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At least 343 records · Page 19

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DOE OSTI2022

Broken-Symmetry Density Functional Theory Analysis of the Ω Intermediate in Radical S -Adenosyl- l -methionine Enzymes: Evidence for a Near-Attack Conformer over an Organometallic Species

Radical S-adenosyl-L-methionine (SAM) enzymes are found in all domains of life and catalyze a wide range of biochemical reactions. Recently, an organometallic intermediate, Ω, has been experimentally implicated in the 5'-deoxyadenosyl radical generation mechanism of the radical SAM superfamily. In this work, we employ broken-symmetry density functional theory to evaluate several structural models of Ω. The results show that the calculated hyperfine coupling constants (HFCCs) for the proposed organometallic structure of Ω are inconsistent with the experiment. In contrast, a near-attack conformer of SAM bound to the catalytic [4Fe–4S] cluster, in which the distance between the unique iron and SAM sulfur is ~3 Å, yields HFCCs that are all within 1 MHz of the experimental values. Furthermore, these results clarify the structure of the ubiquitous Ω intermediate and suggest a paradigm shift reversal regarding the mechanism of SAM cleavage by members of the radical SAM superfamily.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Exploring the benefits of using co-packaged optics in data center and AI supercomputer networks: a simulation-based analysis [Invited]

We investigate the advantages of using co-packaged optics in next-generation data center and AI supercomputer networks. The increased escape bandwidth offered by co-packaged optics provides multiple possibilities for building 50T switches and beyond, expanding the opportunities in both the data center and supercomputing domains. Furthermore, this provides network architects with the opportunity to expand their design space and develop simplified networks with enhanced network locality properties. Co-packaging at the switch and server points enables networks with double capacity while reducing the switch count by 64% compared to state-of-the-art systems. We evaluate these concepts through discrete-event simulations using all-to-all and all-reduce traffic patterns that simulate collective communications commonly found in network-bound applications. Initially, we investigate the all-to-all overhead involved in distributing the virtual machines of the applications across multiple leaf switches and compare it to the scenario in which all VMs are placed under a single switch. Subsequently, we evaluate the performance of an AI supercomputing cluster by simulating both patterns for different message sizes, while also varying the number of participating nodes. The results suggest that networks with improved locality properties become increasingly important as the network stack operates at higher speeds; for a stack latency of 1.25 µs, placing the applications under multiple switches can result in up to 68% higher completion times than placing them under a single switch. For AI supercomputers, significant improvements are observed in the mean server throughput, reaching more than 90% for configurations involving 256 nodes and message sizes of at least 128 KiB.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Deep Learning Based Frequency Stability Assessment in Power Grid with High Renewables

Frequency stability assessment is one critical aspect of power system security assessment. Traditional N-1 screening method is based on the simulations of a few typical daily and seasonal operation scenarios. However, the increasing integration of inverter-based renewables and the retirement of conventional synchronous generators result in decreasing system inertia and growing complexity of system operating conditions. Selecting a few typical operation scenarios cannot cover all operating conditions, and the time-domain simulation of all operation conditions requires tremendous time. This paper proposes a more efficient frequency stability assessment method based on deep learning. The affinity propagation clustering algorithm is used to divide the dataset into different clusters, so the selected dataset for training can cover the diversified operating conditions as much as possible. Also, feature normalization is applied to both the training dataset and testing dataset in order to remove any unnecessary bias. Especially, trained model based on full dataset normalization has bounded error in the prediction. The case study on the reduced 240-bus WECC system demonstrates that the proposed method can predict accurate frequency nadir with limited training dataset. The deep learning model using the revised feature normalization can predict more accurate frequency nadir than that using the traditional feature normalization and has very small maximum prediction error.

affinity propagation↗

DOE OSTI2021

Numerical and Analytical Modeling of the Effect of Cracks on the Self-Inductance of a COTS YJ-41003-TC Toroid

COTS inductors and transformers often contain partial cracks whose effect on inductance, a key performance parameter, have not been carefully studied. In this report, the effects of both partial and complete cracks on the self-inductance of a 100 turn square cross section COTS YJ-41003-TC toroid comprised of J Material was comprehensively investigated using both analytically derived closed form expressions and 3D computational techniques employing commercial codes. Both partial (half-penny) and complete (air gap) cracks of 10 and 25 μm were investigated. The crack is defined as the physical distance between two faces of the toroid's magnetic core, such that the surface normal of either face is along the Φ-direction, in alignment with the B-field. For the purposes of validation, two different approaches were incorporated for both the analytical and numerical models. The two analytical methods are comprised of a first principles approach based on the physics of electromagnetics, as well as linear circuit theory. The former directly utilizes the integral form of Maxwell's equations while the latter exploits the interchangeable relationship between electric and magnetic circuits. Validation within the computational scheme is realized through a code-to-code comparison between commercial solvers, COMSOL Multiphysics and CST, with the former employing the Finite Element Method (FEM) and the latter the Finite Difference Time Domain (FDTD) technique. Sound agreement between all four methods (ie., two analytical and two numerical) is observed, with results indicating that only a perturbation in self-inductance occurs for the half-penny cracks, while a substantial reduction takes place for the case of complete cracks. It is important to note that even though a static μ r is applied, representing the linear region of the BH curve (based on manufacturer specifications), the complete crack results still place a lower conservative bound on the inductance. This follows from the fact that even in the case of a half-penny crack, if the magnetic core portion of the crack approaches saturation, the crack begins to behave like an air gap, or complete crack. When an air gap is introduced into a magnetic core, a substantial reduction in inductance can occur due to the significant difference in permeabilities between the two mediums (ie., μ core >> μ air ). The once intact bulk magnetic core of the toroid essentially begins to behave like an air core.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Structural Basis of Clostridium perfringens Enterotoxin Activation and Oligomerization by Trypsin

Clostridium perfringens enterotoxin (CpE) is a β-pore forming toxin that disrupts gastrointestinal homeostasis in mammals by binding membrane protein receptors called claudins. Although structures of CpE fragments bound to claudins have been determined, the mechanisms that trigger CpE activation and oligomerization that lead to the formation of cytotoxic β-pores remain undetermined. Proteolysis of CpE in the gut by trypsin has been shown to play a role in this and subsequent cytotoxicity processes. Here, we report solution structures of full-length and trypsinized CpE using small-angle X-ray scattering (SAXS) and crystal structures of trypsinized CpE and its C-terminal claudin-binding domain (cCpE) using X-ray crystallography. Mass spectrometry and SAXS uncover that removal of the CpE N-terminus by trypsin alters the CpE structure to expose areas that are normally unexposed. Crystal structures of trypsinized CpE and cCpE reveal unique dimer interfaces that could serve as oligomerization sites. Moreover, comparisons of these structures to existing ones predict the functional implications of oligomerization in the contexts of cell receptor binding and β-pore formation. This study sheds light on trypsin’s role in altering CpE structure to activate its function via inducing oligomerization on its path toward cytotoxic β-pore formation. Its findings can incite new approaches to inhibit CpE-based cytotoxicity with oligomer-disrupting therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Majorana zero modes in a heterogeneous structure of topological and trivial domains in FeSe 1−𝑥 ⁢Te 𝑥

Here, we propose that the existence of vortices in FeSe 1−𝑥 ⁢Te 𝑥 with and without Majorana zero modes (MZMs) can be explained by a heterogeneous mixture of strong topological and trivial superconducting domains, with only vortices in the former exhibiting MZMs. We identify the spectroscopic signatures of topological and trivial vortices and show that they are necessarily separated by a domain wall harboring Majorana edge modes. We demonstrate that when a vortex is moved from a trivial to a topological domain in real time, a domain wall Majorana edge mode is transferred to the vortex as an MZM.

Majorana bound states↗

DOE OSTI2026

$Ξ$ 𝑏 → $Ξ$ form factors from lattice QCD and standard-model predictions for $Ξ$ 𝑏 → $Ξ$⁢𝜇 + ⁢𝜇 − and $Ξ$ 𝑏 → $Ξ$⁢𝛾 decays

We present the first lattice QCD determination of the $Ξ$ 𝑏 → $Ξ$ vector, axial-vector, and tensor form factors, which are relevant for the theory of rare decays including $Ξ$ 𝑏 → $Ξ$⁢ℓ + ⁢ℓ − and $Ξ$ 𝑏 → $Ξ$⁢𝛾. The calculation is performed with 2+1 flavors of domain-wall fermions at three different lattice spacings and pion masses in the range from approximately 430 to 230 MeV. The bottom quark is implemented using an anisotropic clover action. Three-point functions with a wide range of source-sink separations and model averaging are used to extract the ground-state contributions. We fit the dependence of the form factors on the momentum transfer, the pion mass, and the lattice spacing using modified 𝑧 expansions that account for subthreshold branch cuts, and apply dispersive bounds and asymptotic behavior constraints to achieve controlled uncertainties in the full semileptonic kinematic region. Using our form factor results, we present standard model predictions for the $Ξ$$^{−}_{𝑏}$ → $Ξ$ − ⁢𝛾 and $Ξ$$^{−}_{𝑏}$ → $Ξ$ − ⁢𝜇 + ⁢𝜇 − branching fractions and two angular observables.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Convex Q-Learning in Continuous Time with Application to Dispatch of Distributed Energy Resources

Convex Q-learning is a recent approach to reinforcement learning, motivated by the possibility of a firmer theory for convergence, and the possibility of making use of greater a priori knowledge regarding policy or value function structure. This paper explores algorithm design in the continuous time domain, with a finite-horizon optimal control objective. The main contributions are (i) The new Q-ODE: a model-free characterization of the Hamilton-Jacobi-Bellman equation. (ii) A formulation of Convex Q-learning that avoids approximations appearing in prior work. The Bellman error used in the algorithm is defined by filtered measurements, which is necessary in the presence of measurement noise. (iii) Convex Q-learning with linear function approximation is a convex program. It is shown that the constraint region is bounded, subject to an exploration condition on the training input. (iv) The theory is illustrated in application to resource allocation for distributed energy resources, for which the theory is ideally suited.

Lu, Fan↗

DOE OSTI2020

FRaZ: A Generic High-Fidelity Fixed-Ratio Lossy Compression Framework for Scientific Floating-point Data

With ever-increasing volumes of scientific floating-point data being produced by high-performance computing applications, significantly reducing scientific floating-point data size is critical, and error-controlled lossy compressors have been developed for years. None of the existing scientific floating-point lossy data compressors, however, support effective fixed-ratio lossy compression. Yet fixed-ratio lossy compression for scientific floating-point data not only compresses to the requested ratio but also respects a user-specified error bound with higher fidelity. In this paper, we present FRaZ: a generic fixed-ratio lossy compression framework respecting user-specified error constraints. The contribution is twofold. (1) We develop an efficient iterative approach to accurately determine the appropriate error settings for different lossy compressors based on target compression ratios. (2) We perform a thorough performance and accuracy evaluation for our proposed fixed-ratio compression framework with multiple state-of-the-art error-controlled lossy compressors, using several real-world scientific floating-point datasets from different domains. Experiments show that FRaZ effectively identifies the optimum error setting in the entire error setting space of any given lossy compressor. While fixed-ratio lossy compression is slower than fixed-error compression, it provides an important new lossy compression technique for users of very large scientific floating-point datasets.

Underwood, Robert↗

DOE OSTI2023

The structural basis of mRNA recognition and binding by yeast pseudouridine synthase PUS1

The chemical modification of RNA bases represents a ubiquitous activity that spans all domains of life. Pseudouridylation is the most common RNA modification and is observed within tRNA, rRNA, ncRNA and mRNAs. Pseudouridine synthase or ‘PUS’ enzymes include those that rely on guide RNA molecules and others that function as ‘stand-alone’ enzymes. Among the latter, several have been shown to modify mRNA transcripts. Although recent studies have defined the structural requirements for RNA to act as a PUS target, the mechanisms by which PUS1 recognizes these target sequences in mRNA are not well understood. Here we describe the crystal structure of yeast PUS1 bound to an RNA target that we identified as being a hot spot for PUS1-interaction within a model mRNA at 2.4 Å resolution. The enzyme recognizes and binds both strands in a helical RNA duplex, and thus guides the RNA containing the target uridine to the active site for subsequent modification of the transcript. The study also allows us to show the divergence of related PUS1 enzymes and their corresponding RNA target specificities, and to speculate on the basis by which PUS1 binds and modifies mRNA or tRNA substrates.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Electronic nature of chiral charge order in the kagome superconductor CsV 3 Sb 5

Kagome superconductors with T C up to 7 K have been discovered for over 40 y. Recently, unconventional chiral charge order has been reported in kagome superconductor KV 3 Sb 5 , with an ordering temperature of one order of magnitude higher than the T C . However, the chirality of the charge order has not been reported in the cousin kagome superconductor CsV 3 Sb 5 , and the electronic nature of the chirality remains elusive. In this paper, we report the observation of electronic chiral charge order in CsV 3 Sb 5 via scanning tunneling microscopy (STM). We observe a 2 × 2 charge modulation and a 1 × 4 superlattice in both topographic data and tunneling spectroscopy. 2 × 2 charge modulation is highly anticipated as a charge order by fundamental kagome lattice models at van Hove filling, and is shown to exhibit intrinsic chirality. We find that the 1 × 4 superlattices form various small domain walls, and can be a surface effect as supported by our first-principles calculations. Crucially, we find that the amplitude of the energy gap opened by the charge order exhibits real-space modulations, and features 2 × 2 wave vectors with chirality, highlighting the electronic nature of the chiral charge order. STM study at 0.4 K reveals a superconducting energy gap with a gap size 2Δ = 0.85 meV, which estimates a moderate superconductivity coupling strength with 2Δ/k B T C = 3.9. Furthermore, when further applying a c-axis magnetic field, vortex core bound states are observed within this gap, indicative of clean-limit superconductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

A Model for the Signal Initiation Complex Between Arrestin-3 and the Src Family Kinase Fgr

Arrestins regulate a wide range of signaling events, most notably when bound to active G protein-coupled receptors (GPCRs). Among the known effectors recruited by GPCR-bound arrestins are Src family kinases, which regulate cellular growth and proliferation. Here, we focus on arrestin-3 interactions with Fgr kinase, a member of the Src family. Previous reports demonstrated that Fgr exhibits high constitutive activity, but can be further activated by both arrestin-dependent and arrestin-independent pathways. We report that arrestin-3 modulates Fgr activity with a hallmark bell-shaped concentration-dependence, consistent with a role as a signaling scaffold. Here, we further demonstrate using NMR spectroscopy that a polyproline motif within arrestin-3 interacts directly with the SH3 domain of Fgr. To provide a framework for this interaction, we determined the crystal structure of the Fgr SH3 domain at 1.9 Å resolution and developed a model for the GPCR-arrestin-3-Fgr complex that is supported by mutagenesis. This model suggests that Fgr interacts with arrestin-3 at multiple sites and is consistent with the locations of disease-associated Fgr mutations. Collectively, these studies provide a structural framework for arrestin-dependent activation of Fgr.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Computing Edge States without Hard Truncation

We present a numerical method which accurately computes the discrete spectrum and associated bound states of semi-infinite Hamiltonians which model electronic “edge” states localized at boundaries of one- and two-dimensional crystalline materials. The problem is nontrivial since arbitrarily large finite “hard” (Dirichlet) truncations of the Hamiltonian in the infinite bulk direction tend to produce spurious bound states partially supported at the truncation. Our method, which overcomes this difficulty, is to compute the Green's function of the semi-infinite Hamiltonian by imposing an appropriate boundary condition in the bulk direction; then, the spectral data is recovered via Riesz projection. We demonstrate our method's effectiveness by studies of edge states at a graphene zig-zag edge in the presence of defects modeled both by a discrete tight-binding model and a continuum PDE model under finite difference discretization. Our method may also be used to study states localized at domain wall-type edges in one- and two-dimensional materials where the edge Hamiltonian is infinite in both directions; we demonstrate this for the case of a tight-binding model of distinct honeycomb structures joined along a zig-zag edge. Here, we expect our method to be useful for designing novel devices based on precise wave-guiding by edge states.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

The MBARI-WEC: a power source for ocean sensing

Abstract Interest in wave energy converters to provide autonomous power to various ocean-bound systems, such as autonomous underwater vehicles, sensor systems, and even aquaculture farms, has grown in recent years. The Monterey Bay Aquarium Research Institute has developed and deployed a small two-body point absorber wave energy device suitable to such needs. This paper provides a description of the system to support future open-source access to the device and further the general development of similar wave energy systems. Additionally, to support future control design and system modification efforts, a set of hydrodynamic models are presented and cross-compared. To test the viability of using a linear frequency-domain admittance model for controller tuning, the linear model is compared against four WEC-Sim models of increasing complexity. The linear frequency-domain model is found to be generally adequate for capturing system dynamics, as the model agreement is good and the degree of nonlinearity introduced in the WEC-Sim models is generally less than 2.5%.

16 TIDAL AND WAVE POWER↗

DOE OSTI2026

LibraryX: A Framework for Cross-Library-Call Optimization

Scientific applications utilize performance libraries as a software engineering concept: these libraries encapsulate important and well-understood (mathematical) operations, allow for reuse, and are implemented and tuned by experts. Domain scientists then implement complex algorithms based on these domainspecific libraries. While individual library calls are optimized, larger performance gains across sequences of calls—sometimes spanning multiple libraries—are often unrealized, forcing a trade-off between performance and implementation complexity.To overcome this issue, we propose LibraryX, an approach and a system that allows for cross-library-call optimization even when library calls stem from multiple performance libraries. LibraryX annotates library calls with semantic information and optimizes entire directed acyclic graphs (DAGs) of calls dynamically using the SPIRAL code generation system. We demonstrate its effectiveness across a range of memory bound workloads, achieving significant speedups on Nvidia, AMD, and Intel accelerators compared to code using native libraries without cross-call optimization.

Rao, Sanil [Carnegie Mellon University,Department ↗

DOE OSTI2025

Crystalline-symmetry-protected Majorana modes in coupled quantum dots

We propose a minimalist architecture for achieving various crystalline-symmetry-protected Majorana modes in an array of coupled quantum dots. Our framework is motivated by the recent experimental demonstrations of two-site and three-site artificial Kitaev chains in a similar setup. We find that introducing a π-phase domain wall in the Kitaev chain leads to a pair of mirror-protected Majorana zero modes located at or near the junction. Joining two π junctions into a closed loop, we can simulate two distinct classes of two-dimensional higher-order topological superconducting phases, both carrying symmetry-protected Majorana modes around the sample corners. As an extension of the π junction, we further consider a general vertex structure where n Kitaev chains meet, i.e., a Kitaev n vertex. We prove that such an n vertex, if respecting a dihedral symmetry group D n , necessarily carries n vertex-bound Majorana modes protected by the D n symmetry. Resilience of the junction and vertex Majorana bound states against disorder and correlation effects is also discussed. Our architecture paves the way for designing, constructing, and exploring a wide variety of artificial topological crystalline phases in quantum-dot experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗