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DOE OSTI2023

Spin-singlet topological superconductivity in the attractive Rashba-Hubbard model

Fully gapped, spin-singlet superconductors with antisymmetric spin-orbit coupling in a Zeeman magnetic field provide a promising route to realize superconducting states with non-Abelian topological order and therefore fault-tolerant quantum computation. Here we use a quantum Monte Carlo dynamical cluster approximation to study the superconducting properties of a doped two-dimensional attractive Hubbard model with Rashba spin-orbit coupling in a Zeeman magnetic field. We generally find that the Rashba coupling has a beneficial effect towards s-wave superconductivity. In the presence of a finite Zeeman field, when superconductivity is suppressed by Pauli pair breaking, the Rashba coupling counteracts the spin imbalance created by the Zeeman field by mixing the spins, and thus restores superconductivity at finite temperatures. Further, we show that this favorable effect of the spin-orbit coupling is traced to a spin-flip driven enhancement of the amplitude for the propagation of a pair of electrons in time-reversed states. Moreover, by inspecting the Fermi surface of the interacting model, we show that for sufficiently large Rashba coupling and Zeeman field, the superconducting state is expected to be topologically nontrivial.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2018

Large-scale exact diagonalizations reveal low-momentum scales of nuclei

Ab initio methods aim to solve the nuclear many-body problem with controlled approximations. Virtually exact numerical solutions for realistic interactions can only be obtained for certain special cases such as few-nucleon systems. In this paper, we extend the reach of exact diagonalization methods to handle model spaces with dimension exceeding ${10}^{10}$ on a single compute node. This allows us to perform no-core shell model (NCSM) calculations for $^{6}\mathrm{Li}$ in model spaces up to ${N}_{\mathrm{max}}=22$ and to reveal the $^{4}\mathrm{He}$+d halo structure of this nucleus. Still, the use of a finite harmonic-oscillator basis implies truncations in both infrared (IR) and ultraviolet (UV) length scales. These truncations impose finite-size corrections on observables computed in this basis. We perform IR extrapolations of energies and radii computed in the NCSM and with the coupled-cluster method at several fixed UV cutoffs. It is shown that this strategy enables information gain also from data that is not fully UV converged. IR extrapolations improve the accuracy of relevant bound-state observables for a range of UV cutoffs, thus making them profitable tools. We relate the momentum scale that governs the exponential IR convergence to the threshold energy for the first open decay channel. Finally, using large-scale NCSM calculations we numerically verify this small-momentum scale of finite nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

Sirepo : an open-source cloud-based software interface for X-ray source and optics simulations

Sirepo , a browser-based GUI for X-ray source and optics simulations, is presented. Such calculations can be performed using SRW ( Synchrotron Radiation Workshop ), which is a physical optics computer code, allowing simulation of entire experimental beamlines using the concept of a `virtual beamline' with accurate treatment of synchrotron radiation generation and propagation through the X-ray optical system. SRW is interfaced with Sirepo by means of a Python application programming interface. Sirepo supports most of the optical elements currently used at beamlines, including recent developments in SRW . In particular, support is provided for the simulation of state-of-the-art X-ray beamlines, exploiting the high coherence and brightness of modern light source facilities. New scientific visualization and reporting capabilities have been recently implemented within Sirepo , as well as automatic determination of electron beam and undulator parameters. Publicly available community databases can be dynamically queried for error-free access to material characteristics. These computational tools can be used for the development and commissioning of new X-ray beamlines and for testing feasibility and optimization of experiments. The same interface can guide simulation on a local computer, a remote server or a high-performance cluster. Sirepo is available online and also within the NSLS-II firewall, with a growing number of users at other light source facilities. Our open source code is available on GitHub.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2014

Compiler Optimization for Extreme-Scale Scripting

The data-driven task parallelism execution model can support parallel programming models that are well suited for large-scale distributed-memory parallel computing, for example, simulations and analysis pipelines running on clusters and clouds. We describe a novel compiler intermediate representation and optimizations for this execution model, including adaptions of standard techniques alongside novel techniques. These techniques are applied to Swift/T, a high-level scripting language for flexible data flow composition of functions, which may be serial or use lower-level parallel programming models such as MPI and OpenMP. This paper presents preliminary results, indicating that our compiler optimizations reduce communication overhead by 70% to 93% on distributed-memory systems.

Armstrong, Timothy G.↗

DOE OSTI2026

Machine learning at the Spallation Neutron Source accelerator and target

We describe the ongoing efforts to apply Machine Learning techniques to improve the performance of our accelerator and target. Specially, we are looking to minimize halo beam losses in the absence of a proper physics model, automatically detect and log anomalies in the target support systems such as cooling, and detect and prevent errant beam pulses in the linac. We also describe the infrastructure we use to acquire and stream data to the GPU cluster for training, our code development cycle, and edge computing for model inference. To minimize halo beam losses, we use a Reinforcement Learning technique tested on a virtual accelerator. The target anomaly detection is trained on archived data using incomplete physics models and is made part of the existing target reporting system. The errant beam prevention analyzes beam current and beam phase waveforms as well as accelerator configuration data to predict errant pulses. We also develop continual learning to adapt to changes in the accelerator.

Accelerator Physics↗

DOE OSTI2023

The Generalized Green’s function Cluster Expansion: A Python package for simulating polarons

We present an efficient implementation of the Generalized Green’s function Cluster Expansion (GGCE), which is a new method for computing the ground-state properties and dynamics of polarons (single electrons coupled to lattice vibrations) in model electron-phonon systems. The GGCE works at arbitrary temperature and is well suited for a variety of electron-phonon couplings, including, but not limited to, site and bond Holstein and Peierls (Su-SchriefferHeeger) couplings, and couplings to multiple phonon modes with different energy scales and coupling strengths. Quick calculations can be performed efficiently on a laptop using solvers from NumPy and SciPy, or in parallel at scale using the PETSc sparse linear solver engine.

36 MATERIALS SCIENCE↗

DOE OSTI2016

ON THE CONTRIBUTION OF FLUORESCENCE TO LYα HALOS AROUND STAR-FORMING GALAXIES

We quantify the contribution of Lyα fluorescence to observed spatially extended Lyα halos around Lyα emitters at redshift z = 3.1. The key physical quantities that describe the fluorescent signal include (i) the distribution of cold gas in the circumgalactic medium (CGM); we explore simple analytic models and fitting functions to recent hydrodynamical simulations; and (ii) local variations in the ionizing background due to ionizing sources that cluster around the central galaxy. We account for clustering by boosting the observationally inferred volumetric production rate of ionizing photons, ϵ{sub LyC}, by a factor of 1+ξ{sub LyC}(r), in which ξ{sub LyC}(r) quantifies the clustering of ionizing sources around the central galaxy. We compute ξ{sub LyC}(r) by assigning an “effective” bias parameter to the ionizing sources. This novel approach allows us to quantify our ignorance of the population of ionizing sources in a simple parametrized form. We find a maximum enhancement in the local ionizing background in the range 50–200 at r ∼ 10 physical kpc. For spatially uncorrelated ionizing sources and fluorescing clouds we find that fluorescence can contribute up to ∼ 50%–60% of the observed spatially extended Lyα emission. We briefly discuss how future observations can shed light on the nature of Lyα halos around star-forming galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

A Scalable Parallel Hypergraph Generator (HyGen)

Graphs are extensively used to model real-world complex systems. An edge in a graph can model pairwise relationships. However, multiway relationships (connections between three or more vertices) are common in many complex systems such as cellular process, image segmentation, and circuit design. A graph edge cannot model multiway relationships. A hypergraph, which can connect more than two vertices, is thus a better option to model multiway relationships. A large-scale hypergraph analysis has the potential to find useful insights from a complex system and assist in knowledge discovery. Currently a limited number of hypergraphs exists that are representative of real-world datasets. Moreover, real-world hypergraph datasets are small in size and inadequate to incorporate future needs. A graph generator that can produce large-scale synthetic hypergraphs can solve the above mentioned problems. In this paper, we present a scalable parallel hypergraph generator (HyGen) based on the Message Passing Interface (MPI) standard. To generate hypergraphs, HyGen takes the following parameter values as inputs: i) number of vertices, ii) number of hyperedges, iii) number of clusters, iv) vertex distribution, v) hyperedge distribution, vi) local cluster cardinality, and vii) global cluster cardinality. We have demonstrated that HyGen can generate hypergraphs of various sizes in a scalable fashion. HyGen takes approximately four minutes to generate a hypergraph with 4.8 million vertices, 1.6 million hyperedges, and 800 clusters using 1,024 processes on a leadership class computing platform. Our strong and weak scaling experiments on supercomputers demonstrate that HyGen can quickly create large-scale hypergraphs in a parallel manner, thus providing a useful capability for hypergraph analysis.

Hasan, S M Shamimul↗

DOE OSTI2017

Optical potential from first principles

Here, we develop a method to construct a microscopic optical potential from chiral interactions for nucleon-nucleus scattering. The optical potential is constructed by combining the Green’s function approach with the coupled-cluster method. To deal with the poles of the Green’s function along the real energy axis we employ a Berggren basis in the complex energy plane combined with the Lanczos method. Using this approach, we perform a proof-of-principle calculation of the optical potential for the elastic neutron scattering on 16 O. For the computation of the ground-state of 16 O, we use the coupled-cluster method in the singles-and-doubles approximation, while for the A ±1 nuclei we use particle-attached/removed equation-of-motion method truncated at two-particle-one-hole and one-particle-two-hole excitations, respectively. We verify the convergence of the optical potential and scattering phase shifts with respect to the model-space size and the number of discretized complex continuum states. We also investigate the absorptive component of the optical potential (which reflects the opening of inelastic channels) by computing its imaginary volume integral and find an almost negligible absorptive component at low-energies. To shed light on this result, we computed excited states of 16 O using equation-of-motion coupled-cluster method with singles-and- doubles excitations and we found no low-lying excited states below 10 MeV. Furthermore, most excited states have a dominant two-particle-two-hole component, making higher-order particle-hole excitations necessary to achieve a precise description of these core-excited states. We conclude that the reduced absorption at low-energies can be attributed to the lack of correlations coming from the low-order cluster truncation in the employed coupled-cluster method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

Bootstrap AMG for spectral clustering

Graph Laplacian is a popular tool for analyzing graphs, particularly in graph partitioning and clustering. Given a notion of similarity (via an adjacency matrix), graph clustering refers to identifying different groups such that vertices in the same group are more similar compared to vertices across different groups. Data clustering can be reformulated in terms of a graph clustering problem when the given set of data is represented as a graph, also known as similarity graph. In this context, eigenvectors of the graph Laplacian are often used to obtain a new geometric representation of the original data set that generally enhances cluster properties and improves cluster detection. Here, we apply a bootstrap algebraic multigrid (AMG) method that constructs a set of vectors associated with the graph Laplacian. These vectors, referred to as algebraically smooth ones, span a low-dimensional Euclidean space, which we use to represent the data, enabling cluster detection both in synthetic and in realistic well-clustered graphs. We show that, in the case of a good quality bootstrap AMG, the computed smooth vectors employed in the construction of the final AMG operator, which by construction is spectrally equivalent to the originally given graph Laplacian, accurately approximate the space in the lower portion of the spectrum of the preconditioned operator. Thus, our approach can be viewed as a spectral clustering technique associated with the generalized spectral problem (Laplace operator versus the final AMG operator), and hence, it can be seen as an extension of the classical spectral clustering that employs a standard eigenvalue problem.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Green’s function coupled cluster formulations utilizing extended inner excitations

Here, we analyze new approximations of the Green’s function coupled cluster (GFCC) method where locations of poles are improved by extending the excitation level of inner auxiliary operators. These new GFCC approximations can be categorized as the GFCC-i( n, m ) method, where the excitation level of the inner auxiliary operators ( m ) used to describe the ionization potential and electron affinity effects in the N-1 and N+1 particle spaces is higher than the excitation level ( n ) used to correlate the ground-state coupled cluster wave function for the N-electron system. Furthermore, we reveal the so-called “n + 1” rule in this category [or the GFCC-i( n, n + 1) method], which states that in order to maintain size-extensivity of the Green’s function matrix elements, the excitation level of inner auxiliary operators X p (ω) and Y q (ω) cannot exceed n + 1. We also discuss the role of the moments of coupled cluster equations that in a natural way assures these properties. Our implementation in the present study is focused on the first approximation in this GFCC category, i.e., the GFCC-i(2,3) method. As our first practice, we use the GFCC-i(2,3) method to compute the spectral functions for the N 2 and CO molecules in the inner and outer valence regimes. Lastly, in comparison with the Green’s function coupled cluster singles, doubles results, the computed spectral functions from the GFCC-i(2,3) method exhibit better agreement with the experimental results and other theoretical results, particularly in terms of providing higher resolution of satellite peaks and more accurate relative positions of these satellite peaks with respect to the main peak positions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Strong Electronic and Magnetic Coupling in M 4 (M = Ni, Cu) Clusters via Direct Orbital Interactions between Low-Coordinate Metal Centers

In this report we present an extensive study of tetranuclear transition-metal cluster compounds M 4 (NP t Bu 3 ) 4 and [M 4 (NP t Bu 3 ) 4 ][B(C 6 F 5 ) 4 ] (M = Ni, Cu; t Bu = tert -butyl), which feature low-coordinate metal centers and direct metal-metal orbital overlap. X-ray diffraction, electrochemical, magnetic, spectroscopic, and computational analysis elucidate the nature of the bonding interactions in these clusters and the impact of these interactions on the electronic and magnetic properties. Direct orbital overlap results in strongly coupled, large-spin ground states in the [Ni 4 (NP t Bu 3 ) 4 ] +/0 clusters and fully delocalized, spin-correlated electrons. Correlated electronic structure calculations confirm the presence of ferromagnetic ground states that arise from direct exchange between magnetic orbitals, and, in the case of the neutral cluster, itinerant electron magnetism similar to that in metallic ferromagnets. The cationic nickel cluster also possesses large magnetic anisotropy exemplified by a large, positive axial zero-field splitting parameter of D = +7.95 or +9.2 cm -1 , as determined by magnetometry or electron paramagnetic resonance spectroscopy, respectively. The [Ni 4 (NP t Bu 3 ) 4 ] + cluster is also the first molecule with easy-plane magnetic anisotropy to exhibit zero-field slow magnetic relaxation, and under a small applied field, it exhibits relaxation exclusively through an Orbach mechanism with a spin relaxation barrier of 16 cm -1 . The S = 1 / 2 complex [Cu 4 (NP t Bu 3 ) 4 ] + exhibits slow magnetic relaxation via a Raman process on the millisecond time scale, supporting the presence of slow relaxation via an Orbach process in the nickel analogue. Overall, this work highlights the unique electronic and magnetic properties that can be realized in metal clusters featuring direct metal-metal orbital interactions between low-coordinate metal centers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

Reliability Lessons Learned From GPU Experience With The Titan Supercomputer at Oak Ridge Leadership Computing Facility

The high computational capability of graphics processing units (GPUs) is enabling and driving the scientific discovery process at large-scale. The world’s second fastest supercomputer for open science, Titan, has more than 18,000 GPUs that computational scientists use to perform scientific simu- lations and data analysis. Understanding of GPU reliability characteristics, however, is still in its nascent stage since GPUs have only recently been deployed at large-scale. This paper presents a detailed study of GPU errors and their impact on system operations and applications, describing experiences with the 18,688 GPUs on the Titan supercom- puter as well as lessons learned in the process of efficient operation of GPUs at scale. These experiences are helpful to HPC sites which already have large-scale GPU clusters or plan to deploy GPUs in the future.

Tiwari, Devesh↗

DOE OSTI2019

Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning

Computational modeling of chemical and biological systems at atomic resolution is a crucial tool in the chemist’s toolset. The use of computer simulations requires a balance between cost and accuracy: quantum-mechanical methods provide high accuracy but are computationally expensive and scale poorly to large systems, while classical force fields are cheap and scalable, but lack transferability to new systems. Machine learning can be used to achieve the best of both approaches. Here we train a general-purpose neural network potential (ANI-1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology, and chemistry, and billions of times faster than CCSD(T)/CBS calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Adsorption in zeolites using mechanically embedded ONIOM clusters

Here, we have explored mechanically embedded three-layer QM/QM/MM ONIOM models for computational studies of binding in Al-substituted zeolites. In all the models considered, the high-level-theory layer consists of the adsorbate molecule and of the framework atoms within the first two coordination spheres of the Al atom and is treated at the M06-2X/6-311G(2df,p) level. For simplicity, flexibility and routine applicability, the outer, low-level-theory layer is treated with the UFF. We have modelled the intermediate-level layer quantum mechanically and investigated the performance of HF theory and of three DFT functionals, B3LYP, M06-2X and ωB97x-D, for different layer sizes and various basis sets, with and without BSSE corrections. We have studied the binding of sixteen probe molecules in H-MFI and compared the computed adsorption enthalpies with published experimental data. We have demonstrated that HF and B3LYP are inadequate for the description of the interactions between the probe molecules and the framework surrounding the metal site of the zeolite on account of their inability to capture dispersion forces. Both M06-2X and ωB97x-D on average converge within ca. 10% of the experimental values. We have further demonstrated transferability of the approach by computing the binding enthalpies of n-alkanes (C1–C8) in H-MFI, H-BEA and H-FAU, with very satisfactory agreement with experiment. The computed entropies of adsorption of n-alkanes in H-MFI are also found to be in good agreement with experimental data. Finally, we compare with published adsorption energies calculated by periodic-DFT for n-C3 to n-C6 alkanes, water and methanol in H-ZSM-5 and find very good agreement.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

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 OSTI2017

Three-Dimensional Modeling of Fracture Clusters in Geothermal Reservoirs

The objective of this is to develop a 3-D numerical model for simulating mode I, II, and III (tensile, shear, and out-of-plane) propagation of multiple fractures and fracture clusters to accurately predict geothermal reservoir stimulation using the virtual multi-dimensional internal bond (VMIB). Effective development of enhanced geothermal systems can significantly benefit from improved modeling of hydraulic fracturing. In geothermal reservoirs, where the temperature can reach or exceed 350oC, thermal and poro-mechanical processes play an important role in fracture initiation and propagation. In this project hydraulic fracturing of hot subsurface rock mass will be numerically modeled by extending the virtual multiple internal bond theory and implementing it in a finite element code, WARP3D, a three-dimensional finite element code for solid mechanics. The new constitutive model along with the poro-thermoelastic computational algorithms will allow modeling the initiation and propagation of clusters of fractures, and extension of pre-existing fractures. The work will enable the industry to realistically model stimulation of geothermal reservoirs. The project addresses the Geothermal Technologies Office objective of accurately predicting geothermal reservoir stimulation (GTO technology priority item). The project goal will be attained by: (i) development of the VMIB method for application to 3D analysis of fracture clusters; (ii) development of poro- and thermoelastic material sub-routines for use in 3D finite element code WARP3D; (iii) implementation of VMIB and the new material routines in WARP3D to enable simulation of clusters of fractures while accounting for the effects of the pore pressure, thermal stress and inelastic deformation; (iv) simulation of 3D fracture propagation and coalescence and formation of clusters, and comparison with laboratory compression tests; and (v) application of the model to interpretation of injection experiments (planned by our industrial partner) with reference to the impact of the variations in injection rate and temperature, rock properties, and in-situ stress.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Introducing the embedded random phase approximation: H 2 dissociative adsorption on Cu(111) as an exemplar

The random phase approximation (RPA) as a means of treating electron correlation recently has been shown to outperform standard density functional theory (DFT) approximations in a variety of cases. However, the computational cost of the RPA is substantially more than DFT, especially when aiming to study extended surfaces. Properly accounting for sufficient surface ensemble size, Brillouin zone sampling, and vacuum separation of periodic images in standard periodic-planewave-based DFT code raises the cost to achieve converged results. Here, we show that sub-system embedding schemes enable use of the RPA for modeling heterogeneous reactions at reduced computational cost. Further, we explore two different embedded RPA (emb-RPA) approaches, periodic emb-RPA and cluster emb-RPA. We use the (experimentally and theoretically) well-studied H 2 dissociative adsorption on Cu(111) as our exemplar, and first perform full periodic RPA calculations as a benchmark. The full RPA results match well the semi-empirical barrier fit to experimental observables and others derived from high-level computations, e.g., from recent embedded n-electron valence second order perturbation theory [Zhao et al., J. Chem. Theory Comput. 16(11), 7078–7088 (2020)] and quantum Monte Carlo [Doblhoff-Dier et al., J. Chem. Theory Comput. 13(7), 3208–3219 (2017)] simulations. Among the two emb-RPA approaches tested, the cluster emb-RPA accurately reproduces the energy profile (maximum error of 50 meV along the reaction pathway) while reducing the computational cost by approximately two orders of magnitude. We therefore expect that the embedded cluster approach will enable wider RPA implementation in heterogeneous catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗