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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

sOPTICS: a modified density-based algorithm for identifying galaxy groups/clusters and brightest cluster galaxies

A direct approach to studying the galaxy–halo connection is to analyse groups and clusters of galaxies that trace the underlying dark matter haloes, emphasizing the importance of identifying galaxy clusters and their associated brightest cluster galaxies (BCGs). In this work, we test and propose a robust density-based clustering algorithm that outperforms the traditional Friends-of-Friends (FoF) algorithm in the currently available galaxy group/cluster catalogues. Our new approach is a modified version of the Ordering Points To Identify the Clustering Structure (OPTICS) algorithm, which accounts for line-of-sight positional uncertainties due to redshift space distortions by incorporating a scaling factor, and is thereby referred to as sOPTICS. When tested on both a galaxy group catalogue based on semi-analytic galaxy formation simulations and observational data, our algorithm demonstrated robustness to outliers and relative insensitivity to hyperparameter choices. In total, we compared the results of eight clustering algorithms. The proposed density-based clustering method, sOPTICS, outperforms FoF in accurately identifying giant galaxy clusters and their associated BCGs in various environments with higher purity and recovery rate, also successfully recovering 115 BCGs out of 118 reliable BCGs from a large galaxy sample. Furthermore, when applied to an independent observational catalogue without extensive re-tuning, sOPTICS maintains high recovery efficiency, confirming its flexibility and effectiveness for large-scale astronomical surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Genomic Functionalization: The Next Revolution In Biology

We have implemented a ligand-alignment algorithm into our developed computational pipeline for identifying specificity-determining features (SDFs) in protein-ligand complexes. Given a set of protein-ligand complex structures, the algorithm aligns the complexes by ligand rather than by the C -RMSD or standard approach, providing a single reference frame for extracting SDFs. We anticipate that this ligand-alignment capability will be highly useful for protein function prediction. We already have a database containing > 20 K ligand-protein complex crystal structures taken from the Protein Data Bank. By aligning these proteins to single reference frames using ligand alignment, we can submit the complexes to our pipeline for SDF extraction. The SDFs derived from this training procedure can be used as thumbprints that are hallmarks of individual enzyme classes. These SDF thumbprints may then serve as guides to the prediction of function of new unknown proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A three-dimensional reconstruction algorithm for scanning transmission electron microscopy data from thick samples

Increasing interest in three-dimensional nanostructures adds impetus to electron microscopy techniques capable of imaging at or below the nanoscale in three dimensions. We present a reconstruction algorithm that takes as input a focal series of four-dimensional scanning transmission electron microscopy (4D-STEM) data and transcends the prevalent structure retrieval algorithm assumption of a very thin specimen homogenous along the optic axis. We demonstrate this approach by reconstructing the different layers of a lead iridate (Pb$_2$Ir$_2$O$_7$) and yttrium-stabilized zirconia (Y$_{0.095}$Zr$_{0.905}$O$_2$) heterostructure from data acquired with the specimen in a single plan-view orientation, with the epitaxial layers stacked along the beam direction.

Brown, Hamish G↗
DOE OSTI2022

Noisy intermediate-scale quantum algorithms

A universal fault-tolerant quantum computer that can efficiently solve problems such as integer factorization and unstructured database search requires millions of qubits with low error rates and long coherence times. While the experimental advancement toward realizing such devices will potentially take decades of research, noisy intermediate-scale quantum (NISQ) computers already exist. These computers are composed of hundreds of noisy qubits, i.e., qubits that are not error corrected, and therefore perform imperfect operations within a limited coherence time. In the search for achieving quantum advantage with these devices, algorithms have been proposed for applications in various disciplines spanning physics, machine learning, quantum chemistry, and combinatorial optimization. The overarching goal of such algorithms is to leverage the limited available resources to perform classically challenging tasks. In this review, a thorough summary of NISQ computational paradigms and algorithms is provided. The key structure of these algorithms and their limitations and advantages are discussed. Finally, a comprehensive overview of various benchmarking and software tools useful for programming and testing NISQ devices is additionally provided.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Explicit structure-preserving geometric particle-in-cell algorithm in curvilinear orthogonal coordinate systems and its applications to whole-device 6D kinetic simulations of tokamak physics

Explicit structure-preserving geometric particle-in-cell (PIC) algorithm in curvilinear orthogonal coordinate systems is developed. The work reported represents a further development of the structure-preserving geometric PIC algorithm achieving the goal of practical applications in magnetic fusion research. The algorithm is constructed by discretizing the field theory for the system of charged particles and electromagnetic field using Whitney forms, discrete exterior calculus, and explicit non-canonical symplectic integration. In addition to the truncated infinitely dimensional symplectic structure, the algorithm preserves exactly many important physical symmetries and conservation laws, such as local energy conservation, gauge symmetry and the corresponding local charge conservation. As a result, the algorithm possesses the long-term accuracy and fidelity required for first-principles-based simulations of the multiscale tokamak physics. The algorithm has been implemented in the SymPIC code, which is designed for high-efficiency massively-parallel PIC simulations in modern clusters. The code has been applied to carry out whole-device 6D kinetic simulation studies of tokamak physics. A self-consistent kinetic steady state for fusion plasma in the tokamak geometry is numerically found with a predominately diagonal and anisotropic pressure tensor. The state also admits a steady-state sub-sonic ion flow in the range of 10 km s -1 , agreeing with experimental observations and analytical calculations Kinetic ballooning instability in the self-consistent kinetic steady state is simulated. It is shown that high-n ballooning modes have larger growth rates than low-n global modes, and in the nonlinear phase the modes saturate approximately in 5 ion transit times at the 2% level by the E × B flow generated by the instability. These results are consistent with early and recent electromagnetic gyrokinetic simulations.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

Field theory and a structure-preserving geometric particle-in-cell algorithm for drift wave instability and turbulence

A field theory and the associated structure-preserving geometric particle-in-cell (PIC) algorithm are developed to study low frequency electrostatic perturbations with fully kinetic ions and adiabatic electrons in magnetized plasmas. The algorithm is constructed by geometrically discretizing the field theory using discrete exterior calculus, high-order Whitney interpolation forms, and non-canonical Hamiltonian splitting method. The discretization preserves the non-canonical symplectic structure of the particle-field system, as well as the electromagnetic gauge symmetry. As a result, the algorithm is charge-conserving and possesses long-term conservation properties. Because drift wave turbulence and anomalous transport intrinsically involve multi time-scales, simulation studies using fully kinetic particles demand algorithms with long-term accuracy and fidelity. The structure-preserving geometric PIC algorithm developed adequately serves this purpose. The algorithm has been implemented in the code, tested and benchmarked using the examples of ion Bernstein waves and drift waves. We apply the algorithm to study the ion temperature gradient (ITG) instability and turbulence in a 2D slab geometry. Simulation results show that at the early stage of the turbulence, the energy diffusion is between the Bohm scaling and gyro-Bohm scaling. Finally, at later time, the observed diffusion is closer to the gyro-Bohm scaling, and density blobs generated by the rupture of unstable modes are the prominent structures of the fully developed ITG turbulence.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Higher-Order, Space-Time Adaptive Finite Volume Methods: Algorithms, Analysis and Applications

The four main goals outlined in the proposal for this project were: 1. Investigate the use of higher-order (in space and time) finite-volume methods for fluid flow problems. 2. Explore the embedding of iterative temporal methods within traditional block-structured AMR algorithms. 3. Develop parallel in time methods for ODEs and PDEs. 4. Work collaboratively with the Center for Computational Sciences and Engineering (CCSE) at Lawrence Berkeley National Lab towards incorporating new algorithms within existing DOE application codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

An adaptive extended finite element method for the analysis of agglomeration of colloidal particles in a flowing fluid

Direct numerical simulations of the flow-nanoparticle interaction in a colloidal suspension are presented using an extended finite element method (XFEM) in which the dynamics of the nanoparticles is solved in a fully-coupled manner with the flow. The method is capable of accurately describing solid-fluid interfaces without the need of boundary-fitted meshes to investigate the dynamics of particles in complex flows. In order to accurately compute the high interparticle shear stresses and pressures while minimizing computing costs, an adaptive meshing technique is incorporated with the fluid-structure interaction algorithm. The particle-particle interaction at the microscopic level is modeled using the Lennard-Jones (LJ) potential and the corresponding potential parameters are determined by a scaling procedure. The study is relevant to the preparation of inks used in the fabrication of catalyst layers for fuel cells. In this paper, we are particularly interested in investigating agglomeration of the nanoparticles under external shear flow in a sliding bi-periodic Lees-Edwards frame. The results indicate that the external shear has a crucial impact on the structure formation of colloidal particles in a suspension.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Discrete gravity with local Lorentz invariance

A novel structure-preserving algorithm for general relativity in vacuum is derived from a lattice gauge theoretic discretization of the tetradic Palatini action. Here, the resulting model of discrete gravity is demonstrated to preserve local Lorentz invariance and symplectic structure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

PUMIPic: A mesh-based approach to unstructured mesh Particle-In-Cell on GPUs

Unstructured mesh particle-in-cell, PIC, simulations executing on the current and next generation of massively parallel systems require new methods for both the mesh and particles to achieve performance and scalability on GPUs. The traditional approach to implementing PIC simulations defines data structures and algorithms in terms of particles with a full copy of the unstructured mesh on every process. To effectively scale the unstructured mesh and particles, mesh-based PIC uses the unstructured mesh as the predominant data structure with the particles stored in terms of the mesh entities. Here, this paper details the PUMIPic library, a framework for developing efficient and performance-portable mesh-based PIC simulations on GPU systems. A pseudo physics simulation based on a five-dimensional gyro-kinetic code for modeling plasma physics is used to examine the performance of PUMIPic. Scaling studies of the unstructured mesh partition and number of particles are performed up to 4096 nodes of the Summit system at Oak Ridge National Laboratory. The studies show that mesh-based PIC can utilize a partitioned mesh and maintain scaling up to system limitations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Curved-line search algorithm for ab initio atomic structure relaxation

Ab initio atomic relaxations often take large numbers of steps and long times to converge, especially when the initial atomic configurations are far from the local minimum or there are curved and narrow valleys in the multidimensional potentials. An atomic relaxation method based on on-the-flight force learning and a corresponding curved-line search algorithm is presented to accelerate this process. In this work, results demonstrate the superior performance of this method for metal and magnetic clusters when compared with the conventional conjugate-gradient method.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

GBOpt: Grain boundary structure optimization using Monte Carlo and evolutionary algorithms

Polycrystalline materials are made of many small crystals separated by grain boundaries (GBs), whose atomic structure strongly influences material properties. Because the structure of a GB determines its properties, the optimal structure must be known in order to determine those impacts. There are many ways of placing atoms in the GB region, but the optimal structure is defined as the one that gives the lowest value of a target property (typically energy). GB structure optimization has been successfully demonstrated using stochastic and evolutionary methods, but no reusable, community-maintained open-source workflow has been developed. GBOpt (Grain Boundary Optimization) is an open-source Python package that creates that workflow, where we have presently implemented two approaches: Markov Chain Monte Carlo, and genetic algorithm based on elite selection. We demonstrate this capability by successfully reproducing the known optimal structures of a specific GB in two materials, and point interested readers to the GitHub repository for additional examples, including optimization for different properties. Both of the implemented approaches recovered the known structures, with the genetic algorithm approach finding the optimal structure faster on average.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

MCS+: An Efficient Algorithm for Crawling the Community Structure in Multiplex Networks

In this article, we consider the problem of crawling a multiplex network to identify the community structure of a layer-of-interest. A multiplex network is one where there are multiple types of relationships between the nodes. In many multiplex networks, some layers might be easier to explore (in terms of time, money etc.). We propose MCS+, an algorithm that can use the information from the easier to explore layers to help in the exploration of a layer-of-interest that is expensive to explore. We consider the goal of exploration to be generating a sample that is representative of the communities in the complete layer-of-interest. This work has practical applications in areas such as exploration of dark (e.g., criminal) networks, online social networks, biological networks, and so on. For example, in a terrorist network, relationships such as phone records, e-mail records, and so on are easier to collect; in contrast, data on the face-to-face communications are much harder to collect, but also potentially more valuable. We perform extensive experimental evaluations on real-world networks, and we observe that MCS+ consistently outperforms the best baseline—the similarity of the sample that MCS+ generates to the real network is up to three times that of the best baseline in some networks. We also perform theoretical and experimental evaluations on the scalability of MCS+ to network properties, and find that it scales well with the budget, number of layers in the multiplex network, and the average degree in the original network.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Nonintuitive Surface Self-Assembly of Functionalized Molecules on Ag(111)

The fabrication of nanomaterials involves selfordering processes of functional molecules on inorganic surfaces. To obtain specific molecular arrangements, a common strategy is to equip molecules with functional groups. However, focusing on the functional groups alone does not provide a comprehensive picture. Especially at interfaces, processes that govern self-ordering are complex and involve various physical and chemical effects, often leading to unexpected structures, as we showcase here on the example of a homologous series of quinones on Ag(111). Naively, one could expect that such quinones, which all bear the same functionalization, form similar motifs. In salient contrast, our joint theoretical and experimental study shows that profoundly different structures are formed. Using a machine-learning-based structure search algorithm, we find that this is due to a shift of the balance of three antagonizing driving forces: adsorbate-substrate interactions governing adsorption sites, adsorbate-adsorbate interactions favoring close packing, and steric hindrance inhibiting certain otherwise energetically beneficial molecular arrangements. The theoretical structures show excellent agreement with our experimental characterizations of the organic/ inorganic interfaces, both for the unit cell sizes and the orientations of the molecules within. The nonintuitive interplay of similarly important interaction mechanisms will continue to be a challenging aspect for the design of functional interfaces. With a detailed examination of all driving forces, we are, however, still able to devise a design principle for self-assembly of functionalized molecules.

structure prediction↗
DOE OSTI2015

Influence of molecular clustering on the interpretation of diffractograms of hydrocarbon films from tokamak T-10

The influence of molecular clustering on the formerly suggested interpretation of diffraction patterns of hydrocarbon films formed in the vacuum vessel of the tokamak T-10 is analyzed numerically. The simulation of clustering of simple hydrocarbon molecules C(D, H){sub 4}, C{sub 2}(D, H){sub 4}, and C{sub 6}(D, H){sub 6} and molecules composed of curved graphene (fullerenes and toroidal nanotubes) is carried out with the rigid body molecular dynamics method. It is shown that formerly neglected atomic correlations C–C and C–D(H) in the amorphous hydrocarbon component decrease the calculated values of the scattered intensity in the range of scattering vector modulus 5 < q < 20 nm{sup –1} because of homogenization of scatters on the spatial scale of ∼1 nm. The allowance for these correlations does not change the diffraction patterns in the range q > 20 nm{sup –1}. The results suggest the necessity to introduce to the procedure of determining the structural content of the films, similar to those from the tokamak T-10, the clusters formed by the van der Waals adhesion of hydrocarbon molecules to “graphene” nanoparticles. This simplifies the mathematical optimization to the former level of complexity—but for an extended ensemble of objects—and makes it possible to calculate the diffraction patterns of these objects using the distributed computing resources. A modified algorithm of structural content identification on the basis of joint X-ray and neutron diffractometry is suggested.

36 MATERIALS SCIENCE↗
DOE OSTI2019

SUSPECT: MINLP special structure detector for Pyomo

We present SUSPECT, an open source toolkit that symbolically analyzes mixed-integer nonlinear optimization problems formulated using the Python algebraic modeling library Pyomo. We present the data structures and algorithms used to implement SUSPECT. SUSPECT works on a directed acyclic graph representation of the optimization problem to perform: bounds tightening, bound propagation, monotonicity detection, and convexity detection. We show how the tree-walking rules in SUSPECT balance the need for lightweight computation with effective special structure detection. SUSPECT can be used as a standalone tool or as a Python library to be integrated in other tools or solvers. Here, we highlight the easy extensibility of SUSPECT with several recent convexity detection tricks from the literature. We also report experimental results on the MINLPLib 2 dataset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Low-Energy Phases of Bi Monolayer Predicted by Structure Search in Two Dimensions

Here we employ an ab-initio structure search algorithm to explore the configurational space of bismuth in quasi-two dimensions. A confinement potential is introduced to restrict the movement of atoms within a predefined thickness to find the stable and metastable forms of monolayer Bi. In addition to the two known low-energy structures (puckered monoclinic and buckled hexagonal), our calculations predict three new phases:α, β, and γ. Each phase exhibits peculiar electronic properties, ranging from metallic (αandγ)to semiconducting (puckered monoclinic, buckled hexagonal, and β) monolayers. Topologically nontrivial features are predicted for buckled hexagonal andγphases. We also remark on the role of 5d electrons on the electronic properties of Bi monolayer. We conclude that Bi provides a rich playground to study distortion-mediated metal-insulator phase transitions in quasi-2D.

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

SPACE: 3D parallel solvers for Vlasov-Maxwell and Vlasov-Poisson equations for relativistic plasmas with atomic transformations

A parallel, relativistic, three-dimensional particle-in-cell code SPACE has been developed for the simulation of electromagnetic fields, relativistic particle beams, and plasmas. In addition to the standard second-order Particle-in-Cell (PIC) algorithm, SPACE includes efficient novel algorithms to resolve atomic physics processes such as multi-level ionization of plasma atoms, recombination, and electron attachment to dopants in dense neutral gases. SPACE also contains a highly adaptive particle-based method, called Adaptive Particle-in-Cloud (AP-Cloud), for solving the Vlasov-Poisson problems. It eliminates the traditional Cartesian mesh of PIC and replaces it with an adaptive octree data structure. The code's algorithms, structure, capabilities, parallelization strategy, and performance have been discussed. Additionally, typical examples of SPACE applications to accelerator science and engineering problems are described.

43 PARTICLE ACCELERATORS↗