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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Ternary mixed-anion semiconductors with tunable band gaps from machine-learning and crystal structure prediction

In this paper, we report the computational investigation of a series of ternary $X_4Y_2Z$ and $X_5Y_2Z_2$ compounds with $X$ =Mg, Ca, Sr, Ba; $Y$ =P, As, Sb, Bi; and $Z$ =S, Se, Te. The compositions for these materials were predicted through a search guided by machine learning, while the structures were resolved using the minima hopping crystal structure prediction method. Based on ab initio calculations, we predict that many of these phases are thermodynamically stable. In particular, 21 of the $X_4Y_2Z$ compounds crystallize in a tetragonal structure with $I\bar{4}2d$ symmetry, and exhibit band gaps in the range of 0.8 and 1.8 eV, well suited for various energy applications. We show that several candidates (in particular $X_4Y_2$ Te and $X_4\mathrm{Sb_2Se}$) exhibit good photo absorption in the visible range, while others (e.g., $\mathrm{Ba_4Sb_2Se}$) show excellent thermoelectric performance due to high power factors and extremely low lattice thermal conductivities.

36 MATERIALS SCIENCE↗
DOE OSTI2015

WE-G-BRA-04: Common Errors and Deficiencies in Radiation Oncology Practice

Purpose: Dosimetric errors in radiotherapy dose delivery lead to suboptimal treatments and outcomes. This work reviews the frequency and severity of dosimetric and programmatic errors identified by on-site audits performed by the IROC Houston QA center. Methods: IROC Houston on-site audits evaluate absolute beam calibration, relative dosimetry data compared to the treatment planning system data, and processes such as machine QA. Audits conducted from 2000-present were abstracted for recommendations, including type of recommendation and magnitude of error when applicable. Dosimetric recommendations corresponded to absolute dose errors >3% and relative dosimetry errors >2%. On-site audits of 1020 accelerators at 409 institutions were reviewed. Results: A total of 1280 recommendations were made (average 3.1/institution). The most common recommendation was for inadequate QA procedures per TG-40 and/or TG-142 (82% of institutions) with the most commonly noted deficiency being x-ray and electron off-axis constancy versus gantry angle. Dosimetrically, the most common errors in relative dosimetry were in small-field output factors (59% of institutions), wedge factors (33% of institutions), off-axis factors (21% of institutions), and photon PDD (18% of institutions). Errors in calibration were also problematic: 20% of institutions had an error in electron beam calibration, 8% had an error in photon beam calibration, and 7% had an error in brachytherapy source calibration. Almost all types of data reviewed included errors up to 7% although 20 institutions had errors in excess of 10%, and 5 had errors in excess of 20%. The frequency of electron calibration errors decreased significantly with time, but all other errors show non-significant changes. Conclusion: There are many common and often serious errors made during the establishment and maintenance of a radiotherapy program that can be identified through independent peer review. Physicists should be cautious, particularly in areas highlighted herein that show a tendency for errors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Compact Coaxial HOM Damper for SRF Cavities

SRF technology enable particle accelerators operate with greater average beam currents and higher duty cycles. In these regimes parasitic excitation of the cavity HOM spectrum become the limiting factor due to extra RF losses and instabilities appearing in the beam. We discuss the limitations of HOMs for large accelerator projects as the Fermilab MI upgrade and the proposed future circular and linear colliders EIC and ILC. A new compact HOM damper concept is suggested providing required HOM suppression. The idea is based on an oversized coaxial line with radial sections, which is directly connected to the axial region of the SRF cavity. Such radially sectioned coaxial works as a filter for the operating mode, while being transparent for the HOMs. Implementations of this design for the accelerator and crab cavities developed for the MI and EIC machines, respectively, are discussed.

Lunin, A. [Fermilab]↗
DOE OSTI2019

FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing

Large-scale particle physics experiments face challenging demands for high-throughput computing resources both now and in the future. New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that potentially requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) ms with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600–700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Variational Optical Phase Learning on a Continuous-Variable Quantum Compiler

Quantum process learning is a fundamental primitive that draws inspiration from machine learning with the goal of better studying the dynamics of quantum systems. One approach to quantum process learning is quantum compilation, whereby an analog quantum operation is digitized by compiling it into a series of basic gates. While there has been significant focus on quantum compiling for discrete-variable systems, the continuous-variable (CV) framework has received comparatively less attention. We present an experimental implementation of a CV quantum compiler that uses two-mode squeezed light to learn a Gaussian unitary operation. We demonstrate the compiler by learning a parameterized linear phase unitary through the use of target and control phase unitaries to demonstrate a factor of 5.4 increase in the precision of the phase estimation and a 3.6-fold acceleration in the time-to-solution metric when leveraging quantum resources. We further show how our approach can be extended to higher-dimensional compilation tasks. Our results are enabled by the tunable control of our cost landscape via variable squeezing, thus providing a critical framework to simultaneously increase precision and reduce time-to-solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

FPGAs as a Service to Accelerate Machine Learning Inference [PowerPoint]

Large-scale particle physics experiments face challenging demands for high-throughput computing resources both now and in the future. New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC, and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave b y Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) milliseconds with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600--700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Large-volume centimeter-wave cavities for axion searches

The scan rate of an axion haloscope is proportional to the square of the cavity volume. In this paper, a new class of thin-shell cavities are proposed to search for axionic dark matter. These cavities feature active volume much larger (>20×) than that of a conventional cylindrical haloscope, comparable quality factor Q, and a similar frequency tuning range. Furthermore, full 3D numerical finite-element analyses have been used to show that the TM 010 eigenmodes are singly polarized throughout the volume of the cavity and can facilitate axion-photon conversion in uniform magnetic field produced by a superconducting solenoid. To mitigate spurious mode crowding and volume losses due to localization, a pre-amplification binary summing network will be used for coupling. Because of the favorable frequency-scaling, the new cavities are most suitable for centimeter-wavelength (~ 10–100 GHz), corresponding to the promising post-inflation axion production window. In this frequency range, the tight machining tolerances required for high-Q thin-shell cavities are achievable with standard machining techniques for near-infrared mirrors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Statistical relationships across epigenomes using large-scale hierarchical clustering

Recent advances in genomics and sequencing platforms have revolutionized our ability to create immense data sets, particularly for studying epigenetic regulation of gene expression. However, the avalanche of epigenomic data is difficult to parse for biological interpretation given nonlinear complex patterns and relationships. This attractive challenge in epigenomic data lends itself to machine learning for discerning infectivity and susceptibility. In this study, we explore over 3000 epigenomes of uninfected individuals and provide a framework to characterize the relationships among epigenetic modifiers, their modifiers, genetic loci, and specific immune cell types across all chromosomes using hierarchical clustering. Hierarchical clustering of epigenomic data revealed consistent epigenetic patterns across chromosomes, demonstrating that variation due to epigenetic modifiers is greater than variation between cell types. Gene Ontology and KEGG pathway analyses indicated significant enrichment of genes involved in chromatin remodeling, mRNA splicing, immune responses, and the regulation of microRNAs and snoRNAs. Epigenetic modifiers frequently formed biologically relevant clusters, including the cohesin complex, RNA Polymerase II transcription factors, and PRC2 complex members. These clustering behaviors remained consistent across all chromosomes, supported by entropy analysis and high Adjusted Rand Index scores, indicating robust cross-chromosomal similarity. Co-occurrence analysis further revealed specific sets of modifiers that consistently appeared together within clusters, reflecting shared biological functions and interactions. Validation using another dataset confirmed the reproducibility of these clustering patterns and modifier co-occurrence relationships, underscoring the reliability and generalizability of the methodology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Numerical Analysis of High-Order Modes in SRF Resonators for Particle Accelerators

Over the past decades, superconducting technology has rapidly evolved towards high accelerating gradients and low surface resistance, making it possible to operate particle accelerators with high average beam currents and large duty factors. However, RF losses due to coherent excitation of the HOM become the limiting factor for these regimes. Unlike the cavity operating mode, which is tuned separately, the HOM parameters can significantly vary from one cavity to another due to finite mechanical tolerances during the manufacturing process. Thus, it is of utmost importance to know the HOM parameter spread in advance in order to predict unexpected cryogenic losses, overheating of beam line components and maintain stable beam dynamics. In this paper, we present a method for generating cavity geometry with an arbitrary spread of mechanical imperfections and numerically evaluating HOM statistics. Knowing the spread of HOM parameters, we calculated the probability of resonant HOM losses in SRF accelerating cavities used in CW beam current machines such as the PIP-II and LCLS-II linacs, as well as for the SRF crab-cavity for the ILC project. Finally, we present experimental results of HOM spectra measurements in hundreds of 1.3 GHz cavities installed in LCLS-II cryomodules. Studying the effects of HOM excitation results in specifications of the SRF cavity and cryomodule and can significantly impact the efficiency and reliability of the machine operation.

Lunin, Andrei [Fermilab] (ORCID:0000000290096792)↗
DOE OSTI2024

Accelerating the Discovery of New, Single Phase High Entropy Ceramics via Active Learning

High-entropy ceramics have garnered interest due to their remarkable hardness, compressive strength, thermal stability, and fracture toughness; yet the discovery of new high-entropy ceramics (out of a tremendous number of possible elemental permutations) still largely requires costly, inefficient, trial-and-error experimental and computational approaches. The entropy forming ability (EFA) factor was recently proposed as a computational descriptor that positively correlates with the likelihood that a 5-metal high-entropy carbide (HECs) will form the desired single phase, homogeneous solid solution; however, discovery of new compositions is computationally expensive. If you consider 8 candidate metals, the HEC EFA approach uses 49 optimizations for each of the 56 unique 5-metal carbides, requiring a total of 2744 costly density functional theory calculations. Here, we describe an orders-of-magnitude more efficient active learning (AL) approach for identifying novel HECs. To begin, we compared numerous methods for generating composition-based feature vectors (e.g., magpie and mat2vec), deployed an ensemble of machine learning (ML) models to generate an average and distribution of predictions, and then utilized the distribution as an uncertainty. Here we then deployed an AL approach to extract new training data points where the ensemble of ML models predicted a high EFA value or was uncertain of the prediction. Our approach has the combined benefit of decreasing the amount of training data required to reach acceptable prediction qualities and biases the predictions toward identifying HECs with the desired high EFA values, which are tentatively correlated with the formation of single phase HECs. Using this approach, we increased the number of 5-metal carbides screened from 56 to 15,504, revealing 4 compositions with record-high EFA values that were previously unreported in the literature. Our AL framework is also generalizable and could be modified to rationally predict optimized candidate materials/combinations with a wide range of desired properties (e.g., mechanical stability, thermal conductivity).

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep potential generation scheme and simulation protocol for the Li 10 GeP 2 S 12 -type superionic conductors

We report solid-state electrolyte materials with superior lithium ionic conductivities are vital to the next-generation Li-ion batteries. Molecular dynamics could provide atomic scale information to understand the diffusion process of Li-ion in these superionic conductor materials. Here, we implement the deep potential generator to set up an efficient protocol to automatically generate interatomic potentials for Li 10 GeP 2 S 12 -type solid-state electrolyte materials (Li 10 GeP 2 S 12 , Li 10 SiP 2 S 12 , and Li 10 SnP 2 S 12 ). The reliability and accuracy of the fast interatomic potentials are validated. With the potentials, we extend the simulation of the diffusion process to a wide temperature range (300 K–1000 K) and systems with large size (~1000 atoms). Important technical aspects such as the statistical error and size effect are carefully investigated, and benchmark tests including the effect of density functional, thermal expansion, and configurational disorder are performed. The computed data that consider these factors agree well with the experimental results, and we find that the three structures show different behaviors with respect to configurational disorder. Our work paves the way for further research on computation screening of solid-state electrolyte materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Experimental energy confinement time scaling with dimensionless parameters in C-Mod I-mode plasmas

The dependence of energy confinement time on gyroradius, β, and normalized collisionality in I-mode plasmas is investigated through dedicated C-Mod experiments scanning dimensionless parameters with constant safety factor. The gyroradius scaling is calculated to be Ω τE ∝ ρ * -3.9±1.5 , which suggests core transport may scale with gyro–Bohm physics, indicating favorable extrapolation of the I-mode regime to future devices at low ρ * . The scaling exponent for ν C is calculated to be small, but positive (Ω τE ∝ ν $^{0.44 ± 0.24}_{C}$), and the exponent for β is deemed inconclusive (Ω τE ∝ β 1.4±3.1 ) due to high correlation with the other two dimensionless variables in the dataset, and therefore requires further investigation. The individual dimensionless parameter scaling is compared to calculations from larger C-Mod I-mode datasets as well as multi-machine scaling laws for ELMy H-mode and L-mode plasmas. Finally, multiple regression techniques and principal component analysis are used to clarify the single parameter scalings and analyze parameter significance within the dataset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Laser Plasma Interaction in the MG Magnetic Field (Final Report)

Strong magnetic fields change properties of plasma. Employment of the magnetic fields can improve conditions for inertial confinement fusion. Compression and heating of the magnetized plasma is a base of the MagLIF pulsed power approach to the controlled fusion. Study of expansion and heating of plasma in the external magnetic fields, development of plasma instabilities, parametric and other effects are important for basic physics and applications. The main goal of the research for the grant was experimental investigation of laser produced plasma in the well characterized and controlled MG magnetic fields. We developed a platform for investigation of high intensity laser-plasma interaction (LPI) in the MG fields using a university-scale pulsed power machine. A Zebra machine at UNR generated transverse magnetic fields of 3 MG on the surface of the rod load and longitudinal fields of 1.4 MG in the coil loads. The magnetic fields were characterized with two-color Faraday rotation diagnostics. The pulsed power machine provided almost static magnetic fields for LPI compared to the much faster laser driven magnetic fields. We found that eddy currents in the rising magnetic field generate plasma on the metal targets. Eddy currents did not impact Si and CH dielectric targets. A Leopard laser with intensity in the focus up to 10 19 W/cm 2 produced plasma in the MG magnetic fields. The modified reflected Faraday diagnostics was used to study laser driven B-field at the MTW laser at Laboratory for Laser Energetics, UR. We demonstrated a slow dependence of the magnetic field in the coil on the applied laser intensity. The magnetic field increased by a factor of 2 if laser intensity increased by a factor of 30. This scaling is important for comparison of experiments at different laser facilities. Expansion of the plasma in the 2-3 MG external B-field was studied. Laser-produced plasma in the azimuthal magnetic field took the unique form of a thin disc expanding radially with a velocity of 250 km/s and confined in the vertical direction. A HYDRA MHD program at the University of Rochester was applied for simulations of LPI in the magnetic field B >3 MG. Simulations for the plasma disc parameters and the expansion dynamics were in a good agreement with experiments. Generation of narrow dense plasma jets in the longitudinal magnetic field of 0.6-0.8 MG was studied. Narrow plasma jets reached a length of 3-4 mm with the electron plasma density of (0.2-1.2)x10 20 cm -3 . A jet tip propagated with the velocity of 160-200 km/s. MHD simulations showed a good agreement of the dynamics of the formation of plasma jets with experiments. These jets are relevant to astrophysical jets. Simulations of K-shell spectra of Si plasma was performed for LPI in the B-field. The laser prepulse generated plasma near the laser target. A PrismSPECT model with a MeV electron beam produced by the laser was used. The spectral modelling showed the increased plasma density by a factor by 2-3 in the magnetic field. This was in agreement with the observed dynamics of the plasma plume confined by the axial magnetic field. The two-plasmon decay was studied in the 2.5-3 MG transversal magnetic fields. Strong 2-3.5 nm spectral widening and a 2-4 nm shifts of “red” and “blue” 3/2ω 0 spectral components were observed. The large shift and widening exceed the expected temperature and magnetic shift. PIC simulations are performing to clarify physics of this effect. The experimental research program for the grant is completed. New plasma effects in the MG fields were studied at the university-scale pulsed power generator. Two graduate and two undergraduate UNR and UR students carried experiments at the Zebra generator, supported and developed plasma diagnostics, provided data processing, and performed MHD simulations of LPI in the strong magnetic field. Research results are published in 9 referred papers and presented in 12 conferences. Program manager: Kramer U. Akli; Collaborators: R. C. Mancini and H. Sawada, University of Nevada, Reno (UNR); R. Betti and A. V. Maximov, University of Rochester (UR).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Learning an Algebriac Multrigrid Interpolation Operator Using a Modified GraphNet Architecture

This work, building on previous efforts, develops a suite of new graph neural network machine learning architectures that generate data-driven prolongators for use in Algebraic Multigrid (AMG). Algebraic Multigrid is a powerful and common technique for solving large, sparse linear systems. Its effectiveness is problem dependent and heavily depends on the choice of the prolongation operator, which interpolates the coarse mesh results onto a finer mesh. Previous work has used recent developments in graph neural networks to learn a prolongation operator from a given coefficient matrix. In this paper, we expand on previous work by exploring architectural enhancements of graph neural networks. A new method for generating a training set is developed which more closely aligns to the test set. Asymptotic error reduction factors are compared on a test suite of 3-dimensional Poisson problems with varying degrees of element stretching. Results show modest improvements in asymptotic error factor over both commonly chosen baselines and learning methods from previous work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

On the compatibility of established methods with emerging artificial intelligence and machine learning methods for disaster risk analysis

Abstract There is growing interest in leveraging advanced analytics, including artificial intelligence (AI) and machine learning (ML), for disaster risk analysis (RA) applications. These emerging methods offer unprecedented abilities to assess risk in settings where threats can emerge and transform quickly by relying on “learning” through datasets. There is a need to understand these emerging methods in comparison to the more established set of risk assessment methods commonly used in practice. These existing methods are generally accepted by the risk community and are grounded in use across various risk application areas. The next frontier in RA with emerging methods is to develop insights for evaluating the compatibility of those risk methods with more recent advancements in AI/ML, particularly with consideration of usefulness, trust, explainability, and other factors. This article leverages inputs from RA and AI experts to investigate the compatibility of various risk assessment methods, including both established methods and an example of a commonly used AI‐based method for disaster RA applications. This article utilizes empirical evidence from expert perspectives to support key insights on those methods and the compatibility of those methods. This article will be of interest to researchers and practitioners in risk‐analytics disciplines who leverage AI/ML methods.

Mathematical Methods In Social Sciences↗
DOE OSTI2023

Contextually aware roadside radiation measurement testbed

Here we demonstrate a contextually aware multimodal roadside radiation measurement detection testbed for traffic monitoring applications in nuclear nonproliferation. Many variables in traffic such as vehicle or cargo size, mass, speed, shape, and distance of closest approach can have significant impacts on the radiation measured from a vehicle-transported radiation source. These factors can lead to uncertainties in the analysis of the radiation source, especially for lower-strength radiation sources of interest. Our testbed, known as the Multimodal Measurement System (MMS) uses non-radiation sensors including magnetometers, geophones, radiofrequency receivers, cameras, and LiDAR to extract contextual information about vehicles passing by the system. These contextual data can then be fused with data from radiation measurements to increase the system’s sensitivity and accuracy in nuclear threat detection applications. This work describes the instrumentation of the MMS and its data acquisition pipeline. Furthermore, we describe the pre-analysis performed on the raw multimodal data streams for data fusion, and the high-level machine learning analyses for detection and characterization. The variety of sensors within the MMS provides a valuable testbed that can be used to identify the combinations of contextual sensors that provide the greatest improvements to radiation source detection and characterization within the restrictions for various proliferation detection applications. The MMS is also modular so that additional combinations of sensors can be explored in the future.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

New strategies for new physics search with Λ b → Λ ν ν ¯ decays

We examine the new physics sensitivity of the rare decay Λ b → Λ ν ν ¯ , which can be accessible at future Z -pole machines like Future Circular Collider and Circular Electron Positron Collider. We find that the longitudinal polarization of Λ b baryons produced in Z decays introduces a novel observable, the forward-backward asymmetry A FB ↑ in the angle between the outgoing Λ momentum and the Λ b spin. We provide Standard Model predictions for the Λ b → Λ ν ν ¯ branching ratio and A FB ↑ , and show that future precision measurements of these observables are complementary and probe new physics scales comparable to other b → s ν ν ¯ and b → s ℓ + ℓ − processes. We also show that the zero crossing of the forward-backward asymmetry offers a robust test of form factor calculations independent of new physics. Published by the American Physical Society 2025

Altmannshofer, Wolfgang (ORCID:0000000316212561)↗