from a CMS dark matter search and plan to expand it to multiple 100 TB or even PB scale. We will present the striped format, Striped Data Server architecture and performance test results.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2026

Poisson-response Tensor-on-Tensor Regression and Applications

We introduce Poisson-response tensor-on-tensor regression (PToTR), a novel regression framework designed to handle tensor responses composed element-wise of random Poisson-distributed counts. Tensors, or multi-dimensional arrays, composed of counts are common data in fields such as inter national relations, social networks, epidemiology, and medical imaging, where events occur across multiple dimensions like time, location, and dyads. PToTR accommodates such tensor responses alongside tensor covariates, providing a versatile tool for multi dimensional data analysis. We propose algorithms for maximum likelihood estimation under a canonical polyadic (CP) structure on the regression coefficient tensor that satisfy the positivity of Poisson parameters and then provide an initial theoretical error analysis for PToTR estimators. We also demonstrate the utility of PToTR through three concrete applications: longitudinal data analysis of the Integrated Crisis Early Warning System database, positron emission tomography (PET) image reconstruction, and change-point detection of communication patterns in longitudinal dyadic data. These applications highlight the versatility of PToTR in addressing complex, structured count data across various domains.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Active Learning for Metamaterial Optimization on HPC and QC Integrated Systems

Active learning algorithms, integrating machine learning, quantum computing and optics simulation in an iterative loop, offer a promising approach to optimizing metamaterials. However, these algorithms can face difficulties in optimizing highly complex structures due to computational limitations. High-performance computing (HPC) and quantum computing (QC) integrated systems can address these issues by enabling parallel computing. In this study, we develop an active learning algorithm working on HPC-QC integrated systems. We evaluate the performance of optimization processes within active learning (i.e., training a machine learning model, problem-solving with quantum computing, and evaluating optical properties through wave-optics simulation) for highly complex metamaterial cases. Our results showcase that utilizing multiple cores on the integrated system can significantly reduce computational time, thereby enhancing the efficiency of optimization processes. Therefore, we expect that leveraging HPC-QC integrated systems helps effectively tackle large-scale optimization challenges in general.

Kim, Seongmin↗

DOE OSTI2024

Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network

Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space. Published by the American Physical Society 2024

Zhang, Hengrui (ORCID:0000000231831654)↗

DOE OSTI2020

A review of the application of 2D isotropic-anisotropic correlation NMR spectroscopy in structural studies of chalcogenide glasses

The application of solid-state high-resolution NMR spectroscopy in the structural investigation of chalcogenide glasses in Ge/As/P/Si-X (X = S, Se,Te) systems has remained challenging even for the spin-1/2 nuclides ( 29 Si, 31 P, 77 Se, 125 Te), owing to their low natural abundance (except for 31 P), slow spin-lattice relaxation rate and large CSA and chemical shift distribution induced line broadening effects. However, most of these deleterious effects can be successfully overcome in two-dimensional (2D) isotropic-anisotropic correlation NMR experiments, especially when performed at high magnetic field and in conjunction with the Car-Purcell-Meiboom-Gill (CPMG) echo train acquisition. Here, we present a short introduction to the basic principles of such experiments and review their applications over the last decade in deciphering various short- and intermediate- range structural characteristics of chalcogenide glasses in S-Se, Se-Te, Ge-Se, As-Se and Si-Se systems as well as in investigating the molecular dynamics in a P-Se supercooled liquid. We anticipate possible future applications of these 2D isotropic-anisotropic correlation NMR experiments, particularly in conjunction with density functional theory-based calculations of NMR chemical shift tensor parameters and additional signal enhancement schemes, in addressing complex structural correlations and distributions in chalcogenide glasses.

2D NMR↗

DOE OSTI2015

Luminescent hybrid lanthanide sulfates and lanthanide sulfonate-carboxylates with 1,10-phenanthroline involving in-situ oxidation of 2-mercaptonbenzoic acid

A series of lanthanide sulfates and lanthanide sulfonate-carboxylates, [Ln{sub 2}(phen){sub 2}(SO{sub 4}){sub 3}(H{sub 2}O){sub 2}]{sub n} (I:Ln=Nd(1a), Sm(1b), Eu(1c), phen=1,10-phenanthroline) and [Ln(phen)(2-SBA)(BZA)]{sub n} (II: Ln=Sm(2a), Eu(2b), Dy(2c), 2-SBA=2-sulfobenzoate, BZA=benzoate) have been hydrothermally synthesized from lanthanide oxide, 2-mercaptonbenzoic acid with phen as auxiliary ligand and characterized by single-crystal X-ray diffraction, elemental analyses, IR spectra, TG analyses and luminescence spectroscopy. Interestingly, SO{sub 4}{sup 2−} anions in I came from the in situ deep oxidation of thiol groups of 2-mercaptonbenzoic acid while 2-sulfobenzoate and benzoate ligands in II from the middle oxidation and desulfuration reactions of 2-mercaptonbenzoic acid. Compounds I are organic–inorganic hybrid lanthanide sulfates, which have rare one-dimensional column-like structures. Complexes II are binuclear lanthanide sulfonate-carboxylates with 2-sulfobenzoate and benzoate as bridges and 1,10-phenanthroline as terminal. Photoluminescence studies reveal that complexes I and II exhibit strong lanthanide characteristic emission bands in the solid state at room temperature. - Graphical abstract: Lanthanide sulfates and lanthanide sulfonate-carboxylates have been hydrothermally synthesized. Interestingly, sulfate anions, 2-sulfobenzoate and benzoate ligands came from the in situ oxidation and desulfuration reactions of 2-mercaptonbenzoic acid. - Highlights: • In situ oxidation and desulfuration reactions of 2-mercaptonbenzoic acid. • The organic–inorganic hybrid lanthanide sulfates with one-dimensional column-like structure. • The dinuclear lanthanide sulfonate-carboxylates. • The emission spectra exhibit the characteristic transition of {sup 5}D{sub 0}→{sup 7}F{sub J} (J=0–4) of the Eu(III)

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Self‐Assembled Metal–Dielectric Hybrid Metamaterials in Vertically Aligned Nanocomposite Form with Tailorable Optical Properties and Coupled Multifunctionalities

Metal–dielectric hybrid metamaterials have attracted increasing research interest in recent years because of their novel optical properties and promising applications in the fields of electronic and photonic devices. Dielectric permittivity is a key parameter that strongly influences the optical properties of materials. By self‐assembling the metallic and dielectric components in a pillar‐in‐matrix structure, a strong anisotropic structure forms and results in opposite signs of permittivity components (i.e., ε || > 0 and ε ⊥ < 0, or vice versa) and exotic optical responses including hyperbolic dispersion in the visible to near‐infrared region. Herein, the main approaches of tuning the permittivity in self‐assembled metal–dielectric vertically aligned nanocomposite (VAN) thin films are reviewed, including tuning the metal pillar density and geometry, film strain state and background pressure during growth, and seeking other metal‐free and complex structure designs. Future research directions are also proposed, including unique approaches to improve their thermal stability, integrate on flexible substrates toward wearable device fabrication, and explore real‐time tunable metamaterials.

Zhang, Di↗

DOE OSTI2020

Reconfigurable structure and tunable transport in synchronized active spinner materials

Ensembles of actuated colloids are excellent model systems to explore emergent out-of-equilibrium structures, complex collective dynamics, and design rules for the next generation materials. Here, we demonstrate that ferromagnetic microparticles suspended at an air-water interface and energized by an external rotating magnetic field spontaneously form dynamic ensembles of synchronized spinners in a certain range of the excitation field parameters. Each spinner generates strong hydrodynamic flows, and collective interactions of the multiple spinners promote a formation of dynamic lattices. On the basis of experiments and simulations, we reveal structural transitions from liquid to nearly crystalline states in this novel active spinner material and demonstrate that dynamic spinner lattices are reconfigurable, capable of self-healing behavior and that the transport of embedded inert cargo particles can be remotely tuned by the parameters of the external excitation field. Our findings provide insights into the behavior of active spinner materials with reconfigurable structural order and tunable functionalities.

36 MATERIALS SCIENCE↗

NASA NTRS1989

A distributed finite-element modeling and control approach for large flexible structures

An unconventional framework is described for the design of decentralized controllers for large flexible structures. In contrast to conventional control system design practice which begins with a model of the open loop plant, the controlled plant is assembled from controlled components in which the modeling phase and the control design phase are integrated at the component level. The developed framework is called controlled component synthesis (CCS) to reflect that it is motivated by the well developed Component Mode Synthesis (CMS) methods which were demonstrated to be effective for solving large complex structural analysis problems for almost three decades. The design philosophy behind CCS is also closely related to that of the subsystem decomposition approach in decentralized control.

Young, K. D.↗

NASA NTRS1998

In search of cellular control: signal transduction in context

The field of molecular cell biology has experienced enormous advances over the last century by reducing the complexity of living cells into simpler molecular components and binding interactions that are amenable to rigorous biochemical analysis. However, as our tools become more powerful, there is a tendency to define mechanisms by what we can measure. The field is currently dominated by efforts to identify the key molecules and sequences that mediate the function of critical receptors, signal transducers, and molecular switches. Unfortunately, these conventional experimental approaches ignore the importance of supramolecular control mechanisms that play a critical role in cellular regulation. Thus, the significance of individual molecular constituents cannot be fully understood when studied in isolation because their function may vary depending on their context within the structural complexity of the living cell. These higher-order regulatory mechanisms are based on the cell's use of a form of solid-state biochemistry in which molecular components that mediate biochemical processing and signal transduction are immobilized on insoluble cytoskeletal scaffolds in the cytoplasm and nucleus. Key to the understanding of this form of cellular regulation is the realization that chemistry is structure and hence, recognition of the the importance of architecture and mechanics for signal integration and biochemical control. Recent work that has unified chemical and mechanical signaling pathways provides a glimpse of how this form of higher-order cellular control may function and where paths may lie in the future.

Non-NASA Center↗

DOE OSTI2018

Metastable silica high pressure polymorphs as structural proxies of deep Earth silicate melts

Modelling of processes involving deep Earth liquids requires information on their structures and compression mechanisms. However, knowledge of the local structures of silicates and silica (SiO 2 ) melts at deep mantle conditions and of their densification mechanisms is still limited. Here we report the synthesis and characterization of metastable high-pressure silica phases, coesite-IV and coesite-V, using in situ single-crystal X-ray diffraction and ab initio simulations. Their crystal structures are drastically different from any previously considered models, but explain well features of pair-distribution functions of highly densified silica glass and molten basalt at high pressure. Built of four, five-, and six-coordinated silicon, coesite-IV and coesite-V contain SiO 6 octahedra, which, at odds with 3 rd Pauling’s rule, are connected through common faces. Our results suggest that possible silicate liquids in Earth’s lower mantle may have complex structures making them more compressible than previously supposed.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Influence of the hyperfine structure of the atomic states on the collective effects in the Rb{sub 2} quasi-molecule

A consistent quantum approach is used to study the influence of intraatomic spin–orbit and hyperfine interactions on the character of a resonance dipole–dipole interatomic interaction and, hence, collective effects. For this purpose, the collective spontaneous decay of excited states and the spectral dependence of the total scattering cross section of a monochromatic light wave are analyzed in the system consisting of two rubidium-87 atoms. The modification of the radiation properties and the interaction of the atoms with external radiation are studied as functions of the interatomic distance. The presence of a complex structure of the sublevels of both the ground and excited states is shown to modify the collective effects substantially as compared to the case when this structure is absent.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1987

Telerobotic research at NASA Langley Research Center

An overview of Automation Technology Branch facilities and research is presented. Manipulator research includes dual-arm coordination studies, space manipulator dynamics, end-effector controller development, automatic space structure assembly, and the development of a dual-arm master-slave telerobotic manipulator system. Sensor research includes gravity-compensated force control, real-time monovision techniques, and laser ranging. Artificial intelligence techniques are being explored for supervisory task control, collision avoidance, and connectionist system architectures. A high-fidelity dynamic simulation of robotic systems, ROBSIM, is being supported and extended. Cooperative efforts with Oak Ridge National Laboratory have verified the ability of teleoperators to perform complex structural assembly tasks, and have resulted in the definition of a new dual-arm master-slave telerobotic manipulator. A bibliography of research results and a list of technical contacts are included.

Sliwa, Nancy E.↗

NASA NTRS2016

Retrievals of Cloud Droplet Size from the RSP Data: Validation Using in Situ Measurements

We present comparisons of cloud droplet size distributions retrieved from the Research Scanning Polarimeter (RSP) data with correlative in situ measurements made during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). This field experiment was based at St. Johns airport, Newfoundland, Canada with the latest deployment in May - June 2016. RSP was onboard the NASA C-130 aircraft together with an array of in situ and other remote sensing instrumentation. The RSP is an along-track scanner measuring polarized and total reflectances in9 spectral channels. Its unique high angular resolution allows for characterization of liquid water droplet size using the rainbow structure observed in the polarized reflectances in the scattering angle range between 135 and 165 degrees. A parametric fitting algorithm applied to the polarized reflectances provides retrievals of the droplet effective radius and variance assuming a prescribed size distribution shape (gamma distribution). In addition to this, we use a non-parametric method, Rainbow Fourier Transform (RFT), which allows us to retrieve the droplet size distribution (DSD) itself. The latter is important in the case of clouds with complex structure, which results in multi-modal DSDs. During NAAMES the aircraft performed a number of flight patterns specifically designed for comparison of remote sensing retrievals and in situ measurements. These patterns consisted of two flight segments above the same straight ground track. One of these segments was flown above clouds allowing for remote sensing measurements, while the other was at the cloud top where cloud droplets were sampled. We compare the DSDs retrieved from the RSP data with in situ measurements made by the Cloud Droplet Probe (CDP). The comparisons show generally good agreement with deviations explainable by the position of the aircraft within cloud and by presence of additional cloud layers in RSP view that do not contribute to the in situ DSDs. In the latter case the distributions retrieved from the RSP data were consistent with the multi-layer cloud structures observed in the correlative High Spectral Resolution Lidar (HSRL) profiles. The comparison results provide a rare validation of polarimetric droplet size retrieval techniques, which can be used for analysis of satellite data on global scale.

cloud droplets↗

DOE OSTI2019

Application of Laser Powder Bed Fusion for Transformational Challenge Reactor Core

The following report focuses on the utility of Laser Powder Bed Fusion (LPBF) for the construction of components for the Transformational Challenge Reactor (TCR) core. Multiple additive manufacturing processes are under investigation to evaluate the advantages and viability of each process for direct manufacturing of complex structures needed for future nuclear reactors. Technologies under investigation include binder jet, direct energy deposition (DED), electron beam powder bed fusion (EBPBF), and laser powder bed fusion (LPBF). Each process has advantages and disadvantages. For the core design, two manufacturing approaches are under consideration—one involving the exclusive use of LPDF to fabricate the entire core and the other consisting of integration of LPBF and DED where LPBF is used for the core’s top and bottom manifolds (where complexity is high) and DED is used for the manufacture of the cladding where complexity is still high but geometric variability, size, and speed are better suited for DED. For both approaches, LPBF research has focused on processing parameter development, core design, and manufacturing tolerances for integration of fuel elements. The following report highlights reactor core design as it relates to LPBF and experimental validation of test components necessary for finalizing the core design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1984

Galileo spacecraft modal identification using an eigensystem realization algorithm

A modal parameter identification technique referred to as the Eigensystem Realization Algorithm (ERA) was applied to free-response measurements from the Galileo spacecraft modal survey test. The data were recorded following single-point random excitation of the structure. This work is one phase in a research project coordinated by the Jet Propulsion Laboratory to compare the performance of various contemporary identification techniques using Galileo data. Principal emphasis is placed on estimating the accuracy of the ERA-identified modal parameters. Various accuracy indicators, such as Modal Amplitude Coherence and Modal Phase Collinearity, are discussed. More than 20 modes of the spacecraft were identified, demonstrating the ability of the ERA method to determine the dynamics of such complex structures using only a few seconds of test data.

Pappa, R. S.↗

NASA NTRS1986

Solar wind observations near the sun

Observations of interplanetary scintillations with the VLA telescope are reported. The solar wind in the accelerating region from 3 to 12 solar radii was observed by scintillation of the radio source 3C279. The results obtained outside of 7 solar radii showed good agreement with previous work but observations between 3 and 4.5 solar radii were new and unexpected. Turbulence in the solar wind has a spatially anisotropic structure elongated in the radial direction, the flow direction being also in the radial direction. An abrupt change of both the velocity and the spatial anisotropy of turbulence was found at distances from 3 to 4.5 solar radii. There is a large random velocity component inside of 12 solar radii which is comparable to the bulk flow speed, and it has a spatially anisotropic probability distribution. From the measured cross-correlation functions, evidence which may be related to the complex structure of the magnetic field is found.

Armstrong, J. W.↗