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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 OSTI2023

Impact of Soret effect on hydrogen and helium retention in PFC tungsten under ELM-like conditions

Abstract In our previous work, we have demonstrated using nonequilibrium molecular-dynamics simulations that the fluxes of helium and self-interstitial atoms in the presence of a thermal gradient in tungsten are directed opposite to the heat flux, indicating that species transport is governed by a Soret effect, namely, thermal-gradient-driven diffusion, characterized by a negative heat of transport that drives species transport uphill, i.e. from the cooler to the hot regions of the tungsten sample. In this work, the findings of our thermal and species transport analysis have been implemented in our cluster-dynamics code, Xolotl, which has been used to compute temperature and species profiles over spatiotemporal scales representative of plasma-facing component (PFC) tungsten under typical reactor operating conditions, including extreme heat loads at the plasma-facing surface characteristic of plasma instabilities that induce edge localized modes (ELMs). We demonstrate that the steady-state species profiles, when properly accounting for the Soret effect, vary significantly from those where temperature-gradient-driven transport is not accounted for and discuss the implications of such a Soret effect on the response to plasma exposure of plasma-facing tungsten. Although our cluster-dynamics simulations do not yet include self-clustering of helium or hydrogen blister formation, our simulation results show that the Soret effect substantially reduces helium and hydrogenic species retention inside PFC tungsten.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Advancing Understanding of Fission Gas Behavior in Nuclear Fuel through Leadership Class Computing

This report describes the outcome of the pilot SciDAC project jointly funded by the DOE Office of Nuclear Energy and the DOE Office of Science, Advanced Scientific Computing Research to advance understanding of fission gas behavior in nuclear fuel. The computational approach involved utilizing both a bottom up and top down multiscale materials modeling to evaluate the processes of xenon fission gas diffusion, xenon clustering and precipitation and the mechanisms of fission gas resolution by energetic fission fragments in uranium dioxide nuclear fuel. The computer science and applied mathematics research activities were focused on optimizing the uranium – oxygen – xenon interatomic potential implementation in the LAMMPS molecular dynamics code for large-scale simulations of fission gas resolution, in addition to improvements in visualization of accelerated molecular dynamics simulations and performance optimization of the Xolotl continuum cluster dynamics with a new grouping scheme to reduce the computational cost of modeling very large fission gas bubbles containing more than 10,000 xenon atoms. The most impactful result from this project stems from a systematic series of atomistic molecular dynamics to evaluate fission gas resolution from xenon bubbles that, combined with the fission product yields and consideration of electron-phonon coupling, informed a first principles model that predicts the magnitude of fission gas resolution in good agreement with empirical estimates.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Phonon localization in binary alloys with diagonal and off-diagonal disorder: A cluster Green's function approach

We report the development and application of a method for carrying out computational investigations of the effects of mass and force-constant (FC) disorder on phonon spectra. The method is based on the recently developed typical medium dynamical cluster approach, which is a Green's function approach. Excellent quantitative agreement with previous exact diagonalization results establishes the veracity of the method. Application of the method to a model system of binary mass and an FC-disordered system leads to several findings. A narrow resonance, significantly below the van Hove singularity, that has been termed the boson peak, is seen to emerge for low soft particle concentrations. We show, using the typical phonon spectrum, that the states constituting the boson peak cross over from being completely localized to being extended as a function of increasing soft particle concentration. In general, an interplay of mass and FC disorder is found to be cooperative in nature, enhancing phonon localization over all frequencies. However, for a certain range of frequencies, and depending on material parameters, FC disorder can delocalize the states that were localized by mass disorder, and vice versa. Modeling vacancies as weakly bonded sites with vanishing mass, we find that vacancies, even at very low concentrations, are extremely effective in localizing phonons. Thus, inducing vacancies is proposed as a promising route for efficient thermoelectrics. Lastly, we use model parameters corresponding to the alloy system, Ni 1- x Pt x , and we show that mass disorder alone is insufficient to explain the pseudogap in the phonon spectrum; the concomitant presence of FC disorder is necessary.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

THE SWIFT X-RAY TELESCOPE CLUSTER SURVEY. III. CLUSTER CATALOG FROM 2005-2012 ARCHIVAL DATA

We present the Swift X-ray Cluster Survey (SWXCS) catalog obtained using archival data from the X-ray telescope (XRT) on board the Swift satellite acquired from 2005 February to 2012 November, extending the first release of the SWXCS. The catalog provides positions, soft fluxes, and, when possible, optical counterparts for a flux-limited sample of X-ray group and cluster candidates. We consider the fields with Galactic latitude |b| > 20° to avoid high H I column densities. We discard all of the observations targeted at groups or clusters of galaxies, as well as particular extragalactic fields not suitable to search for faint extended sources. We finally select ∼3000 useful fields covering a total solid angle of ∼400 deg{sup 2}. We identify extended source candidates in the soft-band (0.5-2 keV) images of these fields using the software EXSdetect, which is specifically calibrated for the XRT data. Extensive simulations are used to evaluate contamination and completeness as a function of the source signal, allowing us to minimize the number of spurious detections and to robustly assess the selection function. Our catalog includes 263 candidate galaxy clusters and groups down to a flux limit of 7 × 10{sup –15} erg cm{sup –2} s{sup –1} in the soft band, and the logN-logS is in very good agreement with previous deep X-ray surveys. The final list of sources is cross-correlated with published optical, X-ray, and Sunyaev-Zeldovich catalogs of clusters. We find that 137 sources have been previously identified as clusters in the literature in independent surveys, while 126 are new detections. Currently, we have collected redshift information for 158 sources (60% of the entire sample). Once the optical follow-up and the X-ray spectral analysis of the sources are complete, the SWXCS will provide a large and well-defined catalog of groups and clusters of galaxies to perform statistical studies of cluster properties and tests of cosmological models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Revealing EDL-driven reduction mechanisms in binary, ternary, and quaternary fluorinated electrolytes via an integrated MD–DFT–ML framework

Accurately predicting solid electrolyte interphase (SEI) formation requires explicitly resolving the electric double layer (EDL) structure, which deviates significantly from that of the bulk electrolyte. Although an established molecular dynamics (MD) and Density Functional Theory (DFT) framework can model SEI formation by evaluating reduction reactions of local clusters in the EDL, it suffers from a combinatorial computational bottleneck. To overcome this limitation, we introduce a machine-learning-accelerated simulation workflow (MD–DFT–ML), integrating a gradient-boosted regression model trained on EDL composition data to efficiently predict reduction potentials. We apply this framework to seven fluorinated electrolytes comprising fluorinated anions, a fluorinated ester solvent, two types of diluent (ion-solvating ester vs. non-solvating ether), and an FEC additive. The analysis shows that the EDL selectively accumulates cation-binding species; consequently, the non–cation-binding ether diluent rarely enters the EDL and makes minimal contributions to SEI formation. DFT calculations on statistically representative EDL clusters provide reduction potentials and fluorine-release pathways, while the ML model, which substantially reduces the DFT workload, predicts cluster reduction energies with a mean absolute error of 0.1 eV. The combined MD–DFT–ML approach also quantifies contributions from different sources to LiF formation in the SEI. This methodology establishes a generalizable route for multiscale modeling electrolyte and interphase design for next-generation electrochemical energy-storage systems.

DFT-MD-ML workflow↗
DOE OSTI2022

Unitary Selective Coupled-Cluster Method

Simulating molecules using the Variational Quantum Eigensolver method is one of the promising applications for NISQ-era quantum computers. Designing an efficient ansatz to represent the electronic wave function is crucial in such simulations. Standard unitary coupled-cluster with singles and doubles (UCCSD) ansatz tends to have a large number of insignificant terms that do not lower the energy of the system. In this work, we present a unitary selective coupled-cluster method, a way to construct a unitary coupled-cluster ansatz iteratively using a selection procedure with excitations up to fourth order. This approach uses the electronic Hamiltonian matrix elements and the amplitudes for excitations already present in the ansatz to find the important excitations of higher order and to add them to the ansatz. The important feature of the method is that it systematically reduces the energy error with increasing ansatz size for a set of test molecules. {The main advantage of the proposed method is that the effort to increase the ansatz does not require any additional measurements on a quantum computer.}

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Comprehensive Benchmark Suite for Simulation of Particle Laden Flows Using the Discrete Element Method with Performance Profiles from the Multiphase Flow with Interface eXchanges (MFiX) Code

Five benchmark problems are developed and simulated with the computational fluid dynamics and discrete element model code MFiX. The benchmark problems span dilute and dense regimes, consider statistically homogeneous and inhomogeneous (both clusters and bubbles) particle concentrations and a range of particle and fluid dynamic computational loads. Several variations of the benchmark problems are also discussed to extend the computational phase space to cover granular (particles only), bidisperse and heat transfer cases. A weak scaling analysis is performed for each benchmark problem and, in most cases, the scalability of the code appears reasonable up to approx. 103 cores. Profiling of the benchmark problems indicate that the most substantial computational time is being spent on particle-particle force calculations, drag force calculations and interpolating between discrete particle and continuum fields. Hardware performance analysis was also carried out showing significant Level 2 cache miss ratios and a rather low degree of vectorization. These results are intended to serve as a baseline for future developments to the code as well as a preliminary indicator of where to best focus performance optimizations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Processing unit performance projection using dynamic hardware behaviors

Methods for estimating accelerator performance for dynamic hardware behaviors are disclosed. Computer program code to be executed on a first processing unit is received, and an execution of the computer code on the first processing unit is monitored to determine a plurality of performance characteristics. A plurality of dynamic hardware behaviors is determined by applying a clustering algorithm to the performance characteristics, and an equivalent accelerator portion of computer code to be executed on a second processing unit is generated by translating a set of instructions in a first portion of computer code corresponding to a first one of the plurality of dynamic hardware behaviors to an equivalent set of instructions to be executed on the second processing unit. An estimated measure of performance for executing the equivalent accelerator portion on the second processing unit is determined for the first one of the plurality of dynamic hardware behaviors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Challenge Online Time Series Clustering For Demand Response A Theory to Break the ‘Curse of Dimensionality'

The advent of smart meters and advanced communication infrastructures catalyzes numerous smart grid applications such as dynamic demand response, and paves the way to solve challenging research problems in sustainable energy consumption. The space of solution possibilities are restricted primarily by the huge amount of generated data requiring considerable computational resources and efficient algorithms. To overcome this Big Data challenge, data clustering techniques have been proposed. Current approaches however do not scale in the face of the “increasing dimensionality” problem where a cluster point is represented by the entire customer consumption time series. To overcome this aspect we first rethink the way cluster points are created and designed, and then design an efficient online clustering technique for demand response (DR) in order to analyze high volume, high dimensional energy consumption time series data at scale, and on the fly. Our online algorithm is randomized in nature, and provides optimal performance guarantees in a computationally efficient manner. Unlike prior work we (i) study the consumption properties of the whole population simultaneously rather than developing individual models for each customer separately, claiming it to be a ‘killer’ approach that breaks the “curse of dimensionality” in online time series clustering, and (ii) provide tight performance guarantees in theory to validate our approach. Our insights are driven by the field of sociology, where collective behavior often emerges as the result of individual patterns and lifestyles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Three dimensional cluster analysis for atom probe tomography using Ripley’s K-function and machine learning

The size and structure of spatial molecular and atomic clustering can significantly impact material properties and is therefore important to accurately quantify. Ripley’s K-function (K(r)), a measure of spatial correlation, can be used to perform such quantification when the material system of interest can be represented as a marked point pattern. This work demonstrates how machine learning models based on K (r)-derived metrics can accurately estimate cluster size and intra-cluster density in simulated three dimensional (3D) point patterns containing spherical clusters of varying size; over 90% of model estimates for cluster size and intra-cluster density fall within 11% and 18% error of the true values, respectively. These K (r)-based size and density estimates are then applied to an experimental APT reconstruction to characterize MgZn clusters in a 7000 series aluminum alloy. Here we find that the estimates are more accurate, consistent, and robust to user interaction than estimates from the popular maximum separation algorithm. Using K (r) and machine learning to measure clustering is an accurate and repeatable way to quantify this important material attribute.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Efficient Modeling of Structural, Electronic, and Optical Properties of Silver and Gold Metal Nanoclusters and Alloys Using Optimized SCC-DFTB Parameters

Computation of optical properties using conventional time-dependent density functional theory (TD-DFT) is time-consuming and memory-intensive. In this study, we investigate the accuracy and efficiency of the density functional tight binding (DFTB) framework with newly optimized Slater–Koster (SK) parameters for modeling the structural, electronic properties, and absorption spectra of silver and gold nanoclusters and their alloys. Our investigation of the ground state (GS) properties demonstrates that the newly developed GS-SK parameters enable DFTB to closely approximate DFT-calculated bond lengths for octahedron, tetrahedron, icosahedra, and truncated octahedron with sizes Ag n /Au n (n = 19, 20, 38, 55), nanoclusters and Ag 20 /Au 20 nanoalloys, with a maximum deviation of approximately 0.15 Å. Formation energy results indicate that the GS-SK parameters can closely estimate changes in formation energies with alloy composition, and the comparison of electronic structures for Ag 20 , Au 20 , and AgAu alloy nanoclusters using the DFTB approximation reveals good agreement in the projected density of states (DOS) profiles and energy levels. A second set of SK parameters, ES-SK, has been developed to describe excited state (ES) properties, including the absorption spectra of silver octahedron Ag 19 , tetrahedral Ag n (n = 20, 56, 84), truncated octahedron Ag 38 , and icosahedra Ag 55 closed-shell clusters and their gold and alloy counterparts over a broad range of alloy compositions. This parametrization uses TD-DFTB calculations and fine-tunes the d and p eigenvalues by comparing them to reference absorption spectra from first-principles TD-DFT. This enables the generation of absorption spectra that closely match the reference spectra when plasmon excitation is dominant, as demonstrated by studying the plasmonic properties of icosahedral Ag n and Au n (n = 309 and 561) nanoparticles. This includes the rapid loss in plasmon quality when Au partially replaces Ag in alloy clusters. Furthermore, these results provide a foundation for addressing computational bottlenecks in plasmonics and with new prospects for applications in the quantum plasmonics for bimetallic alloys.

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

Navier: Dataflow Architecture for Computation Chemistry

Navier’s objectives were two evaluate the use of emerging technologies, especially dataflow accelerators, for high-performance computing (HPC) applications, specifically in the domain of chemistry, and to develop a prototype software stack to support such applications. Navier builds on capabilities previously developed by synergistic projects, such as PNNL Data Model Convergence (DMC) LDRD Hardware Advanced Workflows (HAW) and DuOMO, as well as DOE ARIAA. Throughout its 18 months, the Navier team developed new capabilities and artifacts at all levels of the HW/SW stack, provided a seamless way to integrate novel computing architectures (Sambanova SN10 and Xilinx Versal AI) into an existing software stack, developed chemistry workflows, data analytics tools, and HPC molecular dynamics workflows that leverage the developed stack and PNNL institutional investments in emerging architectures. Navier also explored the use of active learning to accelerate a computational chemistry workflow for organic molecules on PNNL Junction cluster (in collaboration with AMD/Xilinx). Navier developed tools, methodologies, and studies for hardware software co-design and (sparse) dataflow accelerators that are composable and can be used together or separately. These methodologies are now used in other projects, such as DOE AMAIS and HPDA. This report describes Navier’s achievement, the developed tools and methodologies, and the research findings and conclusions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

QUBO formulations for training machine learning models

Abstract Training machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers can approximately solve NP-hard problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore’s law era. In order to solve problems on adiabatic quantum computers, they must be formulated as QUBO problems, which is very challenging. In this paper, we formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k-means clustering—as QUBO problems, making them conducive to be trained on adiabatic quantum computers. We also analyze the computational complexities of our formulations and compare them to corresponding state-of-the-art classical approaches. We show that the time and space complexities of our formulations are better (in case of SVM and balanced k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Metastability, spectrum, and eigencurrents of the Lennard-Jones-38 network

We develop computational tools for spectral analysis of stochastic networks representing energy landscapes of atomic and molecular clusters. Physical meaning and some properties of eigenvalues, left and right eigenvectors, and eigencurrents are discussed. We propose an approach to compute a collection of eigenpairs and corresponding eigencurrents describing the most important relaxation processes taking place in the system on its way to the equilibrium. It is suitable for large and complex stochastic networks where pairwise transition rates, given by the Arrhenius law, vary by orders of magnitude. The proposed methodology is applied to the network representing the Lennard-Jones-38 cluster created by Wales's group. Its energy landscape has a double funnel structure with a deep and narrow face-centered cubic funnel and a shallower and wider icosahedral funnel. However, the complete spectrum of the generator matrix of the Lennard-Jones-38 network has no appreciable spectral gap separating the eigenvalue corresponding to the escape from the icosahedral funnel. We provide a detailed description of the escape process from the icosahedral funnel using the eigencurrent and demonstrate a superexponential growth of the corresponding eigenvalue. The proposed spectral approach is compared to the methodology of the Transition Path Theory. Finally, we discuss whether the Lennard-Jones-38 cluster is metastable from the points of view of a mathematician and a chemical physicist, and make a connection with experimental works.

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

Structure and Dynamics of Polysulfide Clusters in a Nonaqueous Solvent Mixture of 1,3-Dioxolane and 1,2-Dimethoxyethane

Molecular clustering and associated dynamic processes of lithium polysulfide species were unraveled using classical molecular dynamics and ab initio metadynamics calculations. The spectroscopic signatures of polysulfide clusters were analyzed using a multimodal analysis including experimental and computational nuclear magnetic resonance (NMR) and X-ray absorption spectroscopies. Lithium polysulfide solutes (Li 2 S 4 , Li 2 S 6 , and Li 2 S 8 ) and their mixtures in a 1,3-dioxolane and 1,2-dimethoxyethane (DOL/DME) solvent undergo aggregation driven by intramolecular lithium–sulfur (Li–S) interactions, leading to distributions of cluster sizes, which could critically influence the functioning of lithium–sulfur batteries. Representative polysulfide clusters with systematic increases in molecular size were extracted from the classical molecular dynamics trajectories for subsequent structural and spectroscopic property calculations using density functional theory analysis. Structural analysis of these clusters reveals progressively decreasing solvent involvement in Li+ coordination varying from Li 2 S 4 to Li 2 S 8 , with more pronounced variation and changes in DME compared with those in DOL. These observations are reflected in the analysis of the experimental and theoretical 7 Li and 17 O NMR chemical shifts and pulsed field gradient-NMR diffusion measurements. A comparison of experimental and theoretical S K-edge X-ray absorption near edge spectra shows that relatively large lithium sulfide chain clusters are likely to occur in the DOL/DME-solvated lithium sulfide systems. Ab initio metadynamics simulations and NMR analysis indicate that Li + solvated by only the solvent can occur through Li + dissociation from sulfide chains. However, the occurrence of “sulfide-free” Li + is a minor mechanism compared with the dynamic aggregation and shuttling processes of polysulfide solvates in DOL/DME-based electrolytes of Li–S batteries. Overall, atomistic insights gained about clustering and lithium exchange dynamics will be critical for the predictive understanding of the polysulfide shuttling and nucleation process that dictates the Li–S battery performance.

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

CASM — A software package for first-principles based study of multicomponent crystalline solids

CASM is a software package that enables first-principles based studies of crystalline materials. It has been designed to treat coupled chemical, mechanical, vibrational, and magnetic degrees of freedom to determine ground state and finite temperature properties of crystals. The symmetry of the underlying parent crystal structure is used to enumerate perturbations of the parent crystal structure and generate derivative structures which can be input to first-principles calculations in order to explore the ground state energy landscape. CASM algorithmically constructs cluster expansions that fully couple discrete and continuous degrees of freedom, and generates highly efficient code to evaluate the cluster expansion basis functions. Widely used machine learning methods are integrated for fitting expansion coefficients to first-principle calculations. The fully parameterized cluster expansions can be combined with (kinetic) Monte Carlo methods to calculate finite temperature thermodynamic and kinetic properties. CASM Alloy Manager identifies distinct parent crystal structures in an alloy system, creating individual projects for each, and integrating the results. The integrated infrastructure facilitates the linkage between first-principles statistical mechanics predictions with higher length scale computational methods, such as phase field simulations. In conclusion, CASM projects are designed to be easy to share in repositories, to re-use, and to extend to include additional chemical species or types of degrees of freedom.

36 MATERIALS SCIENCE↗