Search NASA⌕ Search

SEARCH · Search NASA

Results for “Cluster Computing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

The matryoshka run: A Eulerian refinement strategy to study the statistics of turbulence in virialized cosmic structures

We study the statistical properties of turbulence driven by structure formation in a massive merging galaxy cluster at redshift z = 0. The development of turbulence is ensured as the largest eddy turnover time is much shorter than the Hubble time independent of mass and redshift. We achieve a large dynamic range of spatial scales through a novel Eulerian refinement strategy where the cluster volume is refined with progressively finer uniform nested grids during gravitational collapse. This provides an unprecedented resolution of 7.3 h {sup –1} kpc across the virial volume. The probability density functions of various velocity-derived quantities exhibit the features characteristic of fully developed compressible turbulence observed in dedicated periodic-box simulations. Shocks generate only 60% of the total vorticity within the R {sub vir}/3 region and 40% beyond that. We compute second- and third-order longitudinal and transverse structure functions for both solenoidal and compressional components in the cluster core, virial region, and beyond. The structure functions exhibit a well-defined inertial range of turbulent cascade. The injection scale is comparable to the virial radius but increases toward the outskirts. Within R {sub vir}/3, the spectral slope of the solenoidal component is close to Kolmogorov's, but for the compressional component is substantially steeper and close to Burgers's; the flow is mostly solenoidal and statistically rigorously, which is consistent with fully developed homogeneous and isotropic turbulence. Small-scale anisotropy appears due to numerical artifact. Toward the virial region, the flow becomes increasingly compressional, the structure functions become flatter, and modest genuine anisotropy appear particularly close to the injection scale. In comparison, mesh adaptivity based on Lagrangian refinement and the same finest resolution leads to a lack of turbulent power on a small scales, an excess thereof on large scales, and unreliable density-weighted structure functions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Arrested-motility states in populations of shape-anisotropic active Janus particles

The emergence of large-scale collective phenomena from simple interactions between individual units is a hallmark of active matter systems. Active colloids with alignment-dominated interparticle interactions tend to develop orientational order and form motile coherent states, such as flocks and swarms. Alternatively, a combination of self-propulsion and excluded-volume interactions results in self-trapping and active phase separation into dense clusters. Here, we reveal unconventional arrested-motility states in ensembles of active discoidal particles powered by induced-charge electrophoresis. Combining experiments and computational modeling, we demonstrate that the shape asymmetry of the particles promotes the hydrodynamically assisted formation of active particles’ bound states in a certain range of excitation parameters, ultimately leading to a spontaneous collective state with arrested motility. Unlike the jammed clusters obtained through self-trapping, the arrested-motility phase remains sparse, dynamic, and reconfigurable. The demonstrated mechanism of phase separation seeded by bound state formation in ensembles of oblate active particles is generic and should be applicable to other active colloidal systems.

42 ENGINEERING↗
DOE OSTI2017

Highly active Au/δ-MoC and Au/β-Mo 2 C catalysts for the low-temperature water gas shift reaction: effects of the carbide metal/carbon ratio on the catalyst performance

In this paper, the water gas shift (WGS) reaction catalyzed by orthorhombic β-Mo 2 C and cubic δ-MoC surfaces with and without Au clusters supported thereon has been studied by means of a combination of sophisticated experiments and state-of-the-art computational modeling. Experiments evidence the importance of the metal/carbon ratio on the performance of these systems, where Au/δ-MoC is presented as a suitable catalyst for WGS at low temperatures owing to its high activity, selectivity (only CO 2 and H 2 are detected), and stability (oxycarbides are not observed). Periodic density functional theory-based calculations show that the supported Au clusters and the Au/δ-MoC interface do not take part directly in water dissociation but their presence is crucial to switch the reaction mechanism, drastically decreasing the effect of the reverse WGS reaction and favoring the WGS products desorption, thus leading to an increase in CO 2 and H 2 production. Finally, the present results clearly display the importance of the Mo/C ratio and the synergy with the admetal clusters in tuning the activity and selectivity of the carbide substrate.

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

Computational Nuclear Physics and Post Hartree-Fock Methods

We present a computational approach to infinite nuclear matter employing Hartree-Fock theory, many-body perturbation theory and coupled cluster theory. These lectures are closely linked with those of chapters 9, 10 and 11 and serve as input for the correlation functions employed in Monte Carlo calculations in chapter 9, the in-medium similarity renormalization group theory of dense fermionic systems of chapter 10 and the Green's function approach in chapter 11. We provide extensive code examples and benchmark calculations, allowing thereby an eventual reader to start writing her/his own codes. We start with an object-oriented serial code and end with discussions on strategies for porting the code to present and planned high-performance computing facilities.

Lietz, Justin↗
DOE OSTI2015

A Novel Vertex Affinity for Community Detection

We propose a novel vertex affinity measure in this paper. The new vertex affinity quantifies the proximity between two vertices in terms of their clustering strength and is ideal for such graph analytics applications as community detection. We also developed a framework that combines simple graph searches and resistance circuit formulas to compute the vertex affinity efficiently. We study the properties of the new affinity measure empirically in comparison to those of other popular vertex proximity metrics. Our results show that the existing metrics are ill-suited for community detection due to their lack of fundamental properties that are essential for correctly capturing inter- and intra-cluster vertex proximity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Force-Free Identification of Minimum-Energy Pathways and Transition States for Stochastic Electronic Structure Theories

Here, the accurate mapping of potential energy surfaces (PESs) is crucial to our understanding of the numerous physical and chemical processes mediated by atomic rearrangements, such as conformational changes and chemical reactions, and the thermodynamic and kinetic feasibility of these processes. Stochastic electronic structure theories, e.g., Quantum Monte Carlo (QMC) methods, enable highly accurate total energy calculations that in principle can be used to construct the PES. However, their stochastic nature poses a challenge to the computation and use of forces and Hessians, which are typically required in algorithms for minimum-energy pathway (MEP) and transition state (TS) identification, such as the nudged elastic band (NEB) algorithm and its climbing image formulation. Here, we present strategies that utilize the surrogate Hessian line-search method, previously developed for QMC structural optimization, to efficiently identify MEP and TS structures without requiring force calculations at the level of the stochastic electronic structure theory. By modifying the surrogate Hessian algorithm to operate in path-orthogonal subspaces and at saddle points, we show that it is possible to identify MEPs and TSs by using a force-free QMC approach. We demonstrate these strategies via two examples, the inversion of the ammonia (NH 3 ) molecule and the nucleophilic substitution (S N 2) reaction F – + CH 3 F → FCH 3 + F – . We validate our results using Density Functional Theory (DFT)- and Coupled Cluster (CCSD, CCSD(T))-based NEB calculations. We then introduce a hybrid DFT-QMC approach to compute thermodynamic and kinetic quantities, free energy differences, rate constants, and equilibrium constants that incorporates stochastically optimized structures and their energies, and show that this scheme improves upon DFT accuracy. Our methods generalize straightforwardly to other systems and other high-accuracy theories that similarly face challenges computing energy gradients, paving the way for highly accurate PES mapping, transition state determination, and thermodynamic and kinetic calculations at significantly reduced computational expense.

Iyer, Gopal R.↗
DOE OSTI2020

Time-sliced quantum circuit partitioning for modular architectures

Current quantum computer designs will not scale. To scale beyond small prototypes, quantum architectures will likely adopt a modular approach with clusters of tightly connected quantum bits and sparser connections between clusters. We exploit this clustering and the statically-known control flow of quantum programs to create tractable partitioning heuristics which map quantum circuits to modular physical machines one time slice at a time. Specifically, we create optimized mappings for each time slice, accounting for the cost to move data from the previous time slice and using a tunable lookahead scheme to reduce the cost to move to future time slices. We compare our approach to a traditional statically-mapped, owner-computes model. Our results show strict improvement over the static mapping baseline. Here, we reduce the non-local communication overhead by 89.8% in the best case and by 60.9% on average. Our techniques, unlike many exact solver methods, are computationally tractable.

Baker, Jonathan M.↗
DOE OSTI2014

Perturbative approximation to hybrid equation of motion coupled cluster/effective fragment potential method

Hybrid quantum mechanics/molecular mechanics (QM/MM) methods provide an attractive way to closely retain the accuracy of the QM method with the favorable computational scaling of the MM method. Therefore, it is not surprising that QM/MM methods are being increasingly used for large chemical/biological systems. Hybrid equation of motion coupled cluster singles doubles/effective fragment potential (EOM-CCSD/EFP) methods have been developed over the last few years to understand the effect of solvents and other condensed phases on the electronic spectra of chromophores. However, the computational cost of this approach is still dominated by the steep scaling of the EOM-CCSD method. In this work, we propose and implement perturbative approximations to the EOM-CCSD method in this hybrid scheme to reduce the cost of EOM-CCSD/EFP. The timings and accuracy of this hybrid approach is tested for calculation of ionization energies, excitation energies, and electron affinities of microsolvated nucleic acid bases (thymine and cytosine), phenol, and phenolate.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2013

DARK MATTER SUBHALOS AND THE X-RAY MORPHOLOGY OF THE COMA CLUSTER

Structure formation models predict that clusters of galaxies contain numerous massive subhalos. The gravity of a subhalo in a cluster compresses the surrounding intracluster gas and enhances its X-ray emission. We present a simple model, which treats subhalos as slow moving and gasless, for computing this effect. Recent weak lensing measurements by Okabe et al. have determined masses of {approx}10{sup 13} M{sub Sun} for three mass concentrations projected within 300 kpc of the center of the Coma Cluster, two of which are centered on the giant elliptical galaxies NGC 4889 and NGC 4874. Adopting a smooth spheroidal {beta}-model for the gas distribution in the unperturbed cluster, we model the effect of these subhalos on the X-ray morphology of the Coma Cluster, comparing our results to Chandra and XMM-Newton X-ray data. The agreement between the models and the X-ray morphology of the central Coma Cluster is striking. With subhalo parameters from the lensing measurements, the distances of the three subhalos from the Coma Cluster midplane along our line of sight are all tightly constrained. Using the model to fit the subhalo masses for NGC 4889 and NGC 4874 gives 9.1 Multiplication-Sign 10{sup 12} M{sub Sun} and 7.6 Multiplication-Sign 10{sup 12} M{sub Sun }, respectively, in good agreement with the lensing masses. These results lend strong support to the argument that NGC 4889 and NGC 4874 are each associated with a subhalo that resides near the center of the Coma Cluster. In addition to constraining the masses and 3-d location of subhalos, the X-ray data show promise as a means of probing the structure of central subhalos.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Communication: Multireference equation of motion coupled cluster: A transform and diagonalize approach to electronic structure

The novel multireference equation-of-motion coupled-cluster (MREOM-CC) approaches provide versatile and accurate access to a large number of electronic states. The methods proceed by a sequence of many-body similarity transformations and a subsequent diagonalization of the transformed Hamiltonian over a compact subspace. The transformed Hamiltonian is a connected entity and preserves spin- and spatial symmetry properties of the original Hamiltonian, but is no longer Hermitean. The final diagonalization spaces are defined in terms of a complete active space (CAS) and limited excitations (1h, 1p, 2h, …) out of the CAS. The methods are invariant to rotations of orbitals within their respective subspaces (inactive, active, external). Applications to first row transition metal atoms (Cr, Mn, and Fe) are presented yielding results for up to 524 electronic states (for Cr) with an rms error compared to experiment of about 0.05 eV. The accuracy of the MREOM family of methods is closely related to its favorable extensivity properties as illustrated by calculations on the O{sub 2}–O{sub 2} dimer. The computational costs of the transformation steps in MREOM are comparable to those of closed-shell Coupled Cluster Singles and Doubles (CCSD) approach.

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

Analytic second derivative of the energy for density functional theory based on the three-body fragment molecular orbital method

Analytic second derivatives of the energy with respect to nuclear coordinates have been developed for spin restricted density functional theory (DFT) based on the fragment molecular orbital method (FMO). The derivations were carried out for the three-body expansion (FMO3), and the two-body expressions can be obtained by neglecting the three-body corrections. Also, the restricted Hartree-Fock (RHF) Hessian for FMO3 can be obtained by neglecting the density-functional related terms. In both the FMO-RHF and FMO-DFT Hessians, certain terms with small magnitudes are neglected for computational efficiency. The accuracy of the FMO-DFT Hessian in terms of the Gibbs free energy is evaluated for a set of polypeptides and water clusters and found to be within 1 kcal/mol of the corresponding full (non-fragmented) ab initio calculation. The FMO-DFT method is also applied to transition states in S{sub N}2 reactions and for the computation of the IR and Raman spectra of a small Trp-cage protein (PDB: 1L2Y). Some computational timing analysis is also presented.

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

Logical gates embedding in artificial spin ice

The realization and study of arrays of interacting magnetic nanoislands, such as artificial spin ices, have reached mature levels of control that allow design and demonstration of exotic, collective behaviors not seen in natural materials. Advances in the direct manipulation of their local, binary moments also suggest a use as nanopatterned, interacting memory media, for computation within a magnetic memory. Recent experimental work has demonstrated the possibility of building logic gates from clusters of interacting magnetic domains, and yet the possibility of large scale integration of such gates can prove problematic even at the theoretical level. Here we introduce theoretically complete sets of logical gates, in principle realizable in an experiment, and we study the feasibility of their integration into tree-like circuits. By evaluating the fidelity control parameter between their collective behavior and their expected logic functionality we determine conditions for integration. Also, we test our numerical results against the presence of disorder in the couplings, showing that the design gate structure is robust to small coupling perturbations, and thus possibly to small imperfections in the fabrication of the islands.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Machine learning pipeline to predict defect behavior in metallic alloy systems

The interaction between defect and solute atoms is critical to the thermodynamic and kinetic behavior of metallic alloys under exposure to high-energy radiation, causing irradiation damage in materials. Radiation can generate non-equilibrium concentrations of point defects such as vacancies and interstitials. The excess point defects not only accelerate diffusional processes such as precipitation that cause radiation embrittlement, but also change the pathway of phase transformations, including nucleation processes. Understanding these defect behaviors is complicated by the challenge and complexity of addressing each possible local and discrete distribution of environments and chemical interactions around targeted defects-solute or solute-solute complexes. To resolve the challenge, machine learning regression techniques have emerged as powerful tools that can train and construct an energy model to accurately describe the chemical interactions of solutes and defects. In Fiscal Year 2022, the work focused on the workflow development and demonstration using machine learning regression, density functional theory, cluster expansion, and Monte Carlo simulation to predict the effects of ternary solute elements (e.g., aluminum and molybdenum) and point defects on the Cr-rich $\alpha^{\prime}$ precipitation in multicomponent FeCr model alloys. The computational outcomes include the prediction of the ternary phase diagram, vacancy formation energy for different compositions, and the effect of vacancies on the nucleation of Cr-rich clusters. The simulations predict a pronounced change of Cr solubility in bcc Fe by the addition of Al and the rejection of Al atoms from $\alpha^{\prime}$ precipitates. Additionally, the simulations show the formation of Cr-vacancy clusters as the initial nuclei for stable nucleation and growth of $\alpha^{\prime}$ particles. The results demonstrate important outcomes and applications of using machine learning pipeline to study model or commercial alloys with multicomponent solute species and point defects.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Predicting Atomistic Transitions with Transformers

Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation techniques used to find these transitions are extremely computationally intensive. Even with large-scale, accelerated material simulations, the computational cost constrains the applicable domain in practice. Machine learning models, with the potential to learn the complex emergent behaviors governing atomistic transitions as a fast surrogate model, have great promise to predict transitions with a vastly reduced computational cost. Here, we demonstrate how transformers can be trained to predict atomistic transitions in nano-clusters. We show how we evaluate physical validity of the predictions and how a multitude of additional, different microstates can be generated by slightly varying the data provided to the model.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Leveraging Computational Storage Devices in Campaign Storage [Slides]

Computational storage provides new ways of accelerating data-intensive applications. In-drive data management schemes matter (O_DIRECT, clustered index). Layer violation: “cheating” one filesystem may be possible; cheating multiple layers of filesystems is hard (FS internal load balancing, fail over, compression, concurrency control). The future directions include block-based acceleration to object-based acceleration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Homogeneous ice nucleation in an ab initio machine-learning model of water

Molecular simulations have provided valuable insight into the microscopic mechanisms underlying homogeneous ice nucleation. While empirical models have been used extensively to study this phenomenon, simulations based on first-principles calculations have so far proven prohibitively expensive. Here, we circumvent this difficulty by using an efficient machine-learning model trained on density-functional theory energies and forces. We compute nucleation rates at atmospheric pressure, over a broad range of supercoolings, using the seeding technique and systems of up to hundreds of thousands of atoms simulated with ab initio accuracy. The key quantity provided by the seeding technique is the size of the critical cluster (i.e., a size such that the cluster has equal probabilities of growing or melting at the given supersaturation), which is used together with the equations of classical nucleation theory to compute nucleation rates. We find that nucleation rates for our model at moderate supercoolings are in good agreement with experimental measurements within the error of our calculation. We also study the impact of properties such as the thermodynamic driving force, interfacial free energy, and stacking disorder on the calculated rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗