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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Collective mode Brownian dynamics: A method for fast relaxation of statistical ensembles

Sampling equilibrium configurations of correlated systems of particles with long relaxation times (e.g., polymeric solutions) using conventional molecular dynamics and Monte Carlo methods can be challenging. This is especially true for systems with complicated, extended bond network topologies and other interactions that make the use and design of specialized relaxation protocols infeasible. We introduce a method based on Brownian dynamics simulations that can reduce the computational time it takes to reach equilibrium and draw decorrelated samples. Importantly, the method is completely agnostic to the particle configuration and the specifics of interparticle forces. In particular, we develop a mobility matrix that excites non-local, collective motion of N particles and can be computed efficiently in $\mathcal{O}(N)$ time. Particle motion in this scheme is computed by integrating the overdamped Langevin equation with an Euler–Maruyama scheme, in which Brownian displacements are drawn efficiently using a low-rank representation of the mobility matrix in position and wave space. We demonstrate the efficacy of the method with various examples from the realm of soft condensed matter and release a massively parallel implementation of the code as a plugin for the open-source package HOOMD-blue [J. A. Anderson et al., J. Comput. Phys. 227, 5342 (2008) and J. Glaser et al., Comput. Phys. Commun. 192, 97 (2015)] which runs on graphics processing units

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

MOVES Activity Updates Using Fleet DNA Data: Interim Report

The U.S. Environmental Protection Agency's Motor Vehicle Emissions Simulator (MOVES) is a publicly available tool used by researchers and policy makers to help understand motor vehicle emission sources at national, county, and project levels. However, estimates of heavy-duty activity (MOVES2014), have been identified as areas needing improvement. The start activities in MOVES2014 are calculated using a limited data set, prompting the concern that inventory values are not representative. In addition, MOVES2014 is believed to underestimate heavy-duty activity that is not captured in the current drive cycles used to represent on-network activity. For example, MOVES2014 does not account for work-day idling activity that takes place on off-network roads, such as at a distribution center while the truck is queuing or during loading and unloading. Under the guidance and expertise of the EPA, the National Renewable Energy Laboratory has leveraged its extensive Fleet DNA database of heavy-duty vehicles to reinforce the data set behind the next-generation MOVES model and enhance idle activity and start fractions using six heavy-duty vehicle classes. The data available in Fleet DNA from 420 conventional, diesel powered vehicles provided over 120,000 hours of operation. Start fraction, soak fraction, and idle fraction by hour of the day were derived for each source type, state, and vocation, and results were provided in the form of .CSV files representing MOVES table inputs. This midterm report details these results providing graphical analysis and context for the start, soak, and idle distributions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

High pressure structural behavior of YGa{sub 2}: A combined experimental and theoretical study

High pressure structural stability studies were carried out on YGa{sub 2} (AlB{sub 2} type structure at NTP, space group P6/mmm) up to a pressure of ~35 GPa using both laboratory based rotating anode and synchrotron X-ray sources. An isostructural transition with reduced c/a ratio, was observed at ~6 GPa and above ~17.5 GPa, the compound transformed to orthorhombic structure. Bulk modulus B{sub 0} for the parent and high pressure phases were estimated using Birch–Murnaghan and modified Birch–Murnaghan equation of state. Electronic structure calculations based on projector augmented wave method confirms the experimentally observed two high pressure structural transitions. The calculations also reveal that the ‘Ga’ networks remains as two dimensional in the high pressure isostructural phase, whereas the orthorhombic phase involves three dimensional networks of ‘Ga’ atoms interconnected by strong covalent bonds. - Graphical abstract: High pressure X-ray diffraction patterns of YGa{sub 2} up to ~35 GPa shows an isostructural phase transition at ~5 GPa and transition to an orthorhombic structure ~14 GPa. - Highlights: • High pressure structural stability studies were carried out on YGa{sub 2} up to 35 GPa. • An isostructural transition with reduced c/a ratio was observed above 6 GPa. • Above 17.5 GPa, the compound transformed to orthorhombic structure. • PAW based electronic structure calculations have been carried out. • Calculations confirm the experimentally observed structural transitions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

FY24 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

Algorithms for Machine Learning (ML) and data analysis for the 3013 Surveillance Program have been developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). The objective of the algorithms is to automate the identification of corrosion and crack formation in the Inner Container Closure Weld Region (ICCWR) of the canister system used to store Pu-bearing material. Data for corrosion and cracking is collected from large binary files generated by a Laser Confocal Microscope (LCM), the Wide Area 3D Measurement System (WAMS), or,in a recent proposal, by a Scanning Electron Microscope (SEM). The ML software uses the physical attributes in the data files (e.g., one or more of: height, color, and 16-bit grayscale values as functions of position in a plane projection) to detect signs of surface corrosion and cracking after being trained on similar data, with the features to be detected. Although the initial scope included screening for broader indicators of corrosion, e.g., pitting, identification of potential cracks was prioritized for the past several years at the request of program leadership. Labeled training data is essential to developing the ML algorithm, and enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the large volume of data required to train ML algorithms and the relative rarity of cracks in the ICCWR data set. The updated program will read binary data from either LCM, WAMS or SEM files, interrogate data attributes, facilitate user labeling of data for training ML algorithms, execute ML algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. In FY24, hourglass neural networks (HNNs) that were initiated in FY22 were further developed and tested using available LCM data, and their performance was tested against that of the alternative U-Net Neural Network algorithm structure. HNNs along with previously developed Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) comprise a suite of ML tools for identification of cracks in the ICCWR

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

Synthesis, characterization and crystal structure of a novel Strandberg-type polyoxoselenomolybdate Rb{sub 4}[Se{sub 2}Mo{sub 5}O{sub 21}]{center_dot}2H{sub 2}O

Graphical abstract: Crystal structure of Rb{sub 4}[Se{sub 2}Mo{sub 5}O{sub 21}]{center_dot}2H2O. Highlights: Black-Right-Pointing-Pointer Synthesis of the first Strandberg-type polyoxoselenomolybdate Rb{sub 4}[Se{sub 2}Mo{sub 5}O{sub 21}]{center_dot}2H{sub 2}O. Black-Right-Pointing-Pointer Characterization of the novel compound. Black-Right-Pointing-Pointer Chirality of the space group C2 of compound implies that it is expected to have nonlinear optical (NLO) properties -- Abstract: An inorganic compound formulated as Rb{sub 4}[Se{sub 2}Mo{sub 5}O{sub 21}]{center_dot}2H{sub 2}O (1) has been isolated by conventional solution method and structurally characterized by single-crystal X-ray diffraction methods, scanning electron microscopy (SEM), powder XRD, IR, UV-vis spectra, and cyclic voltammetry measurements. This compound crystallizes in the monoclinic system, space group C2 with unit a = 19.701 (3) Angstrom-Sign , b = 10.296 (2) Angstrom-Sign , c = 12.134 (4) Angstrom-Sign , {beta} = 106.96 (2) Degree-Sign and Z = 4. The crystal structure of (1) is built up from a Strandberg clusters connected through hydrogen-bonding interactions into a three-dimensional supramolecular network.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Copper(II)-lanthanide(III) coordination polymers constructed from pyridine-2,5-dicarboxylic acid: Preparation, crystal structure and photoluminescence

A series of 3d-4f heterometallic coordination polymers, formulated as {l_brace} [Cu{sub 3}Ln{sub 2}(pydc){sub 6}(H{sub 2}O){sub 12}]{center_dot}4H{sub 2}O{r_brace} {sub n} [Ln=Tb (1), Eu (2), Dy (3), Ho (4), Lu (5)], {l_brace} [CuNd{sub 2}(pydc){sub 4}(H{sub 2}O){sub 3}]{center_dot}H{sub 2}O{r_brace} {sub n} (6) and {l_brace} [Cu{sub 3}Pr{sub 2}(pydc){sub 6}(H{sub 2}O){sub 13}]{center_dot}4H{sub 2}O{r_brace} {sub n} (7) (where H{sub 2}pydc=pyridine-2,5-dicarboxylic acid), have been hydrothermally prepared by reactions of H{sub 2}pydc ligand with lanthanide ions in the presence of Cu(II) ion. X-ray crystal structure analysis reveals that these compounds exhibit rich structural chemistry. 1-5 are isomorphous and present a two-dimensional network constructed from Ln{sub 2}Cu{sub 2}L{sub 2}(H{sub 2}O){sub 2} SBU rings and CuL{sub 2}(H{sub 2}O) building blocks. In 6, two-dimensional ladder-like layers based on Nd(III) belts and CuL{sub 2}O{sub 2} units are assembled by H{sub 2}pydc ligands into a three-dimensional open framework. Polymer 7 displays a two-dimensional wave-like layer structure containing two distinct ring units, in which a new coordination mode of the pydc{sup 2-} ligand is observed. The results indicate that the coordination flexibility of the pydc{sup 2-} ligand and lanthanide contraction effect play cooperative roles in the formation of coordination polymers with different polymeric architectures. Compounds 1-2 exhibit intense green and red luminescence emission characteristics of Tb(III) and Eu(III), respectively. Furthermore, elemental analyses (EA), infrared spectra (IR) and thermogravimetric analyses (TGA) of these compounds were also studied. - Graphical abstract: Seven 3d-4f heterometallic coordination polymers were synthesized by reactions of H{sub 2}pydc with lanthanide metal ions in the presence of Cu{sup 2+}, the effects of Cu{sup 2+} on the structures and photoluminescent properties of Ln-pydc{sup 2-} systems were investigated. Highlights: Black-Right-Pointing-Pointer Seven 3d-4f heterometallic compounds have been prepared and characterized. Black-Right-Pointing-Pointer 1-5 and 7 are constructed from Ln{sub 2}Cu{sub 2}L{sub 2}(H{sub 2}O){sub 2} rings and CuL{sub 2}(H{sub 2}O) building blocks. Black-Right-Pointing-Pointer One new coordination mode of the pydc{sup 2-} ligand is first observed. Black-Right-Pointing-Pointer 1-2 exhibit intense characteristic luminescence emission spectra of lanthanide ions.

36 MATERIALS SCIENCE↗
DOE OSTI2015

One-pot synthesis of copper-doped graphitic carbon nitride nanosheet by heating Cu–melamine supramolecular network and its enhanced visible-light-driven photocatalysis

Here we report a novel synthetic pathway for preparation of Cu-doped g-C{sub 3}N{sub 4} (Cu-g-C{sub 3}N{sub 4}) with nanosheet morphology by using a two dimensional Cu–melamine supramolecular network as both sacrificial template and precursor. The specific surface area of Cu-g-C{sub 3}N{sub 4} is 40.86 m{sup 2} g{sup −1}, which is more than 7 times larger than that of pure g-C{sub 3}N{sub 4}. Cu-g-C{sub 3}N{sub 4} showed strong optical absorption in the visible-light region and expanded the absorption to the near-infrared region. The uniform nanosheet morphology, higher surface area and strong visible-light absorption have enabled Cu-g-C{sub 3}N{sub 4} exhibiting enhanced visible light photocatalytic activity for the photo-degradation of methylene blue (MB). The results indicate that metal–melamine supramolecular network can be promising precursors for the one step preparation of efficient metal-doped g-C{sub 3}N{sub 4} photocatalysts. - Graphical abstract: Cu-doped g-C{sub 3}N{sub 4} (Cu-g-C{sub 3}N{sub 4}) with nanosheet morphology was fabricated via a simple one step preparation by using a two dimensional Cu–melamine supra-molecular network as both sacrificial template and precursor. - Highlights: • Cu-doped g-C{sub 3}N{sub 4} (Cu-g-C{sub 3}N{sub 4}) with nanosheet morphology was prepared. • Cu-g-C{sub 3}N{sub 4} showed strong optical absorption in the visible-light region. • Cu-g-C{sub 3}N{sub 4} exhibits enhanced visible light photocatalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

The cluster compound In{sub 4}Ti{sub 1.5}Mo{sub 0.5}Mo{sub 14}O{sub 26} containing Mo{sub 14} clusters and the new mono- and bi-capped trioctahedral Mo{sub 15} and Mo{sub 16} clusters: Synthesis, crystal structure, and electrical and magnetic properties

Single crystals of the new quaternary compound In{sub 4}Ti{sub 1.5}Mo{sub 0.5}Mo{sub 14}O{sub 26} were obtained by solid state reaction. The crystal structure was determined by single-crystal X-ray diffraction. In{sub 4}Ti{sub 1.5}Mo{sub 0.5}Mo{sub 14}O{sub 26} crystallizes in the orthorhombic space group Pbca with unit-cell parameters a=9.4432(14) Å, b=11.4828(12) Å, c=20.299(4) Å and Z=4. Full-matrix least-squares refinement on F{sup 2} using 3807 independent reflections for 219 refinable parameters resulted in R{sub 1}=0.0259 and wR{sub 2}=0.0591. The crystal structure contains in addition to Mo{sub 14} clusters the first examples of mono- and bi-capped trioctahedral Mo{sub 14} i.e. Mo{sub 15} and Mo{sub 16} clusters. The oxygen framework derives from a stacking along the a direction of close-packed layers with sequence (…ABAC…). The Mo–Mo distances range between 2.6938(5) and 2.8420(6) Å and the Mo–O distances between 1.879(5) and 2.250(3) Å, as usually observed in molybdenum oxide clusters. The indium atoms form In{sub 4}{sup 6+} bent chains with In–In distances of 2.6682(5) and 2.6622(8) Å and the Ti atoms are in highly distorted octahedral sites of oxygen atoms with Ti–O distances ranging between 1.865(4) and 2.161(4) Å. Magnetic susceptibility measurements confirm the presence of Ti{sup 4+} cations and the absence of localized moments on the Mo network. Electrical resistivity measurements on a single crystal of In{sub 4}Ti{sub 1.5}Mo{sub 0.5}Mo{sub 14}O{sub 26} show a semimetallic behavior. - Graphical abstract: We present here the synthesis, the crystal structure, and the electrical and magnetic properties of the new compound In{sub 4}Ti{sub 1.5}Mo{sub 0.5}Mo{sub 14}O{sub 26} in which Mo{sub 14} clusters coexist statistically with mono- and bi-capped trioctahedral Mo{sub 14} that is Mo{sub 15} and Mo{sub 16} clusters. - Highlights: • Single crystals of In{sub 4}Ti{sub 1.5}Mo{sub 0.5}Mo{sub 14}O{sub 26} were obtained by solid state reaction. • The crystal structure contains Mo{sub 14}, Mo{sub 15} and Mo{sub 16} clusters. • The indium atoms form In{sub 4}{sup 6+} bent chains. • Poorly metallic behavior. • Absence of localized moments on the Mo network as well as on the Ti atoms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

GPU Lifetimes on Titan Supercomputer: Survival Analysis and Reliability

George Ostrouchov, Don Maxwell, Rizwan Ashraf, Mallikarjun Shankar, and James Rogers. 2020. GPU Lifetimes on Titan Supercomputer: Survival Analysis and Reliability. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '20). Association for Computing Machinery, New York, NY, USA. Data and code for SC20 paper about Titan GPU reliability analysis: https://github.com/olcf/TitanGPULife. Includes R code to generate graphics for paper and additional analyses. See code/README for instructions. Includes original Titan GPU reliability data on over 100,000 collective hours of operation: data/titan.gpu.history.txt - history data, data/titan.service.txt - service nodes for exclusion. Includes output data files produced by code/TitanGPUmodel.Rmd: data/gc_full.csv - cleaned up data (see paper and R code); data/gc_summary_loc.csv - one record per GPU (variables: SN, time, nlife, nloc, last, col, row, cage, slot, node, max_loc_events, time_max_loc, dbe, dbe_loc, otb, otb_loc, out, batch, days, years, dead, dead_otb, dead_dbe) (see paper and R code). Includes .Rmd analysis document as TitanGPUmode.html. Includes Python code to process data/gc_full.csv into graphics from time-between-failure analyses: See code/tbf-analyses/README for instructions.

42 ENGINEERING↗
DOE OSTI2017

Order priors for Bayesian network discovery with an application to malware phylogeny

Here, Bayesian networks have been used extensively to model and discover dependency relationships among sets of random variables. We learn Bayesian network structure with a combination of human knowledge about the partial ordering of variables and statistical inference of conditional dependencies from observed data. Our approach leverages complementary information from human knowledge and inference from observed data to produce networks that reflect human beliefs about the system as well as to fit the observed data. Applying prior beliefs about partial orderings of variables is an approach distinctly different from existing methods that incorporate prior beliefs about direct dependencies (or edges) in a Bayesian network. We provide an efficient implementation of the partial-order prior in a Bayesian structure discovery learning algorithm, as well as an edge prior, showing that both priors meet the local modularity requirement necessary for an efficient Bayesian discovery algorithm. In benchmark studies, the partial-order prior improves the accuracy of Bayesian network structure learning as well as the edge prior, even though order priors are more general. Our primary motivation is in characterizing the evolution of families of malware to aid cyber security analysts. For the problem of malware phylogeny discovery, we find that our algorithm, compared to existing malware phylogeny algorithms, more accurately discovers true dependencies that are missed by other algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

In situ ligand generation for novel Mn(II) and Ni(II) coordination polymers with disulfide ligand: Solvothermal syntheses, structures and magnetic properties

Two coordination polymers, ([Mn{sub 2}(L1){sub 2}(μ{sub 2}-H{sub 2}O)(H{sub 2}O){sub 4}]·5H{sub 2}O){sub n}1 and ([Ni(L1)(H{sub 2}O){sub 2}]·2H{sub 2}O){sub n}2 (H{sub 2}L1=2,2′-dithiobisnicotinic acid), were prepared by the solvothermal reactions of the Mn(II) or Ni(II) ions with 2-mercaptonanicotinic acid. In 1, the [Mn{sub 2}(COO){sub 4}] units are connected by the 2,2′-dithiobisnicotinic dianion to form a two-dimensional (4,4)-connected network. In 2, the adjacent Ni(II) ions are connected by the carboxyl groups of the 2,2′-dithiobisnicotinic dianion to form an one-dimensional inorganic rod-shaped chain [Ni(COO){sub 2}]{sub n}, which are further interconnected by the 2,2′-dithiobisnicotinic ligand, giving rise to a two-dimensional framework. Variable-temperature magnetic susceptibilities of 1 and 2 exhibit overall weak antiferromagnetic coupling between the adjacent metal ions. - Graphical abstract: Two 2D coordination polymers were synthesized by transition-metal/in-situ oxidation of 2-mercaptonicotinic acid. The compounds pack into 2D frameworks by the carboxyl groups of 2,2′-dithiobisnicotinic dianion and exhibit overall weak antiferromagnetic coupling. - Highlights: • Two 2D coordination polymers containing 2,2′-dithiobisnicotinic dianion. • In situ oxidation and dehydro coupling reaction of 2-mercaptonbenzoic acid. • Two compounds display weak antiferromagnetic exchanges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Al{sub 15}Ge{sub 4}Ni{sub 3}: A new intergrowth structure with Cu{sub 3}Au- and CaF{sub 2}-type building blocks

The new ternary compound Al{sub 15}Ge{sub 4}Ni{sub 3} (τ{sub 2} in the system Al–Ge–Ni) was synthesized in single crystalline form by a special annealing procedure from samples located in the three phase fields [L+Al+τ{sub 2}] and [L+Ge+τ{sub 2}]. The crystal structure of Al{sub 15}Ge{sub 4}Ni{sub 3} was determined by single-crystal X-ray diffraction. The compound crystallizes in a new structure type in space group I4-bar3m, Pearson Symbol cI88, cubic lattice parameter a=11.405(1) Å. Phase diagram investigations indicate stoichiometric composition without considerable homogeneity range; τ{sub 2} melts peritectically at T=444 °C. The crystal structure of Al{sub 15}Ge{sub 4}Ni{sub 3} shows a unique combination of simple Cu{sub 3}Au- and CaF{sub 2}-type building blocks: a three dimensional network of CaF{sub 2}-type units, formed by Ni and Al atoms, is interspaced by clusters (Al{sub 6}Ge{sub 8}) resembling unit cells of the Cu{sub 3}Au-type. Both structural motifs are connected by Al–Ge bonds. The ground state energy of the compound was obtained by DFT calculations and the densities of states were analyzed in detail. In addition, electron density maps were calculated in four different sections through the unit cell using the full potential linearized augmented plane-wave (FLAPW) method. The bonding situation in Al{sub 15}Ge{sub 4}Ni{sub 3} was discussed combining results from electronic calculations with the analysis of the coordination of atoms. - Graphical abstract: The new compound Al{sub 15}Ge{sub 4}Ni{sub 3} shows a unique combination of simple Cu{sub 3}Au- and CaF{sub 2}-type building blocks. - Highlights: • The crystal structure of Al{sub 15}Ge{sub 4}Ni{sub 3} (space group I4-bar3m) was determined. • It shows a unique combination of CaF{sub 2}- and Cu{sub 3}Au-type building blocks. • Electronic (DFT) calculations were performed to gain insight to chemical bonding.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Na{sub 8}Au{sub 9.8(4)}Ga{sub 7.2} and Na{sub 17}Au{sub 5.87(2)}Ga{sub 46.63}: The diversity of pseudo 5-fold symmetries in the Na–Au–Ga system

The Na-rich part (∼30% Na) of the Na–Au–Ga system between NaAu{sub 2}, NaGa{sub 4}, and Na{sub 22}Ga{sub 39} has been found to contain the ternary phases Na{sub 8}Au{sub 9.8(4)}Ga{sub 7.2} (I) and Na{sub 17}Au{sub 5.87(2)}Ga{sub 46.63} (II), according to the results of single crystal X-ray diffraction measurements. I is orthorhombic, Cmcm, a=5.3040(1), b=24.519(5), c=14.573(3) Å, and contains a network of clusters with local 5-fold symmetry along the a-axis. Such clusters are frequent building units in decagonal quasicrystals and their approximants. II is rhombohedral, R3{sup ¯}m, a=16.325(2), c=35.242(7) Å, and contains building blocks that are structurally identical to the Bergman-type clusters as well as fused icosahedral units known with active metals, triels and late transition elements. II also contains a polycationic network with elements of the clathrate V type structure. Tight-binding electronic structure calculations using linear muffin–tin-orbital (LMTO) methods on idealized models of I and II indicate that both compounds are metallic with evident pseudogaps at the corresponding Fermi levels. The overall Hamilton bond populations are generally dominated by Au–Ga and Au–Au bonds in I and by Ga–Ga bonds in II; moreover, the Na–Au and Na–Ga contributions in I are unexpectedly large, ∼20% of the total. A similar involvement of sodium in covalent bonding has also been found in the electron-richer i-Na{sub 13}Au{sub 12}Ga{sub 15} quasicrystal approximant. - Graphical abstract: Multiply-endohedral Bergman-related clusters in the structure of Na{sub 17}Au{sub 5.9(1)}Ga{sub 46.6.} Display Omitted - Highlights: • Two new compounds with the local 5-fold symmetry have been investigated. • Na{sub 8}Au{sub 9.8(4)}Ga{sub 7.2} is an orthorhombic approximant of the Na{sub 13}Au{sub 12}Ga{sub 15} quasicrystal. • Na{sub 17}Au{sub 5.87(2)}Ga{sub 46.63} represents a rhombohedral distortion of the Bergman-type phases.

36 MATERIALS SCIENCE↗
DOE OSTI2021

FY21 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

The development of algorithms for machine learning and data analysis for the 3013 Surveillance Program is a collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). For corrosion detection, Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS) data is extracted from large binary files, with software written to convert the data to physical attributes (e.g., height, color and grayscale values; all as functions of a location in a plane projection). A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data, flag significant features, execute Machine Learning (ML) algorithms, output parameters for trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Features can be called out by user-specified thresholds, manual labeling or machine learning algorithms when they have been completed. The ability to rapidly label data is important because of the volume of data required for training machine learning algorithms. The GUI has the flexibility to allow addition of improved ML algorithms, methods for data visualization, and statistical computations. Statistical analyses via the GUI include areas of pits within a defined range of pit depths, correlations between Red-Green-Blue (RGB) or grayscale intensity and relative surface height, covariances between values associated with features, and feature histograms. The development of supervised machine learning algorithms, however, has been hindered by a lack of training data. The machine learning algorithms for crack identification are being refined but require improvements to the true positive rate for crack detection. This shortcoming is an artifact of the limited training data currently available, perhaps more so than the structure of the neural networks. At present, the best results are had from a consensus over an ensemble of randomly generated Deep Neural Network (DNN) or Convolutional Neural Network (CNN) algorithms. Although the consensus accuracy method has yielded optimum true positive and true negative rates in excess of 80%, additional validation testing is necessary. In addition to the suite of LCM data that was initially used, and which represents the majority of the work presented in this report, WAMS image data was also reviewed at a preliminary level. The review included a comparison between image resolution and dynamic range for each method. WAMS (ZON file) image data was found to have a pixel pitch of 3.69μm compared to 1 μm for the LCM (vk4 file) data, which implies a lower resolution for the WAMS images. Conversely, the ratio of dynamic range of the WAMS data to the LCM data was approximately 41:20 for height data, suggesting that information from WAMS should more accurately determine the depth of pits. At present, the significance of the greater dynamic range of the WAMS data relative to the LCM data has not yet been evaluated.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2012

The structural influence of aluminium ions on emission characteristics of Sm{sup 3+} ions in lead aluminium silicate glass system

Graphical abstract: The optical absorption and photoluminescence spectra of Sm ions in PbO-Al{sub 2}O{sub 3}-SiO{sub 2} glasses mixed with different concentrations of Al{sub 2}O{sub 3} have been investigated. From these spectra, the emission probabilities and also fluorescence lifetime principal transition viz., {sup 4}G{sub 5/2} {yields} {sup 6}H{sub 7/2} of Sm{sup 3+} ions of has been evaluated. The analysis of results of these studies has indicated that there is a less radiative trapping and enhanced fluorescence lifetime and high quantum efficiency in the glasses mixed with 8.0 mol% of Al{sub 2}O{sub 3}. Highlights: Black-Right-Pointing-Pointer Glasses of the composition (40 - x)PbO-(5 + x)Al{sub 2}O{sub 3}-54SiO{sub 2}:1.0Sm{sub 2}O{sub 3} with x = 5-10 mol% were prepared. Black-Right-Pointing-Pointer Spectroscopic properties (OA, PL and IR) were investigated. Black-Right-Pointing-Pointer Emission probability, lifetime, branching ratio of {sup 4}G{sub 5/2} {yields} {sup 6}H{sub 7/2} transition of Sm{sup 3+} have been evaluated. Black-Right-Pointing-Pointer Analysis of the results indicated that glass mixed with 8.0 mol% exhibits high luminescence efficiency. -- Abstract: Optical absorption and photoluminescence characteristics of Sm{sup 3+} ions in lead silicate glasses mixed with different concentrations of Al{sub 2}O{sub 3} (5-10 mol%) have been investigated. From these studies, the radiative properties viz., spontaneous emission probability A, the total emission probability, the radiative lifetime {tau}{sub R}, the fluorescent branching ratio {beta} of emission transition of {sup 4}G{sub 5/2} {yields} {sup 6}H{sub 7/2} along with other transitions for Sm{sup 3+} have been evaluated and found to be the highest for the glass mixed with 8.0 mol% of Al{sub 2}O{sub 3}.The IR spectral studies have indicated that Al{sup 3+} ions do participate in the glass network with AlO{sub 4} and AlO{sub 6} structural units and further revealed that the concentration of octahedral aluminium ions induce bonding defects in the glass network. Such bonding defects are assumed to be responsible for low phonon losses in these glasses and lead to higher values of radiative parameters for the glass mixed with 8.0 mol% of Al{sub 2}O{sub 3}.

36 MATERIALS SCIENCE↗
DOE OSTI2014

One-pot occurrence of two polymorphs of Rb{sub 2}Sc[Si{sub 4}O{sub 10}]F and their structural, spectroscopic and computational characterization

Single-crystal growth experiments in the system RbF–Sc{sub 2}O{sub 3}–SiO{sub 2} resulted in the crystallization of two forms of Rb{sub 2}Sc[Si{sub 4}O{sub 10}]F within the same run. Basic crystallographic data of the two concomitant polymorphs at ambient conditions are as follows: phase I: space group I4/m, a=11.2619(3) Å, c=8.3053(4) Å, V=1053.36(6) Å{sup 3}, Z=4; phase II: space group P2{sub 1}/m, a=11.5085(6) Å, b=8.3805(3) Å, c=11.6750(6) Å, β=111.147(6)°, V=1050.19(9) Å{sup 3}, Z=4. The structures were determined by direct methods and refined to residuals of R(|F|)=0.0231 (phase I) for 516 and 0.0249 (phase II) for 2050 independent observed reflections with I>2σ(I). Phase I showed twinning by merohedry which was accounted for during the refinement process. Both compounds are based on multiple chains of [SiO{sub 4}]-tetrahedra forming closed columns or tubes. The multiplicity of the unbranched fundamental chains is two (for phase I) and four (for phase II), respectively. The periodicity of the multiple chains has a value of four in both modifications which is also reflected in similar translation periods along the chain directions (phase I: t{sub [001]}=8.31 Å; phase II: t{sub [010]}=8.38 Å). Alternatively, both polymorphs can be described as mixed octahedral–tetrahedral frameworks, which can be classified according to their polyhedral microensembles. A topological analysis of both networks is presented. Structural investigations have been supplemented by micro-Raman spectroscopy. The interpretation of the spectroscopic data including the allocation of the bands to certain vibrational species has been aided by DFT-calculations. - Graphical abstract: Mixed tetrahedral–octahedral framework of the tetragonal polymorph of Rb{sub 2}Sc[Si{sub 4}O{sub 10}]F. - Highlights: • We report the crystal structures of two polymorphs of Rb{sub 2}ScSi{sub 4}O{sub 10}F. • Both phases crystallized concomitantly in the same flux-growth experiment. • The mixed tetrahedral–octahedral nets are compared using a topological approach. • Interpretation of the Raman spectra has been aided by DFT-calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

DeePMD-kit v2: A software package for deep potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

SU-D-206-02: Evaluation of Partial Storage of the System Matrix for Cone Beam Computed Tomography Using a GPU Platform

Purpose: Iterative reconstruction algorithms in computed tomography (CT) require a fast method for computing the intersections between the photons’ trajectories and the object, also called ray-tracing or system matrix computation. This work evaluates different ways to store the system matrix, aiming to reconstruct dense image grids in reasonable time. Methods: We propose an optimized implementation of the Siddon’s algorithm using graphics processing units (GPUs) with a novel data storage scheme. The algorithm computes a part of the system matrix on demand, typically, for one projection angle. The proposed method was enhanced with accelerating options: storage of larger subsets of the system matrix, systematic reuse of data via geometric symmetries, an arithmetic-rich parallel code and code configuration via machine learning. It was tested on geometries mimicking a cone beam CT acquisition of a human head. To realistically assess the execution time, the ray-tracing routines were integrated into a regularized Poisson-based reconstruction algorithm. The proposed scheme was also compared to a different approach, where the system matrix is fully pre-computed and loaded at reconstruction time. Results: Fast ray-tracing of realistic acquisition geometries, which often lack spatial symmetry properties, was enabled via the proposed method. Ray-tracing interleaved with projection and backprojection operations required significant additional time. In most cases, ray-tracing was shown to use about 66 % of the total reconstruction time. In absolute terms, tracing times varied from 3.6 s to 7.5 min, depending on the problem size. The presence of geometrical symmetries allowed for non-negligible ray-tracing and reconstruction time reduction. Arithmetic-rich parallel code and machine learning permitted a modest reconstruction time reduction, in the order of 1 %. Conclusion: Partial system matrix storage permitted the reconstruction of higher 3D image grid sizes and larger projection datasets at the cost of additional time, when compared to the fully pre-computed approach. This work was supported in part by the Fonds de recherche du Quebec - Nature et technologies (FRQ-NT). The authors acknowledge partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council of Canada (Grant No. 432290).

60 APPLIED LIFE SCIENCES↗