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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Real Space Quantum Cluster Formulation for the Typical Medium Theory of Anderson Localization

We develop a real space cluster extension of the typical medium theory (cluster-TMT) to study Anderson localization. By construction, the cluster-TMT approach is formally equivalent to the real space cluster extension of the dynamical mean field theory. Applying the developed method to the 3D Anderson model with a box disorder distribution, we demonstrate that cluster-TMT successfully captures the localization phenomena in all disorder regimes. As a function of the cluster size, our method obtains the correct critical disorder strength for the Anderson localization in 3D, and systematically recovers the re-entrance behavior of the mobility edge. From a general perspective, our developed methodology offers the potential to study Anderson localization at surfaces within quantum embedding theory. This opens the door to studying the interplay between topology and Anderson localization from first principles.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Short- and medium-range orders in Al90Tb10 glass and their relation to the structures of competing crystalline phases

Molecular dynamics simulations using an interatomic potential developed by artificial neural network deep machine learning are performed to study the local structural order in Al90Tb10 metallic glass. We show that more than 80% of the Tb-centered clusters in Al90Tb10 glass have short-range order (SRO) with their 17 first coordination shell atoms stacked in a ‘3661’ or ‘15551’ sequence. Medium-range order (MRO) in Bergman-type packing extended out to the second and third coordination shells is also clearly observed. Analysis of the network formed by the ‘3661’ and ‘15551’ clusters show that ~82% of such SRO units share their faces or vertexes, while only ~6% of neighboring SRO pairs are interpenetrating. Such a network topology is consistent with the Bergman-type MRO around the Tb-centers. Moreover, crystal structure searches using genetic algorithm and the neural network interatomic potential reveal several low-energy metastable crystalline structures in the composition range close to Al 90 Tb 10 . Some of these crystalline structures have the ‘3661’ SRO while others have the ‘15551’ SRO. While the crystalline structures with the ‘3661’ SRO also exhibit the MRO very similar to that observed in the glass, the ones with the ‘15551’ SRO have very different atomic packing in the second and third shells around the Tb centers from that of the Bergman-type MRO observed in the glassy phase.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Instanton Effects in Euclidean vacuum, Real Time Production, and in the Light-front Wave Functions

Nontrivial topological structures of non-Abelian gauge fields were discovered in the 1970s. Instanton solutions, describing vacuum tunneling through topological barriers, have fermionic zero modes which are at the origin of ‘t Hooft effective Lagrangian. In the 1980s, instanton ensembles have been used to explain chiral symmetry breaking. In the 1990s, a large set of numerical simulations were performed deriving Euclidean correlation functions. The special role of scalar diquarks in nucleons and color superconductivity in dense quark matter has been elucidated. In these lectures, we discuss further developments of physics related to gauge topology. We show that the instanton–anti-instanton “streamline” configurations describe “sphaleron transitions” in high-energy collisions, which result in production of hadronic clusters with nontrivial topological/chiral charges. (They are not yet observed, but discussions of dedicated experiments at the LHC and RHIC are ongoing.) Another new direction is instanton effects in hadronic spectroscopy, both in the rest frame and on the light front. We will discuss their role in central and spin-dependent potentials, form factors and antiquark nuclear “sea”. Finally, we summarize the advances in the semiclassical theory of deconfinement, and chiral phase transitions at finite temperature, in QCD, and in some of its “deformed” versions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Dissolution of poorly soluble uranyl phosphate phases in the Metaautunite Subgroup under uranyl peroxide cage cluster forming conditions

Uranyl phosphate minerals are widespread in uranium deposits and usually exhibit very low solubility in aqueous systems. Uranyl phosphates of the autunite group and metaautunite subgroup impact the mobility of uranium in the environment and have inspired groundwater remediation strategies that emphasize their low solubility. The importance of soluble uranium-bearing macro-anions, including nanoscale uranyl peroxide cage clusters, is largely unexplored relative to solubilization of normally low-solubility uranium minerals. Eight synthetic analogs of metaautunite subgroup minerals have been prepared and placed in various alkaline aqueous solutions containing hydrogen peroxide and tetraethylammonium hydroxide. Each uranyl phosphate studied has a topologically identical anionic sheet of uranyl square bipyramids and phosphate tetrahedra combined with various cations (Li + , Na + , K + , Rb + , Cs + , Mg 2+ , Ca 2+ , Ba 2+ ) and water in the interlayer. Uranyl peroxides formed under many of the experimental conditions examined, including solid studtite [(UO 2 )(O 2 )(H 2 O) 2 ](H 2 O) 2 and soluble uranyl peroxide cage clusters containing as many as 28 uranyl ions. Uranyl phosphate solids in contact with solutions in which uranyl peroxide cage clusters formed dissolved extensively or completely. The greatest dissolution of uranyl phosphates occurred in systems that contained cations with larger hydrated radii, Li + and Na + . The details of the uranium speciation in solution depended on the pH and counter cations provided from the interlayers of the uranyl phosphate solids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Flexible silicon photonic architecture for accelerating distributed deep learning

The increasing size and complexity of deep learning (DL) models have led to the wide adoption of distributed training methods in datacenters (DCs) and high-performance computing (HPC) systems. However, communication among distributed computing units (CUs) has emerged as a major bottleneck in the training process. In this study, we propose Flex-SiPAC, a flexible silicon photonic accelerated compute cluster designed to accelerate multi-tenant distributed DL training workloads. Flex-SiPAC takes a co-design approach that combines a silicon photonic hardware platform with a tailored collective algorithm, optimized to leverage the unique physical properties of the architecture. The hardware platform integrates a novel wavelength-reconfigurable transceiver design and a micro-resonator-based wavelength-reconfigurable switch, enabling the system to achieve flexible bandwidth steering in the wavelength domain. The collective algorithm is designed to support reconfigurable topologies, enabling efficient all-reduce communications that are commonly used in DL training. The feasibility of the Flex-SiPAC architecture is demonstrated through two testbed experiments. First, an optical testbed experiment demonstrates the flexible routing of wavelengths by shuffling an array of input wavelengths using a custom-designed spatial-wavelength selective switch. Second, a four-GPU testbed running two DL workloads shows a 23% improvement in job completion time compared to a similarly sized leaf-spine topology. We further evaluate Flex-SiPAC using large-scale simulations, which show that Flex-SiPAC is able to reduce the communication time by 26% to 29% compared to state-of-the-art compute clusters under representative collective operations.

Wu, Zhenguo (ORCID:0000000322847985)↗
DOE OSTI2023

Q-BEEP: Quantum Bayesian Error Mitigation Employing Poisson Modeling over the Hamming Spectrum

Quantum computing technology has grown rapidly in recent years, with new technologies being explored, error rates being reduced, and quantum processor’s qubit capacity growing. However, near-term quantum algorithms are still unable to be induced without compounding consequential levels of noise, leading to non-trivial erroneous results. Quantum Error Correction (in-situ error mitigation) and Quantum Error Mitigation (post-induction error mitigation) are promising fields of research within the quantum algorithm scene, aiming to alleviate quantum errors, increasing the overall fidelity and hence the overall quality of circuit induction. Earlier this year, a pioneering work, namely HAMMER, published in ASPLOS-22 demonstrated the existence of a latent structure regarding post-circuit induction errors when mapping to the Hamming spectrum. However, they intuitively assumed that errors occur in local clusters, and that at higher average Hamming distances this structure falls away. In this work, we show that such a correlation structure is not only local but extends certain non-local clustering patterns which can be precisely described by a Poisson distribution model taking the input circuit, the device run time status (i.e., calibration statistics) and qubit topology into consideration. Using this quantum error characterizing model, we developed an iterative algorithm over the generated Bayesian network state-graph for post-induction error mitigation. Thanks to more precise modeling of the error distribution latent structure and the new iterative method, our Q-Beep approach provides state of the art performance and can boost circuit execution fidelity by up to 234.6% on Bernstein-Vazirani circuits and on average 71.0% on QAOA solution quality, using 16 practical IBMQ quantum processors. For other benchmarks such as those in QASMBench, the fidelity improvement is up to 17.8%. Q-Beep is a light-weight post-processing technique that can be performed offline and remotely, making it a useful tool for quantum vendors to integrate and provide more reliable circuit induction results.

Stein, Samuel A.↗
DOE OSTI2023

Pertechnetate/perrhenate–capped Zr/Hf–Dihydroxide Dimers: Elucidating Zr–TcO 4 Co–Mobility in the Nuclear Fuel Cycle

Spent nuclear fuel contains heavy element fission products that must be separated for effective reprocessing for a safe and sustainable nuclear fuel cycle. 93 Zr and 99 Tc are high-yield fission products that co-transport in liquid-liquid extraction processes. Here we seek atomic-level information of this co-extraction process, as well as fundamental knowledge about Zr IV (and Hf IV ) aqueous speciation in the presence of topology-directing ligands such as pertechnetate (TcO 4 – ) and non-radioactive surrogate perrhenate (ReO 4 – ). In this context, we show that the flat tetrameric oxyhydroxyl-cluster [M IV 4 (OH) 8 (H 2 O) 16 ] 8+ (and related polymers) is dissociated by perrhenate/pertechnetate to yield isostructural dimers, M 2 (OH) 2 (XO 4 – ) 6 (H 2 O) 6 • 3H 2 O (M=Zr/Hf IV ; X=Re/Tc VII ), elucidated by single-crystal X-ray diffraction. We used these model compounds to understand the pervasive 93 Zr- 99 Tc coextraction with further speciation studies in water, nitric acid, and tetrabutylphosphate (TBP) -kerosene; where the latter two media are relevant to nuclear fuel reprocessing. SAXS (small angle X-ray scattering), compositional evaluation, and where experimentally feasible, ESI-MS (electrospray ionization mass spectrometry) showed that perrhenate/pertechnetate influence Zr/Hf IV -speciation in water. Here, in Zr-XO 4 solvent extraction studies to simulate fuel reprocessing, we provide evidence that TcO 4 – enhances extraction of Zr IV , and compositional analysis of the extracted metal-complexes (Zr-ReO 4 study) is consistent with the crystallized Zr IV 2 (OH) 2 (Re VII O 4 – ) 6 (H 2 O) 6 •dimer.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Chromosome compartmentalization alterations in prostate cancer cell lines model disease progression

Prostate cancer aggressiveness and metastatic potential are influenced by gene expression and genomic aberrations, features that can be influenced by the 3D structure of chromosomes inside the nucleus. Using chromosome conformation capture (Hi-C), we conducted a systematic genome architecture comparison on a cohort of cell lines that model prostate cancer progression, from normal epithelium to bone metastasis. We describe spatial compartment identity (A-open versus B-closed) changes with progression in these cell lines and their relation to gene expression changes in both cell lines and patient samples. In particular, 48 gene clusters switch from the B to the A compartment, including androgen receptor, WNT5A, and CDK14. These switches are accompanied by changes in the structure, size, and boundaries of topologically associating domains (TADs). Further, compartment changes in chromosome 21 are exacerbated with progression and may explain, in part, the genesis of the TMPRSS2-ERG translocation. These results suggest that discrete 3D genome structure changes play a deleterious role in prostate cancer progression.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2019

Statistical Hypothesis Testing in Wavelet Analysis: Theoretical Developments and Applications to Indian Rainfall

Statistical hypothesis tests in wavelet analysis are methods that assess the degree to which a wavelet quantity(e.g., power and coherence) exceeds background noise. Commonly, a point-wise approach is adopted in which a wavelet quantity at every point in a wavelet spectrum is individually compared to the critical level of the point-wise test. However, because adjacent wavelet coefficients are correlated and wavelet spectra often contain many wavelet quantities, the point-wise test can produce many false positive results that occur in clusters or patches. To circumvent the point-wise test drawbacks, it is necessary to implement the recently developed area-wise, geometric, cumulative area-wise, and topological significance tests, which are reviewed and developed in this paper. To improve the computational efficiency of the cumulative area-wise test, a simplified version of the testing procedure is created based on the idea that its output is the mean of individual estimates of statistical significance calculated from the geometric test applied at a set of point-wise significance levels. Ideal examples are used to show that the geometric and cumulative area-wise tests are unable to differentiate wavelet spectral features arising from singularity-like structures from those associated with periodicities. A cumulative arc-wise test is therefore developed to strictly test for periodicities by using normalized arc length, which is defined as the number of points composing a cross section of a patch divided by the wavelet scale in question. A previously proposed topological significance test is formalized using persistent homology profiles (PHPs) measuring the number of patches and holes corresponding to the set of all point-wise significance values. Ideal examples show that the PHPs can be used to distinguish time series containing signal components from those that are purely noise. To demonstrate the practical uses of the existing and newly developed statistical methodologies, a first comprehensive wavelet analysis of Indian rainfall is also provided. An R software package has been written by the author to implement the various testing procedures.

Wind speed↗
DOE OSTI2021

Scalar Field Comparison with Topological Descriptors: Properties and Applications for Scientific Visualization

In topological data analysis and visualization, topological descriptors such as persistence diagrams, merge trees, contour trees, Reeb graphs, and Morse–Smale complexes play an essential role in capturing the shape of scalar field data. Herein we present a state–of–the–art report on scalar field comparison using topological descriptors. We provide a taxonomy of existing approaches based on visualization tasks associated with three categories of data: single fields, time–varying fields, and ensembles. These tasks include symmetry detection, periodicity detection, key event/feature detection, feature tracking, clustering, and structure statistics. Our main contributions include the formulation of a set of desirable mathematical and computational properties of comparative measures, and the classification of visualization tasks and applications that are enabled by these measures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Neutrino event selection in the MicroBooNE liquid argon time projection chamber using Wire-Cell 3D imaging, clustering, and charge-light matching

An accurate and efficient event reconstruction is required to realize the full scientific capability of liquid argon time projection chambers (LArTPCs). The current and future neutrino experiments that rely on massive LArTPCs create a need for new ideas and reconstruction approaches. Wire-Cell, proposed in recent years, is a novel tomographic event reconstruction method for LArTPCs. The Wire-Cell 3D imaging approach capitalizes on charge, sparsity, time, and geometry information to reconstruct a topology-agnostic 3D image of the ionization electrons prior to pattern recognition. A second novel method, the many-to-many charge-light matching, then pairs the TPC charge activity to the detected scintillation light signal, thus enabling a powerful rejection of cosmic-ray muons in the MicroBooNE detector. A robust processing of the scintillation light signal and an appropriate clustering of the reconstructed 3D image are fundamental to this technique. In this paper, we describe the principles and algorithms of these techniques and their successful application in the MicroBooNE experiment. A quantitative evaluation of the performance of these techniques is presented. Using these techniques, a 95% efficient pre-selection of neutrino charged-current events is achieved with a 30-fold reduction of non-beam-coincident cosmic-ray muons, and about 80% of the selected neutrino charged-current events are reconstructed with at least 70% completeness and 80% purity.

3D imaging↗
DOE OSTI2025

RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species

Abstract We introduce RWRtoolkit, a multiplex generation, exploration, and statistical package built for R and command-line users. RWRtoolkit enables the efficient exploration of large and highly complex biological networks generated from custom experimental data and/or from publicly available datasets, and is species agnostic. A range of functions can be used to find topological distances between biological entities, determine relationships within sets of interest, search for topological context around sets of interest, and statistically evaluate the strength of relationships within and between sets. The command-line interface is designed for parallelization on high-performance cluster systems, which enables high-throughput analysis such as permutation testing. Several tools in the package have also been made available for use in reproducible workflows via the KBase web application.

Kainer, David (ORCID:0000000172714676)↗
NASA NTRS1993

Quantitative analysis of voids in percolating structures in two-dimensional N-body simulations

We present in this paper a quantitative method for defining void size in large-scale structure based on percolation threshold density. Beginning with two-dimensional gravitational clustering simulations smoothed to the threshold of nonlinearity, we perform percolation analysis to determine the large scale structure. The resulting objective definition of voids has a natural scaling property, is topologically interesting, and can be applied immediately to redshift surveys.

Harrington, Patrick M.↗
DOE OSTI2022

Graph identification of proteins in tomograms ( GRIP‐Tomo )

Abstract In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three‐dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo‐multimeric complexes from 10 topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single‐domain protein native topologies as well as distinct single‐domain proteins from multimeric complexes within noisy volumes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Network Analysis of Academic Medical Center Websites in the United States

Healthcare resources are published annually in repositories such as the AHA Annual Survey Database TM . However, these data repositories are created via manual surveying techniques which are cumbersome in collection and not updated as frequently as website information of the respective hospital systems represented. Also, this resource is not widely available to patients in an easy-to-use format. Network analysis techniques have the potential to create topological maps which serve to aid in pathfinding for patients in their search for healthcare services. This study explores the topological structure of forty United States academic health center websites. Network analysis is utilized to analyze and visualize 48,686 webpages. Several elements of network structure are examined including basic network properties, and centrality measures distributions. The Louvain community detection algorithm is used to examine the extent to which these techniques allow identification of healthcare resources within networks. The results indicate that websites with related healthcare services tend to form observable clusters useful in mapping key resources within a hospital system.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2002

Computing Aerodynamic Performance of a 2D Iced Airfoil: Blocking Topology and Grid Generation

The ice accrued on airfoils can have enormously complicated shapes with multiple protruded horns and feathers. In this paper, several blocking topologies are proposed and evaluated on their ability to produce high-quality structured multi-block grid systems. A transition layer grid is introduced to ensure that jaggedness on the ice-surface geometry do not to propagate into the domain. This is important for grid-generation methods based on hyperbolic PDEs (Partial Differential Equations) and algebraic transfinite interpolation. A 'thick' wrap-around grid is introduced to ensure that grid lines clustered next to solid walls do not propagate as streaks of tightly packed grid lines into the interior of the domain along block boundaries. For ice shapes that are not too complicated, a method is presented for generating high-quality single-block grids. To demonstrate the usefulness of the methods developed, grids and CFD solutions were generated for two iced airfoils: the NLF0414 airfoil with and without the 623-ice shape and the B575/767 airfoil with and without the 145m-ice shape. To validate the computations, the computed lift coefficients as a function of angle of attack were compared with available experimental data. The ice shapes and the blocking topologies were prepared by NASA Glenn's SmaggIce software. The grid systems were generated by using a four-boundary method based on Hermite interpolation with controls on clustering, orthogonality next to walls, and C continuity across block boundaries. The flow was modeled by the ensemble-averaged compressible Navier-Stokes equations, closed by the shear-stress transport turbulence model in which the integration is to the wall. All solutions were generated by using the NPARC WIND code.

Chi, X.↗
DOE OSTI2023

Two excited-state datasets for quantum chemical UV-vis spectra of organic molecules

Abstract We present two open-source datasets that provide time-dependent density-functional tight-binding (TD-DFTB) electronic excitation spectra of organic molecules. These datasets represent predictions of UV-vis absorption spectra performed on optimized geometries of the molecules in their electronic ground state. The GDB-9-Ex dataset contains a subset of 96,766 organic molecules from the original open-source GDB-9 dataset. The ORNL_AISD-Ex dataset consists of 10,502,904 organic molecules that contain between 5 and 71 non-hydrogen atoms. The data reveals the close correlation between the magnitude of the gaps between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), and the excitation energy of the lowest singlet excited state energies quantitatively. The chemical variability of the large number of molecules was examined with a topological fingerprint estimation based on extended-connectivity fingerprints (ECFPs) followed by uniform manifold approximation and projection (UMAP) for dimension reduction. Both datasets were generated using the DFTB+ software on the “Andes” cluster of the Oak Ridge Leadership Computing Facility (OLCF).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Ferromagnetic MnBi 4 Te 7 obtained with low-concentration Sb doping: A promising platform for exploring topological quantum states

The tuning of the magnetic phase, chemical potential, and structure is crucial to observe diverse exotic topological quantum states in Mn Bi 2 Te 4 (Bi 2 Te 3 ) m (m = 0–3). Here we show a ferromagnetic (FM) phase with a chiral crystal structure in Mn (Bi 1–x Sb x ) 4 Te 7 , obtained via tuning the growth conditions and Sb concentration. Unlike previously reported Mn (Bi 1–x Sb x ) 4 Te 7 , which exhibits FM transitions only at high Sb doping levels, our samples show FM transitions (T C = 13.5 K) at 15%–27% doping levels. Furthermore, our single-crystal x-ray-diffraction structure refinements find Sb doping leads to a chiral structure with the space group of P3, contrasted with the centrosymmetric $P\bar{3}m1$ crystal structure of the parent compound MnBi 4 Te 7 . Through angle-resolved photoemission spectroscopy measurements, we also demonstrated that the nontrivial band topology is preserved in the Sb-doped FM samples. Given that the nontrivial band topology of this system remains robust for low Sb doping levels, our success in making FM Mn(Bi 1–x Sb x ) 4 Te 7 with x = 0.15, 0.175, 0.2, and 0.27 paves the way for realizing the predicted topological quantum states, such as the axion insulator and Weyl semimetals. Additionally, we also observed magnetic glassy behavior in both antiferromagnetic MnBi 4 Te 7 and FM Mn (Bi 1–x Sb x )4 Te 7 samples, which we believe originates from cluster spin-glass phases coexisting with long-range antiferromagnetic/FM orders. Further, we have also discussed how the antisite Mn ions impact the interlayer magnetic coupling and how FM interlayer coupling is stabilized in this system.

36 MATERIALS SCIENCE↗