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DOE OSTI2024

Nitrogen Vacancy Centers in Diamond for Stress Sensing Applications: Theory and Experiments

Sensing of stress under high elevated environmental conditions with high resolution has critical importance for range of applications including earth’s subsurface scanning and geological CO2 storage monitoring. Using first principles density functional theory (DFT) approach combined with the theoretical modelling of low energy Hamiltonian, here we investigate a novel approach to detect unprecedented level of pressure by taking advantage of solid-state electron’s spin of Nitrogen vacancy (NV) centers in nanodiamond. We computationally explore the effect of strain on the defect band edges and band gaps by varying the lattice parameters of diamond supercell hosting a single NV center. A low energy Hamiltonian is developed that includes effect of stress on the energy level of ±1 spin manifold at the ground state. By quantifying the energy level shift and split, we predict the pressure sensing of up to 0.3 MPa/√Hz. We show superiority of the quantum sensing over traditional optical sensing techniques by discussing our results from DFT and theoretical modelling for the frequency shift per unit pressure. The proposed quantum stress sensing could be useful to measure earth’s subsurface vibrations. Our results open avenues for development of sensing technology with a high sensitivity and resolution under extreme pressure conditions that potentially has a wider applicability than existing pressure sensing technologies.

Paudel, Hari P.↗

DOE OSTI2020

ConnectIt: a framework for static and incremental parallel graph connectivity algorithms

Connected components is a fundamental kernel in graph applications. The fastest existing multicore algorithms for solving graph connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting. We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 12.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. To guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies. Finally, we show how the techniques in ConnectIt can be used to speed up two important graph applications: approximate minimum spanning forest and SCAN clustering.

Computer Science↗

DOE OSTI2024

Graph states of atomic ensembles engineered by photon-mediated entanglement

Abstract Graph states are a broad family of entangled quantum states, each defined by a graph composed of edges representing the correlations between subsystems. Such states constitute versatile resources for quantum computation and quantum-enhanced measurement. Their generation and engineering require a high level of control over entanglement. Here we report on the generation of continuous-variable graph states of atomic spin ensembles, which form the nodes of the graph. We program the entanglement structure encoded in the graph edges by combining global photon-mediated interactions in an optical cavity with local spin rotations. By tuning the entanglement between two subsystems, we either localize correlations within each subsystem or enable Einstein–Podolsky–Rosen steering—a strong form of entanglement that enables the extraction of precise information from one subsystem through measurements on the other. We further engineer a four-mode square graph state, highlighting the flexibility of our approach. Our method is scalable to larger and more complex graphs, laying groundwork for measurement-based quantum computation and advanced protocols in quantum metrology.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2020

Proximity-induced superconducting gap in the quantum spin Hall edge state of monolayer WTe2

The quantum spin Hall insulator is characterized by a bandgap in the two-dimensional (2D) interior and helical 1D edge states 1 , 2 , 3 . Inducing superconductivity in the helical edge state results in a 1D topological superconductor, a highly sought-after state of matter at the core of many proposals for topological quantum computing 4 . In the present study, we report the coexistence of superconductivity and the quantum spin Hall edge state in a van der Waals heterostructure, by placing a monolayer of 1T'-WTe 2 , a quantum spin Hall insulator 1 , 2 , 3 , on a van der Waals superconductor, NbSe 2 . Using scanning tunnelling microscopy and spectroscopy (STM/STS), we demonstrate that the WTe 2 monolayer exhibits a proximity-induced superconducting gap due to the underlying superconductor and that the spectroscopic features of the quantum spin Hall edge state remain intact. Taken together, these observations provide conclusive evidence for proximity-induced superconductivity in the quantum spin Hall edge state in WTe 2 , a crucial step towards realizing 1D topological superconductivity and Majorana bound states in this van der Waals material platform.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Scalable edge clustering of dynamic graphs via weighted line graphs

Timestamped relational datasets consisting of records (or connections) between pairs of entities are ubiquitous in network science. For applications like peer-to-peer communication, email, various social network interactions, and computer network security, it is useful to organize these records into groups based on how and when they are occurring. Weighted line graphs offer a natural way to model how records are related in such datasets but for large real-world graph topologies, building and utilizing the line graph is prohibitively expensive. Here, we present the framework to cluster the edges of a dynamic graph via the associated line graph that contains two major contributions. The first is a method to work with the line graph implicitly and the second is a distributed scale implementation of an agglomerative hierarchical graph clustering algorithm. We outline a novel hierarchical dynamic graph edge clustering approach that efficiently breaks massive relational datasets into small sets of edges containing events at various timescales. This is in stark contrast to traditional graph clustering algorithms that prioritize highly connected (clique-like) community structures. Our approach relies on constructing a sufficient subgraph of a weighted line graph and applying a hierarchical agglomerative clustering. This approach is related to scalable techniques from spatial clustering, nonlinear-dimension reduction, topological data analysis, and draws particular inspiration from HDBSCAN. As an edge clustering, this method yields an overlapping node clustering. Our algorithm is parallelizable and we demonstrate efficient clustering of a billion-scale, real-world dynamic graph into small edge sets that correlate in topology and time. The entire clustering process for a graph with tens of billions of edges takes just a few minutes of run time on 256 nodes of a distributed compute environment. We argue how the output of the edge clustering is useful for a multitude of data visualization and powerful machine learning tasks, both involving the original massive dynamic graph data and metadata associated with the nodes and edges. Finally, we describe how this approach can be extended to dynamic hypergraphs and dynamic graphs/hypergraphs with unstructured data living on vertices and edges.

Data Analysis↗

DOE OSTI2025

Core-edge integrated predictive studies of ST40 and NSTX plasmas with the scrape-off layer box model

The ability to model the interplay between the core and edge of tokamak plasmas is crucial to designing both the plasma operating scenario of a fusion pilot plant and the design of the tokamak itself. Scrape-off-layer (SOL) models that are tailored to integrated scenario modeling need to have fast turn-around time and minimal computational burden to enable wide parameter-space coverage for design scoping. The SOL 0-D Box model is a reduced SOL model based on global power and particle balance that captures the essential physics of SOL transport with little computational cost. The usage of the 0-D Box model in core-edge coupled simulations has been demonstrated in both interpretive and predictive modes on a variety of devices. This paper presents a sensitivity study of the 0-D Box model to the input SOL heat-flux width for an ST40 plasma. This study demonstrates that accurate prediction of this width is crucial to predicting global performance parameters of a plasma scenario, such as energy confinement time and flux consumption. We also present an extension of the Box model to 1-D to allow for parallel variation of plasma parameters along the magnetic field lines. The 1-D Box model is then compared with SOLPS-ITER simulations of an NSTX plasma. Advantages and limitations of the Box model are discussed, and future directions are outlined.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

BCSR on GPU: A Way Forward Extreme-scale Graph Processing on Accelerator-enabled Frontier Supercomputer

Handling large graphs in a distributed environment requires effective partitioning across processors and efficient management of local partitions. In 2D partitioning, local graphs often become too sparse, making memory-efficient data structures crucial. Using the Compressed Sparse Row (CSR) format wastes space, especially for > 83% of vertices with empty edges for the sparse graphs. This study explores bit-CSR (BCSR), a modified CSR representation, on GPUs to reduce memory usage in graph computations. We achieved 16.67% memory savings on a sparse rmat dataset with 268 million vertices and 357 million edges, without performance degradation, supported by both theoretical and experimental storage savings of 33%. However, we observed a 1.7× slowdown in degree lookup times due to bitwise operations on AMD CPUs. This analysis highlights the potential of BCSR on GPUs for improving Graph500 benchmark performance on GPU-accelerated systems, such as the Frontier supercomputer.

Sattar, Naw Safrin↗

DOE OSTI2020

Gauges, loops, and polynomials for partition functions of graphical models

Graphical models represent multivariate and generally not normalized probability distributions. Computing the normalization factor, called the partition function, is the main inference challenge relevant to multiple statistical and optimization applications. The problem is #P-hard that is of an exponential complexity with respect to the number of variables. Here, aimed at approximating the partition function, we consider multi-graph models where binary variables and multivariable factors are associated with edges and nodes, respectively, of an undirected multi-graph. We suggest a new methodology for analysis and computations that combines the Gauge function technique from Chertkov and Chernyak with the technique developed in Anari and Oveis Gharan 2017 arXiv:1702.02937; Gurvits 2011 arXiv:1106.2844; Straszak and Vishnoi 2017 55th Annual Allerton Conf. on Communication, Control, and Computing, based on the recent progress in the field of real stable polynomials. We show that the Gauge function, representing a single-out term in a finite sum expression for the partition function which achieves extremum at the so-called belief-propagation gauge, has a natural polynomial representation in terms of gauges/variables associated with edges of the multi-graph. Moreover, Gauge function can be used to recover the partition function through a sequence of transformations allowing appealing algebraic and graphical interpretations. Algebraically, one step in the sequence consists of the application of a differential operator over gauges associated with an edge. Graphically, the sequence is interpreted as a repetitive elimination/contraction of edges resulting in multi-graph models on decreasing in size (number of edges) graphs with the same partition function as in the original multi-graph model. Even though the complexity of computing factors in the sequence of the derived multi-graph models and respective Gauge functions grow exponentially with the number of eliminated edges, polynomials associated with the new factors remain bi-stable if the original factors have this property. Moreover, we show that BP estimations in the sequence do not decrease, each low-bounding the partition function.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

High‐Throughput Computational Guided Development of Refractory Complex Concentrated Alloys‐based Composite

ULTIMATE is a leading-edge DOE program to develop ultrahigh temperature materials for gas turbine use in the aviation and power generation industries. This team, headquartered at West Virginia University and including collaborators from the National Energy Technology Laboratory and Advanced Manufacturing LLC, has developed a new class of ultra-high temperature Refractory Complex Concentrated Alloys-based Composites (RCCC) for high temperature applications such as combustion turbines used in the aerospace and energy industries. The RCCC consist of Refractory Complex Concentrated Alloys (RCCA) mixed with particles of Refractory High Entropy Carbides, to increase RCCA strength to withstand extreme conditions. These new materials optimize the balance among strength, creep (deformation), density, and stability at 1300 °C (2372 °F), while maintaining ductility once the alloy cools to room temperature. The research team has developed advanced manufacturing processes using the pulsed electric current and laser 3D printing to produce test coupons of these materials.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Streaming Matching and Edge Cover in Practice

Graph algorithms with polynomial space and time requirements often become infeasible for massive graphs with billions of edges or more. State-of-the-art approaches therefore employ approximate serial, parallel, and distributed algorithms to tackle these challenges. However, such approaches require storing the entire graph in memory and thus need access to costly computing resources such as clusters and supercomputers. In this paper, we present practical streaming approaches for solving massive graph problems using limited memory for two prototypical graph problems: maximum weighted matching and minimum weighted edge cover. For matching, we conduct a thorough computational study on two of the semi-streaming algorithms including a recent breakthrough result that achieves a $1/(2+\varepsilon)$-approximation of the weight while using $O( n \log W /\epsilon)$ memory (here $n$ is the number of vertices and $W$ is the maximum edge weight), designed by Paz and Schwartzman [SODA, 2017]. Empirically, we show that the semi-streaming algorithms produce matchings whose weight is close to the best $1/2$-approximate offline algorithm while requiring less time and an order-of-magnitude less memory. For minimum weighted edge cover, we develop three novel semi-streaming algorithms. Two of these algorithms require a single pass through the input graph, require $O(n \log n)$ memory, and provide a 2-approximation guarantee on the objective. We also leverage a relationship between approximate maximum weighted matching and approximate minimum weighted edge cover to develop a two-pass $3/2+\epsilon$-approximate algorithm with the memory requirement of Paz and Schwartzman's semi-streaming matching algorithm. These streaming approaches are compared against the state-of-the-art 3/2-approximate offline algorithm. The semi-streaming matching and the novel edge cover algorithms proposed in this paper can process graphs with several billions of edges in under 30 minutes using 6 GB of memory, which is at least an order of magnitude improvement from the offline (non-streaming) algorithms. For the largest graph, the best alternative offline parallel approximation algorithm (GPA+ROMA) could not finish in three hours even while employing hundreds of processors and 1 TB of memory. We also demonstrate an application of the semi-streaming algorithm by computing a matching using linearly bounded memory on item intersection graphs derived from three machine learning datasets, whereas the existing offline algorithms could not complete on one of these datasets since their memory requirements exceeded 1TB.

Ferdous, S M.↗

DOE OSTI2026

Reflections on the Shifting Experiences of Scientific Infrastructure

Infrastructure of all types is fundamental to modern work and life. Computing for scientific work, especially, extends from distributed local research sites, often at the edges of other major systems, outward into globally connected high-performance facilities and infrastructures. This commentary reviews longstanding research on the social characteristics of infrastructure. We reflect on social concerns that affect the ongoing development, use, and maintenance of a wide range of scientific computing and data resources. Reflecting on the social nature of infrastructure is timely for Computing in Science & Engineering readers, given continued emphasis on developing even more expansive platforms for data and artificial intelligence work in science (e.g., the United States’ Genesis Mission). We assert that, regardless of technological advances, the complex nature of scientific research and data will require continued understanding of longstanding and nascent social practices across varied communities. This is fundamentally necessary to build and sustain usable infrastructure or platforms that can productively advance scientific research.

Paine, Drew [Lawrence Berkeley National Laboratory↗

DOE OSTI2022

ECP-ExaGraph/Submodular-b-matching

A b-MATCHING is a subset of edges M such that at most b(v) edges in M are incident on each vertex v, where b(v) is specified. We present a distributed-memory parallel algorithm, b-SUITOR, that computes a b-MATCHING with more than half the maximum weight in a graph with weights on the edges

Ferdous, S M↗

DOE OSTI2024

A synergistic experimental and theoretical study elucidating the electronic and thermal properties in spinel CuInSnS 4

Quaternary chalcogenides of different structure types continue to be of interest due to the novel physical properties they exhibit and for applications ranging from optoelectronics to energy-related technologies. Herein we report on the electronic and thermal properties of spinel CuInSnS 4 . UV–vis–NIR spectroscopy indicates strong absorption in the visible-light region with an indirect bandgap of 1.52 eV. First-principles computations corroborate our experimental data and reveal that the band-edge electronic properties are due to strong p – d ands – p hybridization and antibonding interactions. Analyses of temperature-dependent thermal properties together with first principles simulations reveal strong anharmonicity and low-lying optical branches that hybridize with the acoustic modes effectively suppressing thermal conductivity. This intrinsically very low thermal conductivity is much lower than that of related chalcogenides. Finally, our findings are discussed in the light of ongoing interest in quaternary metal chalcogenide materials and may aid in the development of this and similar chalcogenides for applications of interest.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Photochemical reactions on aerosols at West Antarctica: A molecular case-study of nitrate formation among sea salt aerosols

Environmental implications of climate change are complex and exhibit regional variations both within and between the polar regions. The increase of solar UV radiation flux over Antarctica due to stratospheric ozone depletion creates the optimal conditions for photochemical reactions on the snow. Modeling, laboratory, and indirect field studies suggest that snowpack process release gases to the atmosphere that can react on sea salt particles in remote regions such as Antarctica, modifying aerosol composition and physical properties of aerosols. Here, we present evidence of photochemical processing in West Antarctica aerosols using microscopic and chemical speciation of individual atmospheric particles. Individual aerosol particles collected at the Brazilian module Criosfera 1 were analyzed by scanning transmission X-ray microscopy with near edge X-ray absorption fine structure spectroscopy (STXM/NEXAFS) combined with computer controlled scanning electron microscopy (CCSEM) with energy-dispersive X-ray (EDX) microanalysis. The displacement of chlorine relative to sodium was observed over most of the sea salt particles. Particles with a chemical composition consistent with NaCl-NO3 contributed up to 30% of atmospheric particles investigated. Overall, this study provides evidence that the snowpack and particulate nitrate photolysis should be considered in dynamic partition equilibrium in the troposphere. Finally, these findings may assist in reducing modeling uncertainties and present new insights into the aerosol chemical composition in the polar environment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Exceptional Radiation Absorption in a Pentagon-Based Si Allotrope

Excellent photovoltaic performance is predicted in a pentagonal covalent network of Si in a hollow structure exhibiting both thermal and dynamical stability. Consisting of a combination of sp 2 and sp 3 hybridized Si atomic orbitals, the GW0 computed band structure shows an indirect band gap near the zone edge and also a manifold of directly absorbing transitions at frequencies in the window of visible light, in distinction with conventional Si. Hydrogenation of a single sp 2 site is predicted to lead to a robust local magnetic moment. In this study, we find a low formation energy at low pressure that is compatible with other experimentally known phases, suggesting that a stable phase might be obtained.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2020

Two-dimensional forms of robust CO 2 reduction photocatalysts

Photoelectrocatalysts that use sunlight to power the CO 2 reduction reaction will be crucial for carbon-neutral power and energy-efficient industrial processes. Scalable photoelectrocatalysts must satisfy a stringent set of criteria, such as stability under operating conditions, product selectivity, and efficient light absorption. Two-dimensional materials can offer high specific surface area, tunability, and potential for heterostructuring, providing a fresh landscape of candidate catalysts. From a set of promising bulk CO 2 reduction photoelectrocatalysts, we screen for candidate monolayers of these materials, then study their catalytic feasibility and suitability. For stable monolayer candidates, we verify the presence of visible-light band gaps, check that band edges can support CO 2 reduction, determine exciton binding energies, and compute surface reactivity. We find visible light absorption for SiAs, ZnTe, and ZnSe monolayers, and that due to a lack of binding, CO selectivity is possible. We thus identify SiAs, ZnTe, and ZnSe monolayers as targets for further investigation, expanding the chemical space for CO 2 photoreduction candidates.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Stratospheric ozone depletion in the Antarctic region triggers intense changes in sea salt aerosol geochemistry

Abstract Since the early 1980s, the Antarctic environment has served as a natural field laboratory for researchers to investigate the effects of stratospheric ozone depletion, which has resulted in increased surface ultraviolet radiation levels. However, its effective threats still present gaps. We report new pieces of evidence of increased ultraviolet radiation impacting West Antarctica sea salt aerosols. Salt aerosols, particularly in the Southern Ocean Sea, play an important role in the radiative earth balance. To disclose the molecular details of sea salt aerosols, we used a synchrotron-based multi-element microscopic speciation of individual microparticles (Scanning Transmission X-ray Microscopy with Near-Edge X-ray Absorption Fine Structure Spectroscopy combined with Computer-Controlled Scanning Electron Microscopy). Here we identified substantial abundances of chlorine-enriched aerosols in sea salt generated by photolytic products, whereas ice core records revealed increased chlorine depletion from the onset of ozone depletion. Our findings reveal that modern sea salt modification has no Holocene precedent.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

An implicit-explicit time splitting strategy for the far SOL plasma fluid model with DG-FEM discretization

We consider a far scrape-off layer (SOL) plasma fluid model of ions that is governed by a Braginskiitype model: a one-dimensional, nonlinear system of advection-diffusion equations coupled with a diffusion equation for neutral particles. Our motivation for studying this system arises from the coupling between the edge plasma and radio-frequency (RF) heating, where solving a far SOL plasma fluid model provides critical insights into edge plasma dynamics. Numerical simulations of plasma fluid models require advanced computational techniques to achieve both efficiency and accuracy, especially when resolving the boundary layer in magnetically confined plasmas. In this work, we propose an implicit-explicit time operator splitting strategy that allows for an efficient solution algorithm, where the diffusive terms are treated semi-implicitly requiring only a linear solve, while the advection part is handled explicitly using a strong-stability-preserving Runge-Kutta (SSP-RK3) scheme. This leads to a fully decoupled system in which the diffusion and advection sub-problems can be solved separately, simplifying the overall solution procedure and allowing for efficient parallelization, which is particularly relevant for exploring the impact of RF heating on the SOL plasma. The main challenge of the discretization is due to the strong coupling between diffusion and advection, particularly through the boundary conditions. This makes implementation of such a scheme in an accurate and stable manner nontrivial. We discuss in detail how to split the equations and manage boundary conditions to maintain stability and well-posedness for each subsystem. We also describe a spatial discretization approach, based on the discontinuous Galerkin finite element method (DG-FEM) and present numerical results for a one-dimensional system.

Burkovska, Olena [ORNL] (ORCID:0000000163101130)↗