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At least 289 records · Page 16

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DOE OSTI2020

Parallel Embeddings: a Visualization Technique for Contrasting Learned Representations

We introduce ``Parallel Embeddings'', a new technique that generalizes the classical Parallel Coordinates visualization technique from a sequence of features within a single matrix to a sequence of multiple feature matrices. This is accomplished by separately embedding each matrix in the sequence into a 2-D frame, juxtaposing these 2-D representations, then connecting points between frames. We outline how this visualization can be used for several ``model comparison'' tasks in machine learning. We compare user performance with Parallel Embeddings to a baseline using the TensorFlow Embedding Projector for estimating model accuracy and understanding conceptual model differences. We found that users were more accurate with Parallel Embeddings for model comparison and that users learned how to use Parallel Embeddings more quickly than TensorFlow Embedding Projector. Furthermore, users' analytical process using Embedding Projector was positively affected by using Parallel Embeddings beforehand.

Model comparison, machine learing, machine learnin↗

DOE OSTI2024

Multivariate Testing of Sampling Techniques to Address Class Imbalance in Building Use Type Classification

This study addresses the challenges inherent in building use type classification, particularly focusing on the issue of class imbalance in the training datasets for machine learning classifiers. We comprehensively analyze the efficacy of various class-balancing sampling techniques. Employing Monte Carlo simulations and Bayesian optimization, we evaluated the performance of multiple sampling methods, including Random Oversampling, Random Undersampling, SMOTE, Borderline-SMOTE, and ADASYN, across a dataset encompassing nine southeastern coastal states of the United States. Our findings reveal that simple random over- and undersampling techniques outperform more sophisticated methods. Additionally, we show inherent value in creating an imbalance in training data to effectively train a machine learning classifier for distinguishing between residential and nonresidential buildings. This study provides valuable guidance for future research on building use type classification research and lays essential groundwork for developing attribute-rich building stock datasets.

Adams, Daniel↗

DOE OSTI2026

Ultrasonic Resonance Techniques for Materials Research

Mechanical resonances are directly related to the physical behavior of a system at the bulk and microscopic levels. In materials science, resonant ultrasound spectroscopy (RUS) has long been a preferred nondestructive method to study mechanical resonances of solids and precisely measure quantitative material properties, namely elasticity. In recent years, advances in computational power and hardware have enabled RUS to be relevant for an increasing range of applications, such as advanced manufacturing. An extension of this technique, nonlinear RUS (NRUS), has been demonstrated to provide unmatched sensitivity to early-stage damage. NRUS was originally developed to probe geologic materials but has become a vital tool in nondestructive evaluation and materials research, offering a powerful means of quantifying and characterizing microstructural nonlinearity in a broad range of materials. This review summarizes recent developments and growth opportunities in RUS and NRUS techniques, modeling, and applications across a wide range of material systems including metals, composites, geomaterials, and explosives.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Many-Body Effects in Aqueous Systems: Synergies Between Interaction Analysis Techniques and Force Field Development

Interaction analysis techniques, including the many-body expansion (MBE), symmetry-adapted perturbation theory, and energy decomposition analysis, allow for an intuitive understanding of complex molecular interactions. We review these methods by first providing a historical context for the study of many-body interactions and discussing how nonadditivities emerge from Hamiltonians containing strictly pairwise-additive interactions. We then elaborate on the synergy between these interaction analysis techniques and the development of advanced force fields aimed at accurately reproducing the Born–Oppenheimer potential energy surface. In particular, we focus on ab initio–based force fields that aim to explicitly reproduce many-body terms and are fitted to high-level electronic structure results. These force fields generally incorporate many-body effects through ( a) parameterization of distributed multipoles, ( b) explicit fitting of the MBE, ( c) inclusion of many-atom features in a neural network, and ( d) coarse-graining of many-body terms into an effective two-body term. We also discuss the emerging use of the MBE to improve the accuracy and speed of ab initio molecular dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Stochastic Vector Techniques in Ground-State Electronic Structure

Herein we review a suite of stochastic vector computational approaches for studying the electronic structure of extended condensed matter systems. These techniques help reduce algorithmic complexity, facilitate efficient parallelization, simplify computational tasks, accelerate calculations, and diminish memory requirements. While their scope is vast, we limit our study to ground-state and finite temperature density functional theory (DFT) and second-order many-body perturbation theory. More advanced topics, such as quasiparticle (charge) and optical (neutral) excitations and higher-order processes, are covered elsewhere. We start by explaining how to use stochastic vectors in computations, characterizing the associated statistical errors. Next, we show how to estimate the electron density in DFT and discuss effective techniques to reduce statistical errors. Finally, we review the use of stochastic vectors for calculating correlation energies within the second-order Møller-Plesset perturbation theory and its finite temperature variational form. Example calculation results are presented and used to demonstrate the efficacy of the methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Inversion technique to obtain local ion temperature profiles for an axisymmetric plasma with toroidal and radial velocities

A matrix inversion technique is derived to calculate local ion temperature from line-integrated measurements of an extended emission source in an axisymmetric plasma which exactly corrects for both toroidal velocity and radial velocity components. Local emissivity and toroidal velocity can be directly recovered from line-integrated spectroscopic measurements, but an independent measurement of the radial velocity is necessary to complete the temperature inversion. The extension of this technique to handle the radial velocity is relevant for magnetic reconnection and merging compression devices where temperature inversion from spectroscopic measurements is desired. A simulation demonstrates the effects of radial velocity on the determination of ion temperature.

ion temperature↗

DOE OSTI2026

Calibration and Rapid-Adoption Forecasting Techniques

CRAFT (Calibration and Rapid-Adoption Forecasting Techniques) CRAFT is a Python-based project for processing, analyzing, and modeling atmospheric or environmental data. It uses machine learning techniques, specifically Random Forest Regression, to create emulators for various environmental variables such as gross primary production and soil water content. It then uses these emulators to robustly test the parameter space of mechanistic models to provide posterior estimations of the free parameters.

Robins, Zachary↗

DOE OSTI2024

Identifying Climate Patterns Using Clustering Autoencoder Techniques

Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Kurihana, Takuya↗

DOE OSTI2020

A Technique for Estimating Liquid Droplet Diameter and Liquid Water Content in Stratocumulus Clouds Using Radar and Lidar Measurements

This paper describes a technique for estimating the liquid water content (LWC) and a characteristic particle diameter in stratocumulus clouds using radar and lidar observations. The uncertainty in LWC estimate from radar and lidar measurements is significantly reduced once the characteristic particle diameter is known. The technique is independent of the drop size distribution. It is applicable for a broad range of W-band reflectivity Z between -30 and 0 dBZ and all values of lidar backscatter β observations. No partitioning of cloud or drizzle is required on the basis of an arbitrary threshold of Z as in prior studies. A method for estimating droplet diameter and LWC was derived from the electromagnetic simulations of radar and lidar observations. In situ stratocumulus cloud and drizzle probe spectra were input to the electromagnetic simulation. The retrieved droplet diameter and LWC were validated using in situ measurements from the southeastern Pacific Ocean. The retrieval method was applied to radar and lidar measurements from the northeastern Pacific. Uncertainty in the retrieved droplet diameter and LWC that are due to the measurement errors in radar and lidar backscatter measurements are 7% and 14%, respectively. The retrieved LWC was validated using the concurrent G-band radiometer estimates of the liquid water path.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Synthetic β-sheets mimicking fibrillar and oligomeric structures for evaluation of spectral X-ray scattering technique for biomarker quantification

Archetypical cross-β spines sharpen the boundary between functional and pathological proteins including β-amyloid, tau, α-synuclein and transthyretin are linked to many debilitating human neurodegenerative and non-neurodegenerative amyloidoses. An increased focus on development of pathogenic β-sheet specific fluid and imaging structural biomarkers and conformation-specific monoclonal antibodies in targeted therapies has been recently observed. Identification and quantification of pathogenic oligomers remain challenging for existing neuroimaging modalities. We propose two artificial β-sheets which can mimic the nanoscopic structural characteristics of pathogenic oligomers and fibrils for evaluating the performance of a label free, X-ray based biomarker detection and quantification technique. Highly similar structure with elliptical cross-section and parallel cross-β motif is observed among recombinant α-synuclein fibril, Aβ-42 fibril and artificial β-sheet fibrils. We then use these β-sheet models to assess the performance of spectral small angle X-ray scattering (sSAXS) technique for detecting β-sheet structures. sSAXS showed quantitatively accurate detection of antiparallel, cross-β artificial oligomers from a tissue mimicking environment and significant distinction between different oligomer packing densities such as diffuse and dense packings. The proposed synthetic β-sheet models mimicked the nanoscopic structural characteristics of β-sheets of fibrillar and oligomeric states of Aβ and α-synuclein based on the ATR-FTIR and SAXS data. The tunability of β-sheet proportions and shapes of structural motifs, and the low-cost of these β-sheet models can become useful test materials for evaluating β-sheet or amyloid specific biomarkers in a wide range of neurological diseases. By using the proposed synthetic β-sheet models, our study indicates that the sSAXS has potential to evaluate different stages of β-sheet-enriched structures including oligomers of pathogenic proteins.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Crystallization kinetics and thermodynamics of an Ag–In–Sb–Te phase change material using complementary in situ microscopic techniques

The crystallization of an amorphous Ag–In–Sb–Te (AIST) phase change material (PCM) is studied using multiple in situ imaging techniques to directly quantify crystal growth rates over a broad range of temperatures. The measurable growth rates span from ≈ 10 –9 to ≈ 20 m/s. Recent results using dynamic transmission electron microscopy (TEM), a photoemission TEM technique, and TEM with sub-framed imaging are reported here and placed into the context of previous growth rate measurements on AIST. Dynamic TEM experiments show a maximum observed crystal growth rate for as-deposited films to be > 20 m/s. It is shown that crystal growth above the glass transition can be imaged in a TEM through use of subframing and a high-frame-rate direct electron detection camera. Challenges associated with the determination of temperature during in situ TEM experiments are described. Finally, preliminary nanocalorimetry results demonstrate the feasibility of collecting thermodynamic data for crystallization of PCMs with simultaneous TEM imaging.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Improving the Transportability of a Deep Learning Denoising Model Using Transfer Learning Techniques

The adoption of machine learning techniques in the seismology community has led to great performance improvements in several areas, including signal processing. Specifically, the development of deep learning–based seismic waveform denoising models has the potential to yield improvements in signal detection capabilities for networks operating in particularly noisy environments. Recent advancements in the design of these deep learning denoising models have included the incorporation of continuous and discrete wavelet transform functions into the network architecture to improve the learning capabilities and efficiency of said models. These wavelet transform–based seismic denoising models have shown improved denoising capabilities in regions where there is good agreement between the data features present in the training and evaluation datasets. However, questions remain about the overall transportability of these models to other monitoring regions. Here, in this study, we will determine the baseline transportability of a newly developed multilevel wavelet‐transform convolutional neural network (MWCNN) seismic denoising model. We accomplish this by taking a version of the MWCNN denoising model trained on data collected from the Utah region and evaluating its denoising performance on datasets collected from the neighboring Nevada region, which differ with regard to monitoring sensor types and event histories. We find that there is a notable variability in denoising performance related to the degree of similarity between the initial and new target datasets. The most notable difference in denoising performance is the ability of the denoising model to preserve accurate amplitude information associated with the signal energy present in the waveform data. Finally, we evaluate the ability of transfer learning techniques to improve the transportability of the MWCNN denoising model. We find that although there is still a performance gap present in the denoising results of the MWCNN model, transfer learning did yield improved results.

Quinones, Louis [Sandia National Laboratories (SNL↗

DOE OSTI2026

New measurement techniques for gear-changing research using DESIREE

In this work we cover some of the newer techniques developed to measure the effects of a gear changing system maintained in DESIREE at Stockholm University. Gear-changing is a collider synchronization method where two rings with different harmonic numbers in them maintain collisions through different velocities, pathlengths or a combination of the two. This system has been demonstrated using the low energy ion collider DESIREE at Stockholm university. We have not only continued our previous methods of studying the beam using a repeating pattern technique where one bucket in each ring is intentionally left empty, but we now also use recently installed pickups outside of the merger region to study the beams separately while they collide.

Accelerator Physics↗

DOE OSTI2022

Magnetic Field Penetration Technique to Study High Field Shielding of Multilayered Superconductors

The SIS structure which consists of alternative thin layers of superconductors and insulators on a bulk niobium has been proposed to shield niobium cavity surface from high magnetic field and hence increase the accelerating gradient. The study of the behavior of multilayer super-conductors in an external magnetic field is essential to optimize their SRF performance. In this work we report the development of a simple and efficient technique to measure penetration of magnetic field into bulk, thin film and multilayer superconductors. Experimental setup contains a small superconducting solenoid which can produce a parallel surface magnetic field up to 0.5 T and Hall probes to detect penetrated magnetic field across the superconducting sample. This system was calibrated and used to study the effect of niobium sample thickness on the field of full magnetic flux penetration. We determined the optimum thickness of the niobium substrate to fabricate the multilayer structure for the measurements in our setup. This technique was used to measure penetration fields of Nb₃Sn thin films and Nb₃Sn/Al₂O₃ multi-layers deposited on Al₂O₃ wafers.

Senevirathne, I. H.↗

DOE OSTI2022

The crystal structure of Fe2S at 90 GPa based on single-crystal X-ray diffraction techniques

The Fe-S system was explored in a laser-heated diamond-anvil cell at 89(2) GPa and 2380(120) K to better understand the phase stability of Fe 2 S. Upon temperature quenching, crystallites of Fe 2 S were identified, and their structure was investigated using single-crystal X-ray diffraction techniques. At these conditions, Fe 2 S adopts the C23 structure (anti-PbCl 2 , Co 2 P) with space group Pnma (Z = 4). This structure consists of columns of corner-sharing, FeS 4 tetrahedra, and columns of edge-sharing FeS 5 square pyramids linked along edges in the b direction. Sulfur is in ninefold coordination with Fe. This work marks the first high-pressure structural solution and refinement of Fe 2 S synthesized in a multigrain Fe+FeS sample at 90 GPa and 2400 K and establishes the stability of C23 Fe 2 S at these conditions. A previous powder diffraction study reports an orthorhombic Fe 2 S phase with a C37, Co 2 Si-like unit cell above 190 GPa. A C23–C37 structural transition is inferred to explain the previously observed unit-cell parameters at higher pressures and temperatures. These results highlight the utility of applying single-crystal X-ray diffraction techniques to high P-T multigrain samples to explore the structural properties of iron-rich phases in Earth and planetary cores.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Growth of New High-Quality and Large-Size Single Crystals via High Pressure Floating-Zone Technique

This award supports experimental materials physics research on a class of technologically relevant materials that cannot form under ambient conditions. With the complementary expertise at Louisiana State University (LSU) and infrastructure at Oak Ridge National Laboratory (ORNL), single crystals of these materials will be synthesized using the high-pressure floating-zone technique. Our ability to grow high-quality single crystals will facilitate advanced characterization of these materials and improve the scientific outcomes. In addition to in-house material characterization, the principle investigator will partner with scientists at ORNL and Brookhaven National Laboratory (BNL) to tackle materials problems using the state-of-the-art characterization techniques. This will allow us to explore the fundamental physics underlying the emergent phenomena only seen in materials prepared under extreme conditions. Our ultimate goal is to address one of the grand challenges of materials science: how do complex phenomena emerge from simple ingredients? The proposed project will also spearhead a unique opportunity to train “new breed” scientists, co-advised by experts at both LSU and U. S. Department of Energy National Laboratories. They will be essential for expanding materials physics research with ability to grow crystals, characterize their properties, and design new materials -- an imperative skill that is scarce in the U. S. scientific community.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Detection Thresholds and Sensitivities of Geophysical Techniques for CO2 Plume Monitoring

This report assesses capabilities and limitations of different geophysical techniques for monitoring geologic sequestration of CO 2 . Seismic, gravity, electrical, and electromagnetic (EM) techniques are considered. Monitoring approaches for site characterization prior to CO 2 injection are different from those used while injecting CO 2 , or those suitable for post-injection monitoring. The focus of this study is on the aspects relevant to long-term monitoring of large areas in the post-injection phase, and specifically on early detection of secondary CO 2 plumes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Novel In-situ Patterning Technique to Fabricate Single Quantum Dots for Quantum Photonics

Photon sources able to emit single or entangled photon pairs are key components in quantum information systems. Semiconductor quantum dots (QDs) are promising candidates due to their high efficiencies and ease of integration with other photonic or electronic components. State-of-the-art QDs, however, are limited to certain emission wavelengths and specific applications due to material choice constraints and their randomness in shape/size. This project is focused on developing a novel in-situ patterning technique to realize QDs with a broad emission range, shape/size control and the ability to emit single/entangled photons. Our approach has two key elements: (1) In-situ patterning via arsenic-induced nanovoid etching on antimonide surfaces and (2) In-filling of nanovoids to form QDs. By closely controlling the experimental conditions, it is shown that this technique can be used to realize III-V QDs in As 2 - etched nanovoids on a GaSb surface. The exposure to As 2 in terms of substrate temperature, time and flux is found to have a significant impact on the process variables such as nanovoid depth, QD dimensions etc. An in-depth analysis of the etch mechanism reveals that by controlling the As 2 exposure, uniform 3-dimensional nanostructures with varying areal densities can be obtained without an in-filling step. Preliminary optical characterization of these nanostructures shows that these QDs may be relevant for realizing emitters in the telecom wavelength range.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗