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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

The role of silicon, vacancies, and strain in carbon distribution for low temperature bainite

Here, we investigated the phenomenon of carbon supersaturation and carbon clustering in bainitic ferrite with atom probe tomography (APT) and ab-initio density functional theory (DFT) calculations. The experimental results show a homogeneous distribution of silicon in the microstructure, which contains both ferrite and retained austenite. This distribution is mimicked well by the computational approach. In addition, an accumulation of C in certain regions of the bainitic ferrite with C concentrations up to 13 at % is observed. Based on the DFT results, these clusters are explained as strained, tetragonal regions in the ferritic bainite, in which the solution enthalpy of C can reach large, negative values. It seems that Si itself only has a minor influence on this phenomenon.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Trend Analysis in Biomedical Texts via Vector Space Model Synonym Extraction

Accurate synonym extraction is a highly desired tool in computational semantics and natural language processing (NLP). Intrinsic to many current language models is the Vector Space Model (VSM) whereby linguistic properties are modeled by real vectors of small length compared to the document size. Here, several vector models are discussed. Utilizing the open source Word2Vec tool, temporal trends in biomedical abstracts from MEDLINE have been analyzed via a unique transformation matrix method. Additionally, accuracy of the word vector method and clustering of temporal trends are discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Performance of the fusion code GYRO on four generations of Cray computers

GYRO is a code used for the direct numerical simulation of plasma microturbulence. It has been ported to a variety of modern MPP platforms including several modern commodity clusters, IBM SPs, and Cray XC, XT, and XE series machines. We briefly describe the mathematical structure of the equations, the data layout, and the redistribution scheme. Also, while the performance and scaling of GYRO on many of these systems has been shown before, here we show the comparative performance and scaling on four generations of Cray supercomputers including the newest addition - the Cray XC30. The more recently added hybrid OpenMP/MPI imple- mentation also shows a great deal of promise on custom HPC systems that utilize fast CPUs and proprietary interconnects. Four machines of varying sizes were used in the experiment, all of which are located at the National Institute for Computational Sciences at the University of Tennessee at Knoxville and Oak Ridge National Laboratory. The advantages, limitations, and performance of using each system are discussed.

Fahey, Mark R↗
DOE OSTI2025

Nature of molybdenum carbide surfaces for catalytic hydrogen dissociation using machine-learned potentials: an ensemble-averaged perspective

Molybdenum carbides with an electronic structure similar to noble metals have gained attention as a promising low-cost catalyst for biomass valorization and the hydrogen evolution reaction. However, our fundamental understanding of the catalyst surface and how different phases of these catalysts behave at varying reaction conditions is limited to ground state density functional theory calculations as ab initio molecular dynamics (AIMD) is computationally prohibitive at relevant length and time scales. Here, in this work, we train a multi-atomic cluster expansion (MACE) machine-learned interatomic potentials (MLIP) to study hydrogen dissociation and dynamics over Mo, δ-MoC, α-Mo 2 C, and β-Mo 2 C surfaces at varying temperatures and hydrogen partial pressures. Our simulations identify unique and different molecular and atomic hydrogen adsorption sites on different surfaces that do not depend on the temperature. At low hydrogen pressures, the surface coverage is monolayer, which transitions to two-layer adsorption at higher pressures. We find that atomic hydrogen diffusion and recombinations are preferred over molybdenum atom hollow sites, while the diffusion over carbon-terminated facets was negligible, signifying particularly strong C–H interactions. In contrast, molecular hydrogen adsorption occurs mostly atop Mo or the bridging sites. At a comparable hydrogen loading, β-Mo 2 C (001) is the most active surface for hydrogen dissociation reaction. This work provides insights into the dynamic nature of the hydrogen dissociation chemistry and the diversity of hydrogen adsorption sites on molybdenum carbides.

08 HYDROGEN↗
DOE OSTI2018

Calculations of non-adiabatic couplings within equation-of-motion coupled-cluster framework: Theory, implementation, and validation against multi-reference methods

In this work, we report an implementation of non-adiabatic coupling (NAC) forces within the equation-of-motion coupled-cluster with single and double excitations (EOM-CCSD) framework via the summed-state approach. Using illustrative examples, we compare NAC forces computed with EOM-CCSD and multi-reference (MR) wave functions (for selected cases, we also consider configuration interaction singles). In addition to the magnitude of the NAC vectors, we analyze their direction, which is important for the calculations of the rate of non-adiabatic transitions. Our benchmark set comprises three doublet radical-cations (hexatriene, cyclohexadiene, and uracil), neutral uracil, and sodium-doped ammonia clusters. When the characters of the states agree among different methods, we observe good agreement between the respective NAC vectors, both in the Franck-Condon region and away. In the cases of large discrepancies between the methods, the disagreement can be attributed to the difference in the states’ character, which, in some cases, is very sensitive to electron correlation, both within single-reference and multi-reference frameworks. Finally, the numeric results confirm that the accuracy of NAC vectors depends critically on the quality of the underlying wave functions. Within their domain of applicability, EOM-CC methods provide a viable alternative to MR approaches.

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

Using anion photoelectron spectroscopy of cluster models to gain insights into mechanisms of catalyst-mediated H 2 production from water

We report that metal oxide cluster models of catalyst materials offer a powerful platform for probing the molecular-scale features and interactions that govern catalysis. This perspective gives an overview of studies implementing the combination of anion photoelectron (PE) spectroscopy and density functional theory calculations toward exploring cluster models of metal oxides and metal-oxide supported Pt that catalytically drive the hydrogen evolution reaction (HER) or the water–gas shift reaction. The utility in the combination of these experimental and computational techniques lies in our ability to unambiguously determine electronic and molecular structures, which can then connect to results of reactivity studies. In particular, we focus on the activity of oxygen vacancies modeled by suboxide clusters, the critical mechanistic step of forming proximal metal hydride and hydroxide groups as a prerequisite for H2 production, and the structural features that lead to trapped dihydroxide groups. The pronounced asymmetric oxidation found in heterometallic group 6 oxides and near-neighbor group 5/group 6 results in higher activity toward water, while group 7/group 6 oxides form very specific stoichiometries that suggest facile regeneration. Studies on the trans-periodic combination of cerium oxide and platinum as a model for ceria supported Pt atoms and nanoparticles reveal striking negative charge accumulation by Pt, which, combined with the ionic conductivity of ceria, suggests a mechanism for the exceptionally high activity of this system towards the water–gas shift reaction.

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

On the accuracy of density functional theory and wave function methods for calculating vertical ionization energies

The best practice in computational methods for determining vertical ionization energies (VIEs) is assessed, via reference to experimentally determined VIEs that are corroborated by highly accurate coupled-cluster calculations. These reference values are used to benchmark the performance of density functional theory (DFT) and wave function methods: Hartree-Fock theory, second-order Møller-Plesset perturbation theory, and Electron Propagator Theory (EPT). The core test set consists of 147 small molecules. An extended set of six larger molecules, from benzene to hexacene, is also considered to investigate the dependence of the results on molecule size. The closest agreement with experiment is found for ionization energies obtained from total energy difference calculations. In particular, DFT calculations using exchange-correlation functionals with either a large amount of exact exchange or long-range correction perform best. The results from these functionals are also the least sensitive to an increase in molecule size. In general, ionization energies calculated directly from the orbital energies of the neutral species are less accurate and more sensitive to an increase in molecule size. For the single-calculation approach, the EPT calculations are in closest agreement for both sets of molecules. For the orbital energies from DFT functionals, only those with long-range correction give quantitative agreement with dramatic failing for all other functionals considered. The results offer a practical hierarchy of approximations for the calculation of vertical ionization energies. In addition, the experimental and computational reference values can be used as a standardized set of benchmarks, against which other approximate methods can be compared.

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

Using first principles calculations to interpret XANES experiments: extracting the size-dependence of the ( p , T ) phase diagram of sub-nanometer Cu clusters in an O 2 environment

Here, we have used ab initio density functional theory together with ab initio atomistic thermodynamics, and in situ X-ray absorption near edge spectroscopy (XANES) experiments, to study the oxidation of sub-nanometer clusters of Cu n O x supported on a hydroxylated amorphous alumina substrate in an O 2 -rich environment. We obtain ( p , T ) phase diagrams; these differ notably for the nanoclusters compared to the bulk. Both theory and experiment suggest that in the presence of oxygen, the cluster will oxidize from its elemental state to the oxidized state as temperature decreases. We obtain a clear trend for the transition Cu n → Cu n O n/2 : we see that smaller the cluster, greater is the tendency toward oxidation. However, we do not see a monotonic size-dependent trend for the transition Cu n O n/2 → Cu n O n . We suggest that theoretically computed Bader charges constitute a simple yet quantitative way to align experimental measures of XANES edges with theoretical calculations, so as to yield oxidation states for nanoclusters. Our results have important implications for the use of small clusters in fields such as nanocatalysis and nanomedicine.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Predicting phase behavior of grain boundaries with evolutionary search and machine learning

The study of grain boundary phase transitions is an emerging field until recently dominated by experiments. The major bottleneck in the exploration of this phenomenon with atomistic modeling has been the lack of a robust computational tool that can predict interface structure. Here we develop a computational tool based on evolutionary algorithms that performs efficient grand-canonical grain boundary structure search and we design a clustering analysis that automatically identifies different grain boundary phases. Its application to a model system of symmetric tilt boundaries in Cu uncovers an unexpected rich polymorphism in the grain boundary structures. We find new ground and metastable states by exploring structures with different atomic densities. Our results demonstrate that the grain boundaries within the entire misorientation range have multiple phases and exhibit structural transitions, suggesting that phase behavior of interfaces is likely a general phenomenon.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Fluorescence Color by Data-Driven Design of Genomic Silver Clusters

DNA nucleobase sequence controls the size of DNA-stabilized silver clusters, leading to their well-known yet little understood sequence-tuned colors. The enormous space of possible DNA sequences for templating clusters has challenged the understanding of how sequence selects cluster properties and has limited the design of applications that employ these clusters. Here, we investigate the genomic role of DNA sequence for fluorescent silver clusters using a data-driven approach. Employing rapid parallel silver cluster synthesis and fluorimetry, we determine the fluorescence spectra of silver cluster products stabilized by 1432 distinct DNA oligomers. By applying pattern recognition algorithms to this large experimental data set, we discover certain DNA base patterns, or “motifs,” that correlate to silver clusters with similar fluorescence spectra. These motifs are employed in machine learning classifiers to predictively design DNA template sequences for specific fluorescence color bands. Our method improves selectivity of templates by 330% for silver clusters with peak emission wavelengths beyond 660 nm. The discovered base motifs also provide physical insights into how DNA sequence controls silver cluster size and color. Finally, this predictive design approach for color of DNA-stabilized silver clusters exhibits the potential of machine learning and data mining to increase the precision and efficiency of nanomaterials design, even for a soft-matter-inorganic hybrid system characterized by an extremely large parameter space.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Computational stochastic model of ions implantation

Implantation flux ions into crystal leads to phase transition /PT/ 1-st kind. Damaging lattice is associated with processes clustering vacancies and gaseous bubbles as well their brownian motion. System of stochastic differential equations /SDEs/ Ito for evolution stochastic dynamical variables corresponds to the superposition Wiener processes. The kinetic equations in partial derivatives /KE/, Kolmogorov-Feller and Einstein-Smolukhovskii, were formulated for nucleation into lattice of weakly soluble gases. According theory, coefficients of stochastic and kinetic equations uniquely related. Radiation stimulated phase transition are characterized by kinetic distribution functions /DFs/ of implanted clusters versus their sizes and depth of gas penetration into lattice. Macroscopic parameters of kinetics such as the porosity and stress calculated in thin layers metal/dielectric due to Xe{sup ++} irradiation are attracted as example. Predictions of porosity, important for validation accumulation stresses in surfaces, can be applied at restoring of objects the cultural heritage.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

libEnsemble: A complete Python toolkit for dynamic ensembles of calculations

Almost all science and engineering applications eventually stop scaling: their runtime no longer decreases as available computational resources increase. Therefore, many applications will struggle to efficiently use emerging extreme-scale high-performance, parallel, and distributed systems. libEnsemble is a complete Python toolkit and workflow system for intelligently driving ensembles of experiments or simulations at massive scales. It enables and encourages multidisciplinary design, decision, and inference studies portably running on laptops, clusters, and supercomputers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

NEW CONSTRAINTS ON THE FAINT END OF THE UV LUMINOSITY FUNCTION AT z ∼ 7-8 USING THE GRAVITATIONAL LENSING OF THE HUBBLE FRONTIER FIELDS CLUSTER A2744

Exploiting the power of gravitational lensing, the Hubble Frontier Fields (HFF) program aims at observing six massive galaxy clusters to explore the distant universe far beyond the limits of blank field surveys. Using the complete Hubble Space Telescope observations of the first HFF cluster A2744, we report the detection of 50 galaxy candidates at z ∼ 7 and eight candidates at z ∼ 8 in a total survey area of 0.96 arcmin{sup 2} in the source plane. Three of these galaxies are multiply imaged by the lensing cluster. Using an updated model of the mass distribution in the cluster we were able to calculate the magnification factor and the effective survey volume for each galaxy in order to compute the ultraviolet galaxy luminosity function (LF) at both redshifts 7 and 8. Our new measurements reliably extend the z ∼ 7 UV LF down to an absolute magnitude of M {sub UV} ∼ –15.5. We find a characteristic magnitude of M{sub UV}{sup ⋆}=−20.90{sub −0.73}{sup +0.90} mag and a faint-end slope α=−2.01{sub −0.28}{sup +0.20}, close to previous determinations in blank fields. We show here for the first time that this slope remains steep down to very faint luminosities of 0.01 L {sup *}. Although prone to large uncertainties, our results at z ∼ 8 also seem to confirm a steep faint-end slope below 0.1 L {sup *}. The HFF program is therefore providing an extremely efficient way to study the faintest galaxy populations at z > 7 that would otherwise be inaccessible with current instrumentation. The full sample of six galaxy clusters will provide even better constraints on the buildup of galaxies at early epochs and their contribution to cosmic reionization.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models

We introduce DeePKS-kit, an open-source software package for developing machine learning based energy and density functional models. DeePKS-kit is interfaced with PyTorch, an open-source machine learning library, and PySCF, an ab initio computational chemistry program that provides simple and customized tools for developing quantum chemistry codes. It supports the DeePHF and DeePKS methods. In addition to explaining the details in the methodology and the software, we also provide an example of developing a chemically accurate model for water clusters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

BULKI-Store v0.3.2

BULKI-Store is a distributed object storage system optimized for high-performance computing environments. Built with a Rust core and Python bindings, it efficiently manages scientific and machine learning datasets across HPC clusters. The system employs a client-server architecture with MPI integration, enabling seamless scaling on supercomputers like Perlmutter. BULKI-Store's object-oriented approach provides intuitive data organization with rich metadata support, contrasting with traditional file-based solutions. Key optimizations include selective checkpoint loading, unified checkpoint files, and object chunking for large data transfers. For machine learning workloads, BULKI-Store offers advantages through fine-grained access patterns, dynamic data sharing between training instances, and reduced memory pressure. Memory management features include strategic Python GC calls, minimized data copies, and batch processing capabilities. The system leverages Rayon's thread pool for asynchronous data prefetching and supports multiple CPU architectures (ARM64, x86, AMD, RISC-V). By combining performance optimizations with developer-friendly APIs, BULKI-Store addresses the complex data management challenges of modern HPC applications while maintaining compatibility across heterogeneous computing environments.

Zhang, Wei [Lawrence Berkeley National Laboratory ↗
DOE OSTI2023

Final-state interactions and spin structure in E 1 breakup of Li 11 in halo effective field theory

In this work, we calculate the E1 breakup of the 2n halo nucleus 11 Li in halo effective field theory (Halo EFT) at leading order. In Halo EFT, 11 Li is treated as a three-body system of a 9 Li core and two neutrons. We present a detailed investigation of final-state interactions (FSIs) in the neutron-neutron (nn) and neutron-core (nc) channels. We employ Møller operators to formulate an expansion scheme that satisfies the non-energy-weighted cluster sum rule and successively includes higher-order terms in the multiple-scattering series for the FSI. Computing the E1 strength up to third order in this scheme, we observe apparent convergence and good agreement with experiment. The neutron-neutron FSI is by far the most important contribution and largely determines the maximum value of the E1 distribution. However, inclusion of nc FSI does shift the peak position to slightly lower energies. Moreover, we investigate the sensitivity of the E1 response to the spin structure of the neutron- 9 Li interaction. We contrast results for an interaction that is the same in the spin-1 and spin-2 channels with one that is only operative in the spin-2 channel, and find that good agreement with experimental data is only obtained if the interaction is present in both spin channels. The latter case is shown to be equivalent to a calculation in which the spin of 9 Li is neglected.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

A marked correlation function for constraining modified gravity models

Future large scale structure surveys will provide increasingly tight constraints on our cosmological model. These surveys will report results on the distance scale and growth rate of perturbations through measurements of Baryon Acoustic Oscillations and Redshift-Space Distortions. It is interesting to ask: what further analyses should become routine, so as to test as-yet-unknown models of cosmic acceleration? Models which aim to explain the accelerated expansion rate of the Universe by modifications to General Relativity often invoke screening mechanisms which can imprint a non-standard density dependence on their predictions. This suggests density-dependent clustering as a `generic' constraint. This paper argues that a density-marked correlation function provides a density-dependent statistic which is easy to compute and report and requires minimal additional infrastructure beyond what is routinely available to such survey analyses. We give one realization of this idea and study it using low order perturbation theory. We encourage groups developing modified gravity theories to see whether such statistics provide discriminatory power for their models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Unsupervised Clustering-Based Short-Term Solar Forecasting

Solar forecasting accuracy is highly affected by weather conditions, therefore, weather awareness forecasting models are expected to improve the forecasting performance. However, it may not be available or reliable to classify different forecasting tasks by only using predefined meteorological weather categorization. In this paper, an unsupervised clustering-based (UC-based) solar forecasting method is developed for short-term (1-h-ahead) global horizontal irradiance (GHI) forecasting. This UC-based method consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting. The daily GHI time series is first clustered by an Optimized Cross-validated ClUsteRing (OCCUR) method, which determines the optimal number of clusters and best clustering results. Then, support vector machine pattern recognition is adopted to recognize the category of a certain day using the first four hours' data in the forecasting stage. GHI forecasts are generated by the most suitable models in different clusters, which are built by a two-layer machine learning based multi-model (M3) forecasting framework. The developed UC-M3 method is validated by using 1-year of data with 13 solar features from three information sources. Numerical results show that 1) UC-based models outperform non-UC (all-in-one) models with the same M3 architecture by approximately 20%; and 2) M3-based models also outperform the single-algorithm machine learning models by approximately 20%.

14 SOLAR ENERGY↗