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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Higher order topological superconductivity in magnet-superconductor hybrid systems

Abstract Quantum engineering of topological superconductors and of the ensuing Majorana zero modes might hold the key for realizing topological quantum computing and topology-based devices. Magnet-superconductor hybrid (MSH) systems have proven to be experimentally versatile platforms for the creation of topological superconductivity by custom-designing the complex structure of their magnetic layer. Here, we demonstrate that higher order topological superconductivity (HOTSC) can be realized in two-dimensional MSH systems by using stacked magnetic structures. We show that the sensitivity of the HOTSC to the particular magnetic stacking opens an intriguing ability to tune the system between trivial and topological phases using atomic manipulation techniques. We propose that the realization of HOTSC in MSH systems, and in particular the existence of the characteristic Majorana corner modes, allows for the implementation of a measurement-based protocols for topological quantum computing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

One-step fabrication of robust lithium ion battery separators by polymerization-induced phase separation

Conventional lithium ion battery separators are microporous polyolefin membranes that play a passive role in the electrochemical cell. Next generation separators should offer significant performance enhancements, while being fabricated through facile, low cost approaches with the ability to readily tune physicochemical properties. This study presents a single-step manufacturing technique based on UV-initiated polymerization-induced phase separation (PIPS), wherein microporous separators are fabricated from multifunctional monomers and ethylene carbonate (EC), which functions as both the pore-forming agent (porogen) and electrolyte component in the electrochemical cell. By controlling the ratio of the 1,4-butanediol diacrylate (BDDA) monomer to ethylene carbonate, monolithic microporous membranes are readily prepared with 25 μm thickness and pore sizes and porosities ranging from 6.8 to 22 nm and 15.4% to 38.5%, respectively. With 38.5% apparent porosity and an average pore size of 22 nm, the poly(1,4-butanediol diacrylate) (pBDDA) separator takes up 127% liquid electrolyte, resulting in an ionic conductivity of 1.98 mS cm –1 , which is greater than in conventional Celgard 2500. Lithium ion battery half cells consisting of LiNi 0.5 Mn 0.3 Co 0.2 O 2 cathodes and pBDDA separators were shown to undergo reversible charge/discharge cycling with an average discharge capacity of 142 mA h g –1 and a capacity retention of 98.4% over 100 cycles – comparable to cells using state-of-the-art separators. Moreover, similar discharge capacities were achieved in rate performance tests due to the high ionic conductivity and electrolyte uptake of the film. Finally, the pBDDA separators were shown to be thermally stable to 374 °C, lack low temperature thermal transitions that can compromise cell safety, and exhibit no thermal shrinkage up to 150 °C.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

An Iterative Machine Learning Framework for Event Classification and Monte Carlo Tuning in SpinQuest

The E1039/SpinQuest experiment at Fermi National Accelerator Laboratory uses a 120~GeV proton beam from the Main Injector incident on transversely polarized proton and deuteron targets, using $NH_3$ and $ND_3$, respectively. In addition to measuring the Sivers asymmetry in Drell--Yan $pp$ and $pd$ scattering from sea quarks, SpinQuest will study transverse-spin effects, particularly the transverse single-spin asymmetry (TSSA) in $J/\psi$ production. The angular distributions from the $J/\psi$ decay could play an important role in understanding the gluon contribution to the proton spin structure. However, before extracting these angular distributions, it is necessary to isolate signal events originating from the target from events produced by other sources and from the combinatorial background. To effectively and accurately classify the target events, it is important to ensure that the simulated events are properly tuned to the experimental physics channels. We have introduced an iterative technique to match simulated and experimental events and to classify the physics channels using deep neural networks and a generative model based on normalizing flows.

Hossain, Forhad [Virginia U. (main)] (ORCID:000000↗
DOE OSTI2025

Controlling beam trajectory and transport in a tapered helical undulator

In this paper we present a detailed discussion of the helical undulator system developed for high extraction efficiency experiments in the tapered-enhanced stimulated superradiant amplification regime. The design is based on permanent magnet technology and comprises two Halbach arrays orthogonally oriented and shifted by 90° with respect to each other. When used in low energy beamlines for THz generation, the electron beam trajectory and transport are particularly sensitive to the undulator off-axis fields so that it becomes important to complement on-axis field measurements with analysis and tuning of the higher-order field components. Here we describe how the pulsed wire measurement technique can be effectively used to retrieve on- and off-axis magnetic field characteristics of the undulator. A simple two-dipole model is developed to guide the final adjustments to the permanent magnet positions along the array to tune the quadrupole and sextupole components of the field.

free-electron lasers↗
DOE OSTI2025

Machine learning for arbitrary single-qubit rotations on an embedded device

Here, in this study, we present a technique for using machine learning (ML) for single-qubit gate synthesis on field-programmable logic for a superconducting transmon-based quantum computer based on simulated studies. Our approach is multi-stage. We first “bootstrap” a model based on simulation with access to the full state vector for measuring gate fidelity. We next present an algorithm, named adapted randomized benchmarking (ARB), for fine-tuning the gate on hardware based on measurements of the devices. We also present techniques for deploying the model on programmable devices with care to reduce the required resources. While the techniques here are applied to a transmon-based computer, many of them are portable to other architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Optimizing Distributed Training on Frontier for Large Language Models

Large language models (LLMs) have demonstrated remarkable success as foundational models, benefiting various downstream applications through fine-tuning. Loss scaling studies have demonstrated the superior performance of larger LLMs compared to their smaller counterparts. Nevertheless, training LLMs with billions of parameters poses significant challenges and requires considerable computational resources. For example, training a one trillion parameter GPT-style model on 20 trillion tokens requires a staggering 120 million exaflops. This research explores efficient distributed training strategies to extract this computation from Frontier, the world's first exascale supercomputer. We enable and investigate various model and data parallel training techniques, such as tensor parallelism, pipeline parallelism, and sharded data parallelism, to facilitate training a trillion-parameter model on Frontier. We empirically assess these techniques and their associated parameters to determine their impact on memory footprint, communication latency, and GPU's computational efficiency. We analyze the complex interplay among these techniques and find a strategy to combine them to achieve high throughput through hyperparameter tuning. We have identified efficient strategies for training large LLMs of varying sizes through empirical analysis and hyperparameter tuning. For 22 Billion, 175 Billion, and 1 Trillion parameters, we achieved GPU throughputs of 38.38%, 36.14%, and 31.96%, respectively. For the training of the 175 Billion parameter model and the 1 Trillion parameter model, we achieved 100% weak scaling efficiency on 1024 and 3072 Mi250X GPUs, respectively. We also achieved strong scaling efficiencies of 89% and 87% for these two models. We trained these models only tens of iterations instead of training till completion.

Yin, Junqi↗
DOE OSTI2024

Rapid curing dynamics of PEG-thiol-ene resins allow facile 3D bioprinting and in-air cell-laden microgel fabrication

Thiol-norbornene photoclick hydrogels are highly efficient in tissue engineering applications due to their fast gelation, cytocompatibility, and tunability. In this work, we utilized the advantageous features of polyethylene glycol (PEG)-thiol-ene resins to enable fabrication of complex and heterogeneous tissue scaffolds using 3D bioprinting and in-air drop encapsulation techniques. We demonstrated that photoclickable PEG-thiol-ene resins could be tuned by varying the ratio of PEG-dithiol to PEG norbornene to generate a wide range of mechanical stiffness (0.5–12 kPa) and swelling ratios. Importantly, all formulations maintained a constant, rapid gelation time (<0.5 s). We used this resin in biological projection microstereolithography (BioPµSL) to print complex structures with geometric fidelity and demonstrated biocompatibility by printing cell-laden microgrids. Moreover, the rapid gelling kinetics of this resin permitted high-throughput fabrication of tunable, cell-laden microgels in air using a biological in-air drop encapsulation apparatus (BioIDEA). We demonstrated that these microgels could support cell viability and be assembled into a gradient structure. This PEG-thiol-ene resin, along with BioPµSL and BioIDEA technology, will allow rapid fabrication of complex and heterogeneous tissues that mimic native tissues with cellular and mechanical gradients. The engineered tissue scaffolds with a controlled microscale porosity could be utilized in applications including gradient tissue engineering, biosensing, and in vitro tissue models.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Mid-infrared spectroscopy with a broadly tunable thin-film lithium niobate optical parametric oscillator

Mid-infrared spectroscopy, an important technique for sensing molecules, has encountered barriers from sources either limited in tuning range or excessively bulky for widespread use. We present a compact, efficient, and broadly tunable optical parametric oscillator surmounting these challenges. Leveraging dispersion-engineered thin-film lithium niobate-on-sapphire photonics and a singly resonant cavity allows broad, controlled tuning over an octave from 1.5–3.3 µm. The device generates >25mW of mid-infrared light at 3.2 µm with 15% conversion efficiency. The ability to precisely control the device’s mid-infrared emission enables spectroscopy of methane and ammonia, demonstrating our approach’s relevance for sensing. Our work signifies an important advance in nonlinear photonics miniaturization, bringing practical field applications of high-speed, broadband mid-infrared spectroscopy closer to reality.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, G. C.↗
DOE OSTI2021

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, Chris↗
DOE OSTI2021

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, Chris↗
DOE OSTI2020

Improved modeling of β electronic recoils in liquid xenon using LUX calibration data

We report here methods and techniques for creating and improving a model that reproduces the scintillation and ionization response of a dual-phase liquid and gaseous xenon time-projection chamber. Starting with the recent release of the Noble Element Simulation Technique (NEST v2.0), electronic recoil data from the $\beta$ decays of ${}^3$H and ${}^{14}$C in the Large Underground Xenon (LUX) detector were used to tune the model, in addition to external data sets that allow for extrapolation beyond the LUX data-taking conditions. This paper also presents techniques used for modeling complicated temporal and spatial detector pathologies that can adversely affect data using a simplified model framework. The methods outlined in this report show an example of the robust applications possible with NEST v2.0, while also providing the final electronic recoil model and detector parameters that will used in the new analysis package, the LUX Legacy Analysis Monte Carlo Application (LLAMA), for accurate reproduction of the LUX data. As accurate background reproduction is crucial for the success of rare-event searches, such as dark matter direct detection experiments, the techniques outlined here can be used in other single-phase and dual-phase xenon detectors to assist with accurate ER background reproduction.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Fermilab Booster loss modelling and rebalancing using Bayesian methods

To meet PIP-II upgrade requirements, Fermilab Booster losses need to be reduced by 50% compared to present levels. So far, simulations are not good enough to predict loss patterns. Thus, an extensive Booster tune up will be necessary to achieve required performance. In this paper we present an effort to build a data-driven loss model using Bayesian techniques, and subsequently to rebalance losses for higher trip margins. We first created several sets of spatially and temporally isolated orbit and optics knobs, and trained Gaussian process models for each beam loss monitor as well as beam current. Novel techniques of uncertainty constraints and approximate GP fitting were introduced to handle safety and timing requirements. We then performed single and multi-objective tuning using scalarized objectives comprised of critical beam loss locations. We achieved significant rebalancing of losses, increasing margins by 25%, as well as an overall improvement in transmission efficiency of 0.4%. Automated data collection is being developed so that more accurate surrogate models can be trained over time.

Kuklev, N. [Fermilab]↗
DOE OSTI2021

Meniscus Guided Coating and Evaporative Crystallization of UiO-66 Metal Organic Framework Thin Films

Thin-film fabrication of metal organic frameworks (MOFs) has been explored for a range of applications, including separations, catalysis, sensing, and charge transport. However, many fabrication techniques have obstacles, including slow crystallization, control over film thickness, and control over crystallinity. Recently, a meniscus-guided coating technique, called solution shearing, has been shown to create MOF thin films within minutes and with control over the film thickness. However, there have been no previous reports of solution shearing based evaporative crystallization of zirconium-based MOFs, which have been widely studied for the aforementioned applications. In this work, for the first time, we show that (i) the zirconium 1,4-dicarboxybenzene MOF, UiO-66, can be formed using evaporative crystallization during solution shearing, and (ii) a wide range of parameters can be tuned to control the film thickness, coverage, and crystallinity. Finally, we bring the solution shearing technique closer to separation applications by growing a full film of UiO-66 crystals up to the resolution of scanning electron microscopy (SEM) on anodic alumina oxide (AAO). This is the first instance of UiO-66 crystals being formed using an evaporative crystallization-based flow coating method, and solution shearing shows the promise to be applicable to form large area zirconium-based MOF crystals in a rapid manner (within seconds to minutes).

42 ENGINEERING↗
DOE OSTI2025

Reward based optimization of resonance-enhanced piezoresponse spectroscopy

Dynamic spectroscopies in scanning probe microscopy (SPM) are critical for probing material properties, such as force interactions, mechanical properties, polarization switching, electrochemical reactions, and ionic dynamics. However, the practical implementation of these measurements is constrained by the need to balance imaging time and data quality. Signal to noise requirements favor long acquisition times and high frequencies to improve signal fidelity. However, these are limited on the low end by contact resonant frequency and photodiode sensitivity and on the high end by the time needed to acquire high-resolution spectra or the propensity for sample degradation under high field excitation over long times. The interdependence of key parameters such as instrument settings, acquisition times, and sampling rates makes manual tuning labor-intensive and highly dependent on user expertise, often yielding operator-dependent results. These limitations are prominent in techniques like dual amplitude resonance tracking in piezoresponse force microscopy that utilize multiple concurrent feedback loops for topography and resonance frequency tracking. Here, a reward-driven workflow is proposed that automates the tuning process, adapting experimental conditions in real time to optimize data quality. Furthermore, this approach significantly reduces the complexity and time required for manual adjustments and can be extended to other SPM spectroscopic methods, enhancing overall efficiency and reproducibility.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Self‐Assembled Monolayer Templating for Engineered Nanopinholes in Passivated Contact Solar Cells

We present a novel self-assembled monolayer (SAM)-based technique to make nanopinhole-enabled passivated contacts on silicon solar cells by tuning the SAM coverage area and etch selectivity. We deposit trimethyl-silyl Si(CH 3 ) 3 groups using hexamethyldisilazane (HMDS) as the precursor over passivating dielectric layers and their stacks (SiO 2 , SiN x , SiO 2 /SiN x ) and interrupt the HMDS attachment chemistry shortly before a full monolayer is formed on its surface. Subsequent etching in dilute HF produces pinholes through the dielectric layers due to the higher etch resistance of the SAM to HF etching. The pinhole areal density (10 4 –10 8 /cm 2 ) and size (10–1000 nm) can be tuned both by duration of HMDS attachment and HF etch time. Pinholes were characterized by atomic force microscopy, tetramethylammonium hydroxide (TMAH) selective etch, and Ag decoration by electroless plating. Polysilicon (poly-Si) passivated contacts enabled by pinholes were formed by subsequent deposition of doped amorphous silicon (a-Si:H) followed by thermal crystallization and dopant drive-in. At optimal areal pinhole density ≈10 7 /cm 2 , contacts exhibit both passivation and carrier transport via pinholes as evidenced by electron beam induced current, transmission line measurements, and carrier lifetime measurements. Solar cells based with these pinhole contacts show V oc = 723 mV and FF = 80.3%. The remaining SAM layer does not affect device performance.

14 SOLAR ENERGY↗
DOE OSTI2026

Profile Generation for GPU Targets

GPU accelerators are ubiquitous, but their ecosystem is far less evolved than the host one. Compiler heuristics are often tuned for CPUs and reused for GPU. Similarly, tooling and more evolved optimization techniques are historically not available on GPU targets. In this work, we address one of these shortcomings and enable profile generation and profile-guided optimizations (PGO) for GPU targets. While this is only a single step towards a CPU equivalent ecosystem for offload devices, it shows how old misconceptions on the limitations of GPUs are often not warranted anymore. Through our implementation in LLVM/Offload, we enable device-side PGO for full scientific applications and open up tooling opportunities, including code coverage analysis and compiler-built-in roofline analysis. Our evaluation highlights the performance implications of profile generation, the insights gained from these profiles, and the (missed) opportunities in utilizing the information for GPU compilation.

McDonough, Ethan Luis [Lawrence Livermore National↗
DOE OSTI2022

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

Building performance simulation has been adopted to support decision making in the building life cycle. An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations. Building energy model calibration is a technique that takes various types of measured performance data (e.g., energy use) and tunes key model parameters to match the simulated results with the actual measurements. This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings. A public building dataset that includes high-level building attributes (e.g., building type, vintage, total floor area, number of stories, zip code) of 111 buildings in San Francisco, California, USA, was used to generate building models in EnergyPlus. Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings' monthly electricity and natural gas consumption. The results showed 57 out of 111 buildings were successfully calibrated against actual buildings, while the remaining buildings showed opportunities for future calibration improvements. Enhancements to the pattern-based model calibration method are identified to expand its use for: (1) central heating, ventilation and air conditioning (HVAC) systems with chillers, (2) space heating and hot water heating with electricity sources, (3) mixed-use building types, and (4) partially occupied buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗