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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Massively parallel GPU enabled third-order cluster perturbation excitation energies for cost-effective large scale excitation energy calculations

We present here a massively parallel implementation of the recently developed CPS(D-3) excitation energy model that is based on cluster perturbation theory. The new algorithm extends the one developed in Baudin et al. [J. Chem. Phys., 150, 134110 (2019)] to leverage multiple nodes and utilize graphical processing units for the acceleration of heavy tensor contractions. Furthermore, we show that the extended algorithm scales efficiently with increasing amounts of computational resources and that the developed code enables CPS(D-3) excitation energy calculations on large molecular systems with a low time-to-solution. More specifically, calculations on systems with over 100 atoms and 1000 basis functions are possible in a few hours of wall clock time. This establishes CPS(D-3) excitation energies as a computationally efficient alternative to those obtained from the coupled-cluster singles and doubles model.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2014

Giant and pigmy dipole resonances in 4 He, 16,22 O, and 40 Ca from chiral nucleon-nucleon interactions

We combine the coupled-cluster method and the Lorentz integral transform for the computation of inelastic reactions into the continuum. We show that the bound-state-like equation characterizing the Lorentz integral transform method can be reformulated based on extensions of the coupled-cluster equation-of-motion method, and we discuss strategies for viable numerical solutions. Starting from a chiral nucleon-nucleon interaction at next-to-next-to-next-to-leading order, we compute the giant dipole resonances of 4 He, 16,22 O, and 40 Ca, truncating the coupled-cluster equation-of-motion method at the two-particle–two-hole excitation level. Within this scheme, we find a low-lying E1 strength in the neutron-rich 22 O nucleus, which compares fairly well with data from Leistenschneider et al. [Phys. Rev. Lett. 86, 5442 (2001)]. We also compute the electric dipole polarizability in 40 Ca. Deficiencies of the employed Hamiltonian lead to overbinding, too-small charge radii, and a too-small electric dipole polarizability in 40 Ca.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Low oxidation state aluminum-containing cluster anions: Cp{sup ∗}Al{sub n}H{sup −}, n = 1–3

Three new, low oxidation state, aluminum-containing cluster anions, Cp*Al{sub n}H{sup −}, n = 1–3, were prepared via reactions between aluminum hydride cluster anions, Al{sub n}H{sub m}{sup −}, and Cp*H ligands. These were characterized by mass spectrometry, anion photoelectron spectroscopy, and density functional theory based calculations. Agreement between the experimentally and theoretically determined vertical detachment energies and adiabatic detachment energies validated the computed geometrical structures. Reactions between aluminum hydride cluster anions and ligands provide a new avenue for discovering low oxidation state, ligated aluminum clusters.

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

Clustering and α-capture reaction rate from ab initio 20 Ne

We introduce a new framework for studying clustering and for calculating α partial widths using ab initio wave functions. We demonstrate the formalism for 20 Ne, by calculating the overlap between the 16 O + α cluster configuration and states in 20 Ne computed in the ab initio symmetry-adapted no-core shell model. We present spectroscopic amplitudes and spectroscopic factors, and compare those to no-core symplectic shell-model results in larger model spaces, to gain insight into the underlying physics that drives α clustering. Specifically, we report on the α partial width of the lowest 1 – resonance in 20 Ne, which is found to be in good agreement with experiment.We also present first no-core shell-model estimates for asymptotic normalization coefficients for the ground state, as well as for the first excited 4 + state in 20 Ne that lies in a close proximity to the α + 16 O threshold. This outcome highlights the importance of correlations for developing cluster structures and for describing α widths. The widths can then be used to calculate α-capture reaction rates for narrow resonances of interest to astrophysics. Finally, we explore the reaction rate for the α-capture reaction 16 O(α, γ ) 20 Ne at astrophysically relevant temperatures and determine its impact on simulated x-ray burst abundances.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Unveiling Temporal Performance Deviation: Leveraging Clustering in Microservices Performance Analysis

As the market for cloud computing continues to grow, an increasing number of users are deploying applications as microservices. The shift introduces unique challenges in identifying and addressing performance issues, particularly within large and complex infrastructures. To address this challenge, we propose a methodology that unveils temporal performance deviations in microservices by clustering containers based on their performance characteristics at different time intervals. Showcasing our methodology on the Alibaba dataset, we found both stable and dynamic performance patterns, providing a valuable tool for enhancing overall performance and reliability in modern application landscapes.

Clustering↗
DOE OSTI2022

Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists

Experimental results are often plotted as 2-dimensional graphical plots (aka graphs) in scientific domains depicting dependent versus independent variables to aid visual analysis of processes. Repeatedly performing laboratory experiments consumes significant time and resources, motivating the need for computational estimation. The goals are to estimate the graph obtained in an experiment given its input conditions, and to estimate the conditions that would lead to a desired graph. Existing estimation approaches often do not meet accuracy and efficiency needs of targeted applications. We develop a computational estimation approach called AutoDomainMine that integrates clustering and classification over complex scientific data in a framework so as to automate classical learning methods of scientists. Knowledge discovered thereby from a database of existing experiments serves as the basis for estimation. Challenges include preserving domain semantics in clustering, finding matching strategies in classification, striking a good balance between elaboration and conciseness while displaying estimation results based on needs of targeted users, and deriving objective measures to capture subjective user interests. These and other challenges are addressed in this work. The AutoDomainMine approach is used to build a computational estimation system, rigorously evaluated with real data in Materials Science. Our evaluation confirms that AutoDomainMine provides desired accuracy and efficiency in computational estimation. It is extendable to other science and engineering domains as proved by adaptation of its sub-processes within fields such as Bioinformatics and Nanotechnology.

Computer Science↗
DOE OSTI2018

Microscopic optical potentials for calcium isotopes

We construct nucleonic microscopic optical potentials by combining the Green's function approach with the coupled-cluster method for 40 Ca and 48 Ca. For the computation of the ground states of 40 Ca and 48 Ca, we use the coupled-cluster method in the singles-and-doubles approximation, while for the A = ±1 nuclei we use particle-attached and particle-removed equation-of-motion method truncated at two-particle–one-hole and one-particle–two-hole excitations, respectively. Our calculations are based on the chiral nucleon-nucleon and three-nucleon interaction NNLO sat , which reproduces the charge radii of 40 Ca and 48 Ca, and the chiral nucleon-nucleon interaction NNLO opt . In all cases considered here, we observe that the overall form of the neutron scattering cross section is reproduced for both interactions, but the imaginary part of the potential, which reflects the loss of flux in the elastic channel, is negligible. Furthermore, the latter points to neglected many-body correlations that would appear beyond the coupled-cluster truncation level considered in this work. We show that, by artificially increasing the parameter η in the Green's function, practical results can be further improved.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

The molecular dipole moment ( μ ) is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra as well as induction and long-range electrostatic interactions. Furthermore, it can be extracted directly—via the ground state electron density—from high-level quantum mechanical calculations, making it an ideal target for machine learning (ML). Here, we choose to represent this quantity with a physically inspired ML model that captures two distinct physical effects: local atomic polarization is captured within the symmetry-adapted Gaussian process regression framework which assigns a (vector) dipole moment to each atom, while the movement of charge across the entire molecule is captured by assigning a partial (scalar) charge to each atom. The resulting “MuML” models are fitted together to reproduce molecular μ computed using high-level coupled-cluster theory and density functional theory (DFT) on the QM7b dataset, achieving more accurate results due to the physics-based combination of these complementary terms. The combined model shows excellent transferability when applied to a showcase dataset of larger and more complex molecules, approaching the accuracy of DFT at a small fraction of the computational cost. We also demonstrate that the uncertainty in the predictions can be estimated reliably using a calibrated committee model. The ultimate performance of the models—and the optimal weighting of their combination—depends, however, on the details of the system at hand, with the scalar model being clearly superior when describing large molecules whose dipole is almost entirely generated by charge separation. These observations point to the importance of simultaneously accounting for the local and non-local effects that contribute to μ ; furthermore, they define a challenging task to benchmark future models, particularly those aimed at the description of condensed phases.

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

VoroClust

SAND2025-11465O VoroClust, also known as Voronoi Clustering, is a fast, density-based unsupervised clustering algorithm applicable to high-resolution and high-dimensional data. It operates as quickly as distance-based clustering methods while effectively capturing complex regional geometries, matching the performance of current density-based methods. VoroClust employs a data-centered sphere cover to reduce computational demands while preserving data topology. It propagates clusters outward from local density peaks. Although supervised machine learning is powerful for applications like image classification and segmentation, it requires comprehensive, consistent datasets, which many applications lack. Unsupervised clustering algorithms analyze the structure of each dataset rather than relying on similarities with other examples, making them well-suited for practical applications with insufficient or inappropriate data for supervised learning. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Ebeida, Mohamed [Sandia National Lab. (SNL-CA), Li↗
DOE OSTI2017

Modeling of wave processes in blocky media with porous and fluid-saturated interlayers

The wave processes in blocky media are analyzed by applying different mathematical models, wherein the elastic blocks interact with each other via pliant interlayers with the complex mechanical properties. Four versions of constitutive equations are considered. In the first version, an elastic interaction between the blocks is simulated within the framework of linear elasticity theory, and the model of elastic–plastic interlayers is constructed to take into account the appearance of irreversible deformation of interlayers at short time intervals. In the second one, the effects of viscoelastic shear in the interblock interlayers are taken into the consideration using the Poynting–Thomson rheological scheme. In the third option, the model of an elastic porous material is used in the interlayers, where the pores collapse if an abrupt compressive stress is applied. In the fourth case, the model of a fluid-saturated material with open pores is examined based on Biot's equations. The collapse of pores is modeled by the generalized rheological approach, wherein the mechanical properties of a material are simulated using four rheological elements. Three of them are the traditional elastic, viscous and plastic elements, the fourth element is the so-called rigid contact, which is used to describe the behavior of materials with the different resistance to tension and compression. It was shown that the thermodynamically consistent model is provided, which means that the energy balance equation is fulfilled for an entire blocky structure, where the kinetic and potential energy of the system is the sum of the kinetic and potential energies of the blocks and interlayers. Under numerical implementation of the interlayers models, the dissipationless finite difference Ivanov's method was used. The splitting method by spatial variables in the combination with the Godunov gap decay scheme was applied in the blocks. As a result, robust and stable computational algorithms are built and tested. Using MPI technology, the parallel software was designed for the modeling of wave processes in 2D setting. The numerical results are presented, discussed and future studies are outlined. - Highlights: • We model the propagation of waves in a blocky medium with compliant interlayers. • Models of porous and fluid saturated interlayers are proposed using Biot's approach. • A parallel computational algorithm is developed for supercomputers of cluster type. • Problems of instant rotation of central and boundary blocks are solved numerically. • Wave motions are compared in the cases of elastic and fluid-saturated interlayers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Approximating large-basis coupled-cluster theory vibrational frequencies using focal-point approximations

The focal-point approximation can be used to estimate a high-accuracy, slow quantum chemistry computation by combining several lower-accuracy, faster computations. We examine the performance of focal-point methods by combining second-order Møller–Plesset perturbation theory (MP2) with coupled-cluster singles, doubles, and perturbative triples [CCSD(T)] for the calculation of harmonic frequencies and that of fundamental frequencies using second-order vibrational perturbation theory (VPT2). In contrast to standard CCSD(T), the focal-point CCSD(T) method approaches the complete basis set (CBS) limit with only triple-ζ basis sets for the coupled-cluster portion of the computation. The predicted harmonic and fundamental frequencies were compared with the experimental values for a set of 20 molecules containing up to six atoms. Here, the focal-point method combining CCSD(T)/aug-cc-pV(T + d)Z with CBS-extrapolated MP2 has mean absolute errors vs experiment of only 7.3 cm −1 for the fundamental frequencies, which are essentially the same as the mean absolute error for CCSD(T) extrapolated to the CBS limit using the aug-cc-pV(Q + d)Z and aug-cc-pV(5 + d)Z basis sets. However, for H 2 O, the focal-point procedure requires only 3% of the computation time as the extrapolated CCSD(T) result, and the cost savings will grow for larger molecules.

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

Real-time Object Bounding in LiDAR Data With Computer Vision

The Multimodal Measurement System is a roadside radiation measurement testbed used to detect radiation sources in passing vehicles. It works by combining sensor signals from various modalities to produce a thorough scan of the source. A LiDAR sensor is used to measure the dimensions of the vehicle and provide a velocity estimate. However, the current LiDAR setup uses propriety software for which the source code is unavailable and cannot be updated to improve performance. Therefore, it is imperative to the accuracy of the analysis to create a custom vehicle detection that can return the dimensions and velocity of passing vehicles in real time. This new custom detection is written in C++ using the PointCloud Library, which keeps it lightweight. It also utilizes Docker and the Robot Operating System, which allows the versatility of running both on a small computer or the Lawrence Livermore National Laboratory cluster while utilizing different models of LiDAR sensors. The custom detection outperforms the current detection model, which increases the accuracy of radiation source detection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

SYCL for Performance Portability: Application Experience with Coupled Cluster Formalism in Quantum Chemistry on Exascale Systems

The exascale computing has brought unprecedented heterogeneity in node architectures, with systems such as Frontier and Aurora featuring diverse GPU accelerators, network connectivity among others. Ensuring performance portability across these platforms is a key challenge. To address this, we employ the SYCL programming model to develop portable, high-performance quantum chemistry workloads. As a representative application, we focus on the non-iterative Triples component of the coupled-cluster CCSD(T) method, a key driver in quantum chemistry. In this work, we report on our experience deploying SYCL-based implementations using both DPC++ and AdaptiveCPP across two flagship exascale platforms: OLCF Frontier with AMD MI250X GPUs and ALCF Aurora with Intel GPUs. Our results demonstrate that SYCL enables efficient, single-source implementations that scale to thousands of nodes, delivering performance on par with vendor-optimized HIP solutions. We highlight key insights into runtime behavior, kernel portability, and scaling characteristics, showing that SYCL offers a viable path for performance-portable computing.

Bagusetty, Abhishek [Argonne National Laboratory (↗
DOE OSTI2018

Parallel multiple-chain DRAM MCMC for large-scale geosteering inversion and uncertainty quantification

Geosteering is the proactive control of a wellbore placement based on the downhole measurements, aiming at maximizing economic production from the well. We report the recent development of azimuthal resistivity logging-whiledrilling (LWD) tools have a much larger depth of detection, thus sets higher demands for geosteering inversion as more unknown parameters need to be taken into account in the earth model to be inverted. For complicated nonlinear problems, traditional deterministic inversion methods are more likely to be trapped near local optima. In general, Markov chain Monte Carlo (MCMC) inversion methods are more capable of finding the global optimal solution and providing additional uncertainty analyses by sampling from the target distribution. However, MCMC methods usually have the problem of slow convergence. Even though optimization methods like delayed rejection (DR) and adaptive Metropolis (AM) were proposed to speed up the convergence, using MCMC methods to solve geosteering inversion problems may still incur unacceptable time cost. To reduce the sampling cost of MCMC methods, we use parallel multiple-chain DRAM MCMC methods to solve geosteering inverse problems and estimate the corresponding uncertainty. A clustering method, density-based spatial clustering of applications with noise (DBSCAN), is applied to select the best solution among many results. Lastly, the simulation results show that running many relatively short MCMC chains for one problem can obtain a similar result as running a long single chain. Besides, avoiding communications between multiple Markov chains during the sampling process yields almost linear scalability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Route to Chemical Accuracy for Computational Uranium Thermochemistry

Benchmark spinor-based relativistic coupled-cluster calculations for the ionization energies of the uranium atom, the uranium monoxide molecule (UO), and the uranium dioxide molecule (UO 2 ) and for the bond dissociation energies of UO and UO 2 are reported. The accuracy of these calculations in the treatments of relativistic, electron correlation, and basis-set effects is analyzed. Furthermore, the intrinsic convergence of the computed results and the favorable comparison with the experimental values demonstrate the unique applicability of the spinor representation of quantum-chemical methods to open-shell uranium-containing atomic and molecular species with uranium oxidation states ranging from U(0) to U(V).

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

BiGEST

Natural products have provided a rich reservoir of beneficial compounds in public health including antibiotics, therapeutics, and immunosuppressants. These natural products are synthesized by enzymes encoded by Biosynthetic Gene Clusters (BGCs), clusters of co-localized biosynthetic genes. Computational detection of BGCs has become a crucial step in natural product discovery. While this process has been facilitated in bacterial and fungal organisms thanks to the currently available tools (e.g., antiSMASH), a large spectrum of eukaryotic organisms have been neglected by these existing tools due to the scarcity and incompleteness of genome annotation resources. Here, we introduce Biosynthetic Gene cluster Extensive Search Tool (BiGEST) to provide an extensive annotation-free search for BGCs in diverse eukaryotic organisms. As a result, BiGEST uncovers eukaryotic BGCs that could be undetected by other BGC detection tools.

Adriani, Lisa↗
DOE OSTI2020

A Clustering-Based Scenario Generation Framework for Power Market Simulation with Wind Integration

A critical step in stochastic optimization models of power system analysis is to select a set of appropriate scenarios and significant numbers of scenario generation methods exist in the literature. This paper develops a clustering based scenario generation method, which aims to improve the performance of existing scenario generation techniques by grouping a set of correlated wind sites into clusters according to their cross-correlations. Copula based models are utilized to model spatiotemporal correlations and the Gibbs sampling is then used to generate scenarios for day-ahead markets. Our results show that the generated scenarios based on clustered wind sites outperform existing approaches in terms of reliability and sharpness and can reduce the total computational time for scenario generation and reduction significantly. The clustering-based framework can therefore provide a better support for real-world market simulations with high wind penetration.

data visualization↗
DOE OSTI2014

Toward a Molecular Understanding of Energetics in Li–S Batteries Using Nonaqueous Electrolytes: A High-Level Quantum Chemical Study

The Li-S battery (secondary cell or redox flow) technology is a promising future alternative to the present lithium intercalation-based energy storage and, therefore, a molecular level understanding of the chemical processes and properties such as stability of intermediates, reactivity of polysulfides and reactivity towards the non-aqueous electrolytes in the Li-S batteries is of great interest. In this paper, quantum chemical methods (G4MP2, MP2, and B3LYP) were utilized to compute reduction potentials of lithium polysulfides and polysulfide molecular clusters, energetics of disproportionation and association reactions of likely intermediates, and their reactions with ether-based electrolytes. Based on the computed reaction energetics in solution, a probable mechanism during the discharge process for polysulfide anions and lithium polysulfides in solution is proposed and likely intermediates such as S42-,S32-, S22-, and S31- radical were identified. Additionally, the stability and reactivity of propylene carbonate and tetraglyme solvent molecules were assessed against the above-mentioned intermediates and other reactive species by computing the reaction energetics required to initiate the solvent decomposition reactions in solution. Calculations suggest that the propylene carbonate molecule is unstable against the polysulfide anions such as S22-, S32-, and S42- (ΔH† < 0.8 eV) and highly reactive towards Li2S2 and Li2S3. Even though the tetraglyme solvent molecule exhibits increased stability towards polysulfide anions compared to propylene carbonate, this molecule too is vulnerable to nucleophilic attack from Li2S2 and Li2S3 species in solutions. Hence, a long- term stability of the ether molecules is unlikely if high concentration of these reactive intermediates present in the Li-S energy storage systems.

25 ENERGY STORAGE↗