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At least 1,639 records · Page 91

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DOE OSTI2023

Distributed memory, GPU accelerated Fock construction for hybrid, Gaussian basis density functional theory

With the growing reliance of modern supercomputers on accelerator-based architecture such a graphics processing units (GPUs), the development and optimization of electronic structure methods to exploit these massively parallel resources has become a recent priority. While significant strides have been made in the development GPU accelerated, distributed memory algorithms for many modern electronic structure methods, the primary focus of GPU development for Gaussian basis atomic orbital methods has been for shared memory systems with only a handful of examples pursing massive parallelism. Here in this work, we present a set of distributed memory algorithms for the evaluation of the Coulomb and exact exchange matrices for hybrid Kohn–Sham DFT with Gaussian basis sets via direct density-fitted (DF-J-Engine) and seminumerical (sn-K) methods, respectively. The absolute performance and strong scalability of the developed methods are demonstrated on systems ranging from a few hundred to over one thousand atoms using up to 128 NVIDIA A100 GPUs on the Perlmutter supercomputer.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Dynamic structure of the full-length scaffolding protein NHERF1 influences signaling complex assembly

The Na+/H+ exchange regulatory cofactor 1 (NHERF1) protein modulates the assembly and intracellular trafficking of several transmembrane G protein–coupled receptors (GPCRs) and ion transport proteins with the membrane–cytoskeleton adapter protein ezrin. We applied in this report solution NMR and small-angle neutron scattering (SANS) to structurally characterize full-length NHERF1 and disease-associated variants that are implicated in impaired phosphate homeostasis. Using NMR, we mapped the modular architecture of NHERF1, which is composed of two structurally-independent PDZ domains that are connected by a flexible, disordered linker. We observed that the ultra-long and disordered C-terminal tail of NHERF1 has a type 1 PDZ-binding motif that interacts weakly with the proximal, second PDZ domain to form a dynamically autoinhibited structure. Using ensemble-optimized analysis of SANS data, we extracted the molecular size distribution of structures from the extensive conformational space sampled by the flexible chain. Our results revealed that NHERF1 is a diffuse ensemble of variable PDZ domain configurations and a disordered C-terminal tail. The joint NMR/SANS data analyses of three disease variants (L110V, R153Q, and E225K) revealed significant differences in the local PDZ domain structures and in the global conformations compared with the WT protein. Furthermore, we show that the substitutions affect the affinity and kinetics of NHERF1 binding to ezrin and to a C-terminal peptide from G protein–coupled receptor kinase 6A (GRK6A). Significant insight into the modulation of the intrinsic flexibility of NHERF1 by disease-associated point mutations that alter the dynamic assembly of signaling complexes are provided in this work.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Growth and characterization of homoepitaxial β-Ga 2 O 3 layers

ß-Ga 2 O 3 is a next-generation ultra-wide bandgap semiconductor (E g = 4.8 eV to 4.9 eV) that can be homoepitaxially grown on commercial substrates, enabling next-generation power electronic devices among other important applications. Analyzing the quality of deposited homoepitaxial layers used in such devices is challenging, in part due to the large probing depth in traditional x-ray diffraction (XRD) and also due to the surface-sensitive nature of atomic force microscopy (AFM). Here, a combination of evanescent grazing-incidence skew asymmetric XRD and AFM are investigated as an approach to effectively characterize the quality of homoepitaxial ß-Ga 2 O 3 layers grown by molecular beam epitaxy at a variety of Ga/O flux ratios. Accounting for both structure and morphology, optimal films are achieved at a Ga/O ratio of ~1.15, a conclusion that would not be possible to achieve by either XRD or AFM methods alone. Finally, fabricated Schottky barrier diodes with thicker homoepitaxial layers are characterized by J-V and C-V measurements, revealing an unintentional doping density of 4.3×10 16 cm -3 - 2×10 17 cm -3 in the epilayer. These results demonstrate the importance of complementary measurement methods for improving the quality of the ß-Ga 2 O 3 homoepitaxial layers used in power electronic and other devices.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Twelve-crystal prototype of Li 2 MoO 4 scintillating bolometers for CUPID and CROSS experiments

An array of twelve 0.28 kg lithium molybdate (LMO) low-temperature bolometers equipped with 16 bolometric Ge light detectors, aiming at optimization of detector structure for CROSS and CUPID double-beta decay experiments, was constructed and tested in a low-background pulse-tube-based cryostat at the Canfranc underground laboratory in Spain. Performance of the scintillating bolometers was studied depending on the size of phonon NTD-Ge sensors glued to both LMO and Ge absorbers, shape of the Ge light detectors (circular vs. square, from two suppliers), in different light collection conditions (with and without reflector, with aluminum coated LMO crystal surface). The scintillating bolometer array was operated over 8 months in the low-background conditions that allowed to probe a very low, μBq/kg, level of the LMO crystals radioactive contamination by 228 Th and 226 Ra.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

American cities in a time of global environmental change: the case of the Baltimore Social-Environmental Collaborative

The Baltimore Social-Environmental Collaborative (BSEC) Urban Integrated Field Laboratory seeks a new paradigm for urban climate research. Motivated by deep uncertainties in urban climate and the future of urban systems, BSEC works collaboratively across institutions and stakeholder groups to co-generate the science needed to advance energy security and resilience to extreme events across the city of Baltimore, Maryland, USA, and to do so in a manner that can inform similar efforts in other cities. BSEC begins with stakeholder priorities (health, affordable energy, etc) and designs observation networks and models to deliver climate science to address them. This takes the form of an iterative collaborative cycle, in which an initial research strategy is repeatedly updated in conversation with community partners, and researchers and stakeholders learn from each other. To date, this cycle has included multiple rounds of collaborative deliberation on urban heat mitigation, in which a multicriteria decision tool has been updated with more community-relevant spatial structure and modified optimization metrics. The guiding objective of this cycle is to inform potential ‘secure and resilient pathways’ for energy and infrastructure. In doing so, BSEC addresses fundamental urban science questions in natural and social sciences. It also tests our ability to integrate this science in a manner that advances participatory decision-making for urban resilience.

climate↗

DOE OSTI2022

Radial–axial transport coordination enhances sugar translocation in the phloem vasculature of plants

Abstract Understanding mass transport of photosynthates in the phloem of plants is necessary for predicting plant carbon allocation, productivity, and responses to water and thermal stress. Several hypotheses about optimization of phloem structure and function and limitations of phloem transport under drought have been proposed and tested with models and anatomical data. However, the true impact of radial water exchange of phloem conduits with their surroundings on mass transport of photosynthates has not been addressed. Here, the physics of the Munch mechanism of sugar transport is re-evaluated to include local variations in viscosity resulting from the radial water exchange in two dimensions (axial and radial) using transient flow simulations. Model results show an increase in radial water exchange due to a decrease in sap viscosity leading to increased sugar front speed and axial mass transport across a wide range of phloem conduit lengths. This increase is around 40% for active loaders (e.g. crops) and around 20% for passive loaders (e.g. trees). Thus, sugar transport operates more efficiently than predicted by previous models that ignore these two effects. A faster front speed leads to higher phloem resiliency under drought because more sugar can be transported with a smaller pressure gradient.

58 GEOSCIENCES↗

DOE OSTI2020

Spatial nematic fluctuation in BaFe 2 (As 1-x P x ) 2 revealed by spatially and angle-resolved photoemission spectroscopy

Nematicity, where rotational symmetry is broken while translational symmetry is conserved, is prevalent in higherature superconductors. In particular, nematic quantum critical point has been universally found near the optimum doping of the superconducting dome of several iron-based superconductor families. In such a regime, evidence for strong nematic fluctuations have been observed. As the precursor to this order, nematic fluctuations emerge before nematicity, providing favorable ground to study how nematic order modifies the electronic structure in the absence of structural distortion. Here we use spatially resolved angle-resolved photoemission spectroscopy to investigate the correlation between the onset of nematic fluctuations and electronic structure in an optimally doped BaFe 2 (As 1-x P x )2 (x ~ 0.3) superconductor. We reveal a strong spatially varying anisotropy of the Fermi surface on a length scale of tens of microns with strong correlation between the changes in the hole and electron Fermi pockets, consistent with the variations expected in the presence of fluctuating nematic order. These results provide direct evidence for spatial nematic fluctuations in the optimal doping regime of iron-based superconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2019

LP-BNN: Ultra-low-Latency BNN Inference with Layer Parallelism

High inference latency seriously limits the deployment of DNNs in real-time domains such as autonomous driving, robotic control, and many others. To address this emerging challenge, researchers have proposed approximate DNNs with reduced precision, e.g., Binarized Neural Networks (BNNs). While BNNs can be built to have little loss in accuracy, latency reduction still has much room for improvement. In this paper, we propose a single-FPGA-based BNN accelerator that achieves microsecond-level ultra-low-latency inference of ImageNet, LP- BNN. We obtain this performance via several design optimizations. First, we optimize the network structure by removing Batch Normalization (BN) functions which leads to significant latency in BNNs without any loss on accuracy. Second, we propose a parameterized architecture which is based on layer parallelism and supports nearly perfect load balancing for various types of BNNs. Third, we fuse all the convolution layers and the first fully connected layer. We process them in parallel through fine- grained inter-layer pipelining. With our proposed accelerator, the inference of binarized AlexNet, VGGNet, and ResNet are completed within 21.5µs, 355µs, and 67.8µs respectively, with no loss in accuracy as compared with other BNN implementations.

Geng, Tong↗

DOE OSTI2024

The Role of Azimuthal Prestress in the Longitudinal Degradation of Nb 3 Sn Superconducting Magnets

Superconducting magnet coils are subject to enormous electro-magnetic forces which push the cables away from the winding pole, and against the surrounding structure. This structure is usually optimized trying to limit the overall motion and the strains experienced by the superconducting elements. To achieve this, preload forces are applied both in the coil cross-section and along its length. If the e.m. forces overcome these preloads, separation between the coil and the pole occurs, resulting in an overall loss of rigidity. During the magnet design process, it is often tempting to treat the optimization of the azimuthal and longitudinal preload systems separately. However, the two are inextricably related: as the cross-section preload increases, friction can prevent any motion in the longitudinal plane, and decreasing it can instead allow dangerous motions in the conductor ends. The latter can result in very high strains and, in Nb 3 Sn conductors, damage that can prevent the magnet to reach the desired performances. In an attempt to define design guidelines, in this paper we use simplified numerical models to compute, as a function of the in-plane prestress, the variation of the peak strains in the end region of the coils. Finally, we investigate the impact of the azimuthal prestress on a real magnet case, the High-Luminosity Nb 3 Sn Quadrupole MQXF.

Nb3Sn↗

DOE OSTI2018

Geometry Optimization and Charge Density Distribution of Single Layer of ZN-Based Metal-Organic Framework

The set of theoretical approaches were used to obtain the optimized geometries, electronic structure and charge density of single layer of metal-organic framework based on Zn [Zn{sub 2}(TBAP{sub y})(H{sub 2}O){sub 2} · 3.5DEF]{sub n} (MOF-Zn). Infinite monolayer composed of the unit cell of the MOF-Zn was considered. Ground state properties were researched using density functional theory with BLYP and PBE exchange-correlation functionals. The influence of the type of these approaches on the spatial structure and charge density was discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Effects of MEA Fabrication and Ionomer Composition on Fuel Cell Performance of PGM-Free ORR Catalyst

Carbon-based platinum group metal-free (PGM-free) catalysts for oxygen reduction reaction (ORR) have received increasing attention as potential candidates for low-cost fuel cell cathode catalysts. Mass-transport within the very thick catalyst layer (CL) presents a major challenge to further improve the fuel cell performance of PGM-free catalysts, which may be realized through the optimization of CL structure. Herein, we demonstrate that membrane electrode assemblies (MEAs) with not hot-pressed PGM-free cathode showed improved H 2 -air fuel cell performance in mass transport region when compared to MEAs prepared via hot-pressing technique. Further, the effects of the ionomer content and equivalent weight (EW) on fuel cell performance were systematically explored. We observed that an increase in ionomer content resulted in performance improvement in the kinetic region, while negatively affecting the performance in the mass transport region. The overall optimum fuel cell performance was achieved with an ionomer EW of 830 g mol -1 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Lattice Optimization for Porous Electrode

This code performs an optimization over the structure of a porous electrode formed from a lattice of unit cells. This is a steepest descent version of the code used in: V. A. Beck, J. J. Wong, C. F. Jekel, D. A. Tortorelli, S. E. Baker, E. B. Duoss and M. A. Worsley, Computational design of microarchitected porous electrodes for redox flow batteries, J. Power Sources, 2021, 512, 230453.

Wong, JonathanJ↗

DOE OSTI2025

SpectraCodec: A Hilbert curve-based method for encoding metadata in mass spectra for machine learning applications (SpectraCodec) v1

Machine learning approaches to mass spectrometry (MS) data analysis require structured metadata for optimal performance. However, current MS file formats necessitate external metadata sources, creating integration challenges that impede analytical workflows. Here, we present a novel approach for encoding metadata directly within mzML files using one-hot encoding of ASCII characters mapped via Hilbert space-filling curves. This strategy embeds metadata in the first spectrum's m/z-intensity space, ensuring persistence with the primary data, eliminating the need for external metadata files, and maintaining compatibility with existing MS software. We demonstrate that the Hilbert curve mapping efficiently utilizes the two-dimensional spectral space while maintaining robust data recovery. This method offers a practical solution for machine learning applications in mass spectrometry by ensuring metadata and spectral data remain unified through all stages of analysis.

Bowen, Benjamin [Lawrence Berkeley National Labora↗

DOE OSTI2024

Deep Learning Image Segmentation for Atmospheric Rivers

Abstract The identification of atmospheric rivers (ARs) is crucial for weather and climate predictions as they are often associated with severe storm systems and extreme precipitation, which can cause large impacts on society. This study presents a deep learning model, termed ARDetect, for image segmentation of ARs using ERA5 data from 1960 to 2020 with labels obtained from the TempestExtremes tracking algorithm. ARDetect is a convolutional neural network (CNN)-based U-Net model, with its structure having been optimized using automatic hyperparameter tuning. Inputs to ARDetect were selected to be the integrated water vapor transport (IVT) and total column water (TCW) fields, as well as the AR mask from TempestExtremes from the previous time step to the one being considered. ARDetect achieved a mean intersection-over-union (mIoU) rate of 89.04% for ARs, indicating its high accuracy in identifying these weather patterns and a superior performance than most deep learning–based models for AR detection. In addition, ARDetect can be executed faster than the TempestExtremes method (seconds vs minutes) for the same period. This provides a significant benefit for online AR detection, especially for high-resolution global models. An ensemble of 10 models, each trained on the same dataset but having different starting weights, was used to further improve on the performance produced by ARDetect, thus demonstrating the importance of model diversity in improving performance. ARDetect provides an effective and fast deep learning–based model for researchers and weather forecasters to better detect and understand ARs, which have significant impacts on weather-related events such as floods and droughts.

Galea, Daniel↗

DOE OSTI2021

High-throughput virtual laboratory for drug discovery using massive datasets

Time-to-solution for structure-based screening of massive chemical databases for COVID-19 drug discovery has been decreased by an order of magnitude, and a virtual laboratory has been deployed at scale on up to 27,612 GPUs on the Summit supercomputer, allowing an average molecular docking of 19,028 compounds per second. Over one billion compounds were docked to two SARS-CoV-2 protein structures with full optimization of ligand position and 20 poses per docking, each in under 24 hours. GPU acceleration and high-throughput optimizations of the docking program produced 350× mean speedup over the CPU version (50× speedup per node). GPU acceleration of both feature calculation for machine-learning based scoring and distributed database queries reduced processing of the 2.4 TB output by orders of magnitude. The resulting 50× speedup for the full pipeline reduces an initial 43 day runtime to 21 hours per protein for providing high-scoring compounds to experimental collaborators for validation assays.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Ultra-short beam expander with segmented curvature control: the emergence of a semi-lens

We introduce direct curvature control in designing a segmented beam expander, and explore novel design possibilities for ultra-compact beam expanders. Assisted by the particle swarm optimization algorithm, we search for an optimal curvature-controlled multi-segment taper that maintains width continuity. Counterintuitively, the optimization yields a structure with abrupt width discontinuity and width compression features. Through spatial phase and parameterized analysis, a semi-lens feature is revealed that helps to flatten the wavefront at the output end for higher coupling efficiency. Such functionality cannot be achieved by normal tapers in a short distance. The structure is fabricated and characterized experimentally. By a figure of merit that accounts for expansion ratio, length, and efficiency, this structure outperforms an adiabatic taper by 9 times.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2018

Quarterly Report on Node (FY2018_Q2): Advanced Electrode and Solid Electrolyte Materials for Elevated Temperature Water Electrolysis to Support UTRC HTE Project

The team in Idaho National Laboratory (INL) has made a good progress towards the milestones in this program to support UTRC. This technical report highlights progress by the HydroGEN Super Proton-Conducting High Temperature Electrodes (SPC HTE) Materials Node Capability in the following areas: (1) measurements of conductivities of two BCZY electrolytes; and investigate their stability in moist conditions (50% H 2 O, 200 hours)., (2) development of PBSCF electrode in proton conducting SOEC button-cell device, (3) performance improvement by optimizing micro-structure of steam electrode to achieve the target in milestone, and (4) research plan for the next quarter, to support UTRC HTE project.

08 HYDROGEN↗

DOE OSTI2018

Progress Summary: Simulating stochastic and additively manufactured foams at large strains and high strain rates

Motivation: Understanding the mechanical response of polymer foams is essential in developing system material models and in designing replacements for legacy pads and cushions. Simulations can help elucidate structure-property relationships and compare local stress distributions to understand aging behavior and improve designs. In many applications,these materials experience significant loadings and computations need the capability to reproduce those conditions. Significance: In designing the compressive and shear response of additively manufactured foams, we expect the curvature of the substrate to influence their mechanical behavior. Progress:We have the capability of mapping planar structures to arbitrary curved surfaces and are currently developing the software and procedures to translate the mapped pattern into a geometrical model that can be imported to either finite element or material point software to evaluate the mechanical response. Figure 1 depicts a four-layer face-centered tetragonal pattern (FCT) mapped to a hemispherical surface. Because the mapping cannot exactly preserve both distances and angles, we expect some local variation in mechanical properties and porosity. These models will help determine the sensitivity of these variations to structure and curvature, optimize the mapping procedure, and inform the design of foams for non-planar surfaces.

36 MATERIALS SCIENCE↗