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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Sequestration of Radionuclides in Metal–Organic Frameworks from Density Functional Theory Calculations

The high porosity, modularity, and synthetic diversity of metal-organic frameworks (MOFs) make them attractive candidate materials for selective sensing, separation, and sequestration of the radionuclides present in the nuclear wastes. Here, we use Density Functional Theory (DFT) calculations to determine the favorability of ion-exchange at the metal node of Tc, U, Th, Am, and Cm in Zr-, U-, and Th-based MOF clusters. A range of DFT methods, including several exchange-correlation functionals, DFT+U technique, relativistic effects, and magnetic effects are employed to establish robustness in the results. We also explore various reference states of ions including the vacuum, continuum water medium and explicit water molecules surrounding ions. Substitution of Tc, Am, Cm, and Th is believed to be energetically favorable in all the MOFs using ions surrounded by explicit water molecules as a reference.

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

The role of electron correlations in the electronic structure of putative Chern magnet TbMn6Sn6

Abstract A member of the RMn 6 Sn 6 rare-earth family materials, TbMn 6 Sn 6 , recently showed experimental signatures of the realization of a quantum-limit Chern magnet. In this work, we use quantum Monte Carlo (QMC) and density functional theory with Hubbard U (DFT + U ) calculations to examine the electronic structure of TbMn 6 Sn 6 . To do so, we optimize accurate, correlation-consistent pseudopotentials for Tb and Sn using coupled-cluster and configuration–interaction (CI) methods. We find that DFT + U and single-reference QMC calculations suffer from the same overestimation of the magnetic moments as meta-GGA and hybrid density functional approximations. Our findings point to the need for improved orbitals/wavefunctions for this class of materials, such as natural orbitals from CI, or for the inclusion of multi-reference effects that capture the static correlations for an accurate prediction of magnetic properties. DFT + U with Mn magnetic moments adjusted to the experiment predict the Dirac crossing in bulk to be close to the Fermi level, within ~120 meV, in agreement with the experiments. Our non-stoichiometric slab calculations show that the Dirac crossing approaches even closer to the Fermi level, suggesting the possible realization of Chern magnetism in this limit.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Searching for metastable particles using graph computing

The reconstruction of charged particle trajectories at the Large Hadron Collider and future colliders relies on energy depositions in sensors placed at distances ranging from a centimeter to a meter from the colliding beams. We propose a method of detecting charged particles that decay invisibly after traversing a short distance of about 25 cm inside the experimental apparatus. One of the decay products may constitute the dark matter known to be 84% of all matter at galactic and cosmological distance scales. Our method uses graph computing to cluster spacepoints recorded by two-dimensional silicon pixel sensors into mathematically-defined patterns. The algorithm may be implemented on silicon-based integrated circuits using field-programmable gate array technology to augment or replace traditional computing platforms.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Machine learning enabled quantification of the hydrogen bonds inside the polyelectrolyte brush layer probed using all-atom molecular dynamics simulations

The configuration of densely grafted charged polyelectrolyte (PE) brushes is strongly dictated by the properties and behavior of the counterions that screen the PE brush charges and the solvent molecules (typically water) that solvate the brush molecules and these screening counterions. Only recently, efforts have been made to study the PE brushes atomistically, thereby shedding light on the properties of brush-supported ions and water molecules. However, even for such efforts, there are limitations associated with using a generic definition to estimate certain properties of water and ions inside the brush layer. For example, water–water hydrogen bonds (HBs) will behave differently for locations outside and inside the brush layer, given the fact that the densely closely grafted PE brush molecules create a soft nanoconfinement where the water connectivity becomes highly disrupted: therefore, using the same definition to quantify the HBs inside and outside the brush layer will be unwise. In this paper, we address this limitation by employing an unsupervised machine learning (ML) approach to predict the water–water hydrogen bonding inside a cationic PE brush layer modeled using all-atom molecular dynamics (MD) simulations. Here, the ML method, which relies on a clustering approach and uses the equilibrium coordinates of the water molecules (obtained from the all-atom MD simulations) as the input, is capable of identifying the structural modification of water–water HBs (revealed through appropriate clustering of the data) inside the PE brush layer induced soft nanoconfinement. Such capabilities would not have been possible by using a generic definition of the HBs. Our calculations lead to four key findings: (1) the clusters formed inside and outside the brush layer are structurally similar; (2) the margin of the cluster is shorter inside the PE brush layer confirming the possible disruption of the HBs inside the PE brush layer; (3) the average “hydrogen–acceptor-oxygen–donor-oxygen” angle that defines the HB is reduced for the HBs formed inside the brush layer; (4) the use of the generic definition (definition usable for characterizing the HBs in brush-free bulk) leads to an overprediction of the number of HBs formed inside the PE brush layer.

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

Correlation consistent basis sets for actinides. II. The atoms Ac and Np–Lr

New correlation consistent basis sets optimized using the all-electron third-order Douglas-Kroll-Hess (DKH3) scalar relativistic Hamiltonian are reported for the actinide elements Ac and Np through Lr. These complete the series of sets reported previously for Th–U [K. A. Peterson, J. Chem. Phys. 142, 074105 (2015); M. Vasiliu et al., J. Phys. Chem. A 119, 11422 (2015)]. The new sets range in size from double- to quadruple-zeta and encompass both those optimized for valence (6s6p5f7s6d) and outer-core electron correlations (valence + 5s5p5d). The final sets have been contracted for both the DKH3 and eXact 2-component (X2C) Hamiltonians, yielding cc-pVnZ-DK3/cc-pVnZ-X2C sets for valence correlation and cc-pwCVnZ-DK3/cc-pwCVnZ-X2C sets for outer-core correlation (n = D, T, Q in each case). In order to test the effectiveness of the new basis sets, both atomic and molecular benchmark calculations have been carried out. In the first case, the first three atomic ionization potentials (IPs) of all the actinide elements Ac–Lr have been calculated using the Feller-Peterson-Dixon (FPD) composite approach, primarily with the multireference configuration interaction (MRCI) method. Excellent convergence towards the respective complete basis set (CBS) limits is achieved with the new sets, leading to good agreement with experiment, where these exist, after accurately accounting for spin-orbit effects using the 4-component Dirac-Hartree-Fock method. For a molecular test, the IP and atomization energy (AE) of PuO 2 have been calculated also using the FPD method but using a coupled cluster approach with spin-orbit coupling accounted for using the 4-component MRCI. The present calculations yield an IP 0 for PuO 2 of 159.8 kcal/mol, which is in excellent agreement with the experimental electron transfer bracketing value of 162 ± 3 kcal/mol. Likewise, the calculated 0 K AE of 305.6 kcal/mol is in very good agreement with the currently accepted experimental value of 303.1 ± 5 kcal/mol. The ground state of PuO 2 is predicted to be the 5 Σ$+\atop{0g}$ state.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Glueballs at physical pion mass

Glueballs are investigated through gluonic operators on two $ N_f=2+1 $ RBC/UKQCD gauge ensembles at the physical pion mass. The statistical errors of glueball correlation functions are considerably reduced through the cluster decomposition error reduction (CDER) method. The Bethe-Salpeter wave functions are obtained for the scalar, tensor, and pseudoscalar glueballs by using spatially extended glueball operators defined through the gauge potential $ A_\mu(x) $ in the Coulomb gauge. These wave functions exhibit similar features of non-relativistic two-gluon systems and are used to optimize the signals of the related correlation functions at the early time regions, where the ground state masses are extracted. These masses are close to those from the quenched approximation and indicate the possible existence of glueballs at the physical point. The resonance feature of glueballs and the mixing with conventional mesons and multi-hadron systems should be considered in a more systematic lattice study.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. A-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. In conclusion, we find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Spin-projected generalized Hartree-Fock method as a polynomial of particle-hole excitations

The past several years have seen renewed interest in the use of symmetry-projected Hartree-Fock for the description of strong correlations. Unfortunately, these symmetry-projected mean-field methods do not adequately account for dynamic correlation. Presumably, this shortcoming could be addressed if one could combine symmetry-projected Hartree-Fock with a many-body method such as coupled-cluster theory, but this is by no means straightforward because the two techniques are formulated in very different ways. However, we have recently shown that the singlet S 2 -projected unrestricted Hartree-Fock wave function can in fact be written in a coupled-cluster-like wave function. That is, the spin-projected unrestricted Hartree-Fock wave function can be written as a polynomial of a double-excitation operator acting on some closed-shell reference determinant. Here, we extend this result and show that the spin-projected generalized Hartree-Fock wave function (which has both S 2 and S z projection) is likewise a polynomial of low-order excitation operators acting on a closed-shell determinant and provide a closed-form expression for the resulting polynomial coefficients. The spin projection of the generalized Hartree-Fock wave function introduces connected triple and quadruple excitations which are absent when spin-projecting an unrestricted Hartree-Fock determinant. We include a few preliminary applications of the combination of this spin-projected Hartree-Fock and coupled-cluster theory to the Hubbard Hamiltonian and comment on generalizations of the methodology. Results here are not for production level, but a similarity-transformed theory that combines the two offers the promise of being accurate for both weak and strong correlation, and may offer significant improvements in the intermediate correlation regime where neither projected Hartree-Fock nor coupled cluster is particularly accurate.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Ab Initio Computation of the Longitudinal Response Function in 40 Ca

In this work, we present a consistent ab initio computation of the longitudinal response function RL in 40 Ca using the coupled-cluster and Lorentz integral transform methods starting from chiral nucleon-nucleon and three-nucleon interactions. We validate our approach by comparing our results for R L in 4 He and the Coulomb sum rule in 40 Ca against experimental data and other calculations. For R L in 40 Ca we obtain a very good agreement with experiment in the quasielastic peak up to intermediate momentum transfers, and we find that final state interactions are essential for an accurate description of the data. This work presents a milestone towards ab initio computations of neutrino-nucleus cross sections relevant for experimental long-baseline neutrino programs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

The Dark Energy Survey and Operations: Year 6 – The Finale

The Dark Energy Survey (DES) is an optical and near-infrared imaging survey aimed at understanding the accelerating expansion of the universe using four complementary methods: weak gravitational lensing, galaxy cluster counts, baryon acoustic oscillations, and Type Ia supernovae. To perform the 6-season 5000 sq-degree wide field and the 5-season 30 sq-degree supernova surveys, the DES Collaboration built the Dark Energy Camera (DECam), a 3 square-degree, 570-Megapixel CCD camera that was installed at the prime focus of the Blanco 4-meter telescope at the Cerro Tololo Inter-American Observatory (CTIO). DES started its observations in “Year 1” (Y1) on Aug. 31, 2013 and completed its sixth and final observing season on January 9, 2019. This paper describes DES Y6, the survey strategy, an outline of the survey operations procedures, the efficiency of operations and the causes of lost observing time. It provides details about the quality of the final season's data, and a summary of the Y1-Y6 observing. It contains a brief description of the last night of observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Symposium: Quantum Theory, Quantum Materials, Quantum Computing (Final Report, DE-SC0022551)

Quantum mechanics is fundamental to understanding, predicting, and controlling many physical processes in a vast range of scientific and technological areas. In molecules and materials, the quantum mechanics of Coulombic many-body systems determines far more than the ground state and bulk properties. Quantum mechanics governs magnetism, electron and spin transport, chemical reactions and catalysis, superconductivity, photosynthesis, etc. Each plays a vital role in modern society. As digital technologies push both temporal and length-scale limits, fundamental quantum principles become the current focus for a new class of technologies: quantum computing, quantum sensing, quantum encryption, etc. This oncoming quantum information sciences wave is manifest recently in frontier foci of all six Department of Energy Office of Science programs. The Sanibel Symposium offers a proven, influential platform for facilitating and advancing cross-fertilization among theoretical and computational chemists and physicists, computer scientists, and materials engineers. The Symposia have a long, successful record of driving progress on theory and computation of materials, nanostructures, and their molecular constituents. The Symposia thus are well-suited for advancing the quantum information science thrust in the Office of Basic Energy Sciences (BES). With that QIS thrust as the focus, we propose five sessions of the 61st Sanibel Symposium on the theme Quantum Theory, Quantum Materials, Quantum Computing. From a longer perspective, this thematic program for 2022 is the second year of a five year plan of program segments addressed at challenges in quantum information sciences and quantum materials. The rest of the Symposium program will be of considerable relevance, with, for example, sessions on quantum molecular and material dynamics, and new directions in coupled cluster theory. New ideas for methods and applications can be expected to emerge from the stimulus of the formal presentations and informal discussions. The proposed award is targeted at students, post-docs, and junior scientists. Conscious effort, characteristic of the Sanibel Symposia, to stimulate the participation of women and under-represented minorities will continue.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Development of Multimodal Few-Shot Analytics for Electron Micrographs

Recent advances in materials data analytics have provided new avenues for determining process-structure-property (PSP) linkages in a variety of materials. Machine learning techniques including few-shot learning have increased the efficiency of classifying microscopy images for the purposes of material characterization. Attempts at creating a multimodal approach can provide further improvements to current models and help extract more salient features from data. In this vein, raw spectrum data was taken to provide an additional modality to our current pyCHIP classifier. Modifications in segmentation also show potential in improving the accuracy of the pyCHIP classifier. Classifier output was analyzed using network graphs and unsupervised clustering algorithms such as spectral clustering to detect better segmentation methods than the current “chipping” approach. We suggest that the chip selection process can be automated in the future using a combination of these techniques to enable high-throughput analyses.

36 MATERIALS SCIENCE↗
NASA NTRS1973

Signature extension: An approach to operational multispectral surveys

Two data processing techniques were suggested as applicable to the large area survey problem. One approach was to use unsupervised classification (clustering) techniques. Investigation of this method showed that since the method did nothing to reduce the signal variability, the use of this method would be very time consuming and possibly inaccurate as well. The conclusion is that unsupervised classification techniques of themselves are not a solution to the large area survey problem. The other method investigated was the use of signature extension techniques. Such techniques function by normalizing the data to some reference condition. Thus signatures from an isolated area could be used to process large quantities of data. In this manner, ground information requirements and computer training are minimized. Several signature extension techniques were tested. The best of these allowed signatures to be extended between data sets collected four days and 80 miles apart with an average accuracy of better than 90%.

Nalepka, R. F.↗
NASA NTRS1977

LANDSAT image interpretation aids

In the Large Area Crop Inventory Experiment, image interpretation aids were produced to assist in selecting and/or identifying representative samples of signatures in a given LANDSAT scene. The three methods employed are based on clustering techniques, information extraction, and aggregation of like spectral information on a two dimensional spectral plot.

Abotteen, R. A.↗
NASA NTRS1977

Landsat image interpretation aids

In the Large Area Crop Inventory Experiment, image interpretation aids were produced to assist in selecting and/or identifying representative samples of signatures in a given Landsat scene. The three methods employed are based on clustering techniques, information extraction, and aggregation of like spectral information on a two-dimensional spectral plot.

Abotteen, R. A.↗
NASA NTRS1977

Adaptive coding of MSS imagery

A number of adaptive data compression techniques are considered for reducing the bandwidth of multispectral data. They include adaptive transform coding, adaptive DPCM, adaptive cluster coding, and a hybrid method. The techniques are simulated and their performance in compressing the bandwidth of Landsat multispectral images is evaluated and compared using signal-to-noise ratio and classification consistency as fidelity criteria.

Habibi, A.↗
NASA NTRS1988

Cloud classification from satellite data using a fuzzy sets algorithm: A polar example

Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine likely areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.

Key, J. R.↗
NASA NTRS1989

Cloud classification from satellite data using a fuzzy sets algorithm - A polar example

Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine like areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.

Key, J. R.↗