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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids

Ionic liquids (ILs) have unique solvent properties and have thus garnered significant interest. However, exhaustive experimental determination of the physicochemical properties of ILs is unrealistic due to the large structural diversity of anions and cations, their high cost, the requirements of elevated temperature and pressure, and the time required. To circumvent these experimental costs, computational approaches to accurately calculate these properties have emerged. Here in the present study, we present a demonstration of two machine learning (ML) models for the prediction of two critical IL physical properties, the surface tension and the speed of sound, across a wide range of temperatures and pressures. The models make use of molecular descriptors derived from the COSMO-RS, a quantum chemical-based model. The ML models show excellent agreement with experimental observations, with an R2 value of 0.96–0.99 and RMSE of 1.71 mN/m and 16.12 m/s for the surface tension and speed of sound, respectively. This work paves the way for the development of COSMO-RS-informed ML models for the prediction of IL properties which can help to further optimize and accelerate technology development for ILs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Pharmacoepidemiology, Machine Learning and COVID-19: An intent-to-treat analysis of hydroxychloroquine, with or without azithromycin, and COVID-19 outcomes amongst hospitalized US Veterans

Hydroxychloroquine (HCQ) was proposed as an early therapy for coronavirus disease 2019 (COVID-19) after in vitro studies indicated possible benefit. Previous in vivo observational studies have presented conflicting results, though recent randomized clinical trials have reported no benefit from HCQ amongst hospitalized COVID-19 patients. In this work, we examined the effects of HCQ alone, and in combination with azithromycin, in a hospitalized COVID-19 positive, United States (US) Veteran population using a propensity score adjusted survival analysis with imputation of missing data. From March 1, 2020 through April 30, 2020, 64,055 US Veterans were tested for COVID-19 based on Veteran Affairs Healthcare Administration electronic health record data. Of the 7,193 positive cases, 2,809 were hospitalized, and 657 individuals were prescribed HCQ within the first 48-hours of hospitalization for the treatment of COVID-19. There was no apparent benefit associated with HCQ receipt, alone or in combination with azithromycin, and an increased risk of intubation when used in combination with azithromycin [Hazard Ratio (95% Confidence Interval): 1.55 (1.07, 2.24)]. In conclusion, we assessed the effectiveness of HCQ with or without azithromycin in treating patients hospitalized with COVID-19 using a national sample of the US Veteran population. Using rigorous study design and analytic methods to reduce confounding and bias, we found no evidence of a survival benefit from the administration of HCQ.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Classification of AB O 3 perovskite solids: a machine learning study

Here we explored the use of machine learning methods for classifying whether a particular AB O 3 chemistry forms a perovskite or non-perovskite structured solid. Starting with three sets of feature pairs (the tolerance and octahedral factors, the A and B ionic radii relative to the radius of O, and the bond valence distances between the A and B ions from the O atoms), we used machine learning to create a hyper-dimensional partial dependency structure plot using all three feature pairs or any two of them. Doing so increased the accuracy of our predictions by 2–3 percentage points over using any one pair. We also included the Mendeleev numbers of the A and B atoms to this set of feature pairs. Moreover, doing this and using the capabilities of our machine learning algorithm, the gradient tree boosting classifier, enabled us to generate a new type of structure plot that has the simplicity of one based on using just the Mendeleev numbers, but with the added advantages of having a higher accuracy and providing a measure of likelihood of the predicted structure.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits

The usefulness of genomic prediction in crop and livestock breeding programs has prompted efforts to develop new and improved genomic prediction algorithms, such as artificial neural networks and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and six non-linear algorithms. First, we found that hyperparameter selection was necessary for all non-linear algorithms and that feature selection prior to model training was critical for artificial neural networks when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple algorithms (i.e., ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits. Although artificial neural networks did not perform best for any trait, we identified strategies (i.e., feature selection, seeded starting weights) that boosted their performance to near the level of other algorithms. Our results highlight the importance of algorithm selection for the prediction of trait values.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Vehicle Position Detection Based on Machine Learning Algorithms in Dynamic Wireless Charging

Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized into long-track transmitters and segmented coil arrays. The segmented coil array, favored for its heightened efficiency and reduced electromagnetic interference, stands out as the preferred option. However, in such DWC systems, the need arises to detect the vehicle’s position, specifically to activate the transmitter coils aligned with the receiver pad and de-energize uncoupled transmitter coils. This paper introduces various machine learning algorithms for precise vehicle position determination, accommodating diverse ground clearances of electric vehicles and various speeds. Through testing eight different machine learning algorithms and comparing the results, the random forest algorithm emerged as superior, displaying the lowest error in predicting the actual position.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

CoSHA: Code for Stellar Properties Heuristic Assignment—for the MaStar Stellar Library

We introduce CoSHA: a Code for Stellar properties Heuristic Assignment. In order to estimate the stellar properties, CoSHA implements a Gradient Tree Boosting algorithm to label each star across the parameter space (T eff , $\mathrm{log}g$, [Fe/H], and [α/Fe]). We use CoSHA to estimate the stellar atmospheric parameters of 22,000 unique stars in the MaNGA Stellar Library (MaStar). To quantify the reliability of our approach, we run internal tests, using both the Göttingen Stellar Library (a theoretical library) and the first data release of MaStar, and external tests, by comparing the resulting distributions in the parameter space with the APOGEE estimates of the same properties. In summary, our parameter estimates span the ranges T eff = [2900, 12,000] K, $\mathrm{log}g=[-0.5,5.6]$, [Fe/H] = [-3.74, 0.81], and αM = [-0.22, 1.17]. We report internal (external) uncertainties of the properties of ${\sigma }_{{T}_{\mathrm{eff}}}\sim 43(240)$ K, ${\sigma }_{\mathrm{log}g}\sim 0.2(0.4)$, σ [Fe/H] ~ 0.16(0.24), and σ [α/Fe] ~ 0.09(0.08). These uncertainties are comparable to those of other methods with similar objectives. Despite the fact that CoSHA is not aware of the spatial distributions of these physical properties in the Milky Way, we are able to recover the main trends known in the literature. The catalog of physical properties for MaStar can be accessed online.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Benchmarking parametric and machine learning models for genomic prediction of complex traits

The usefulness of genomic prediction in crop and livestock breeding programs has prompted efforts to develop new and improved genomic prediction algorithms, such as artificial neural networks and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and six non-linear algorithms. First, we found that hyperparameter selection was necessary for all non-linear algorithms and that feature selection prior to model training was critical for artificial neural networks when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple algorithms (i.e. ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits. Although artificial neural networks did not perform best for any trait, we identified strategies (i.e. feature selection, seeded starting weights) that boosted their performance to near the level of other algorithms. Our results highlight the importance of algorithm selection for the prediction of trait values.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

ANN↗
NASA NTRS2020

Building a landslide hazard indicator with machine learning and land surface models

The U.S. Pacific Northwest has a history of frequent and occasionally deadly landslides caused by various factors. Using a multivariate, machine-learning approach, we combined a Pacific Northwest Landslide Inventory with a 36-year gridded hydrologic dataset from the National Climate Assessment – Land Data Assimilation System to produce a landslide hazard indicator (LHI) on a daily 0.125-degree grid. The LHI identified where and when landslides were most probable over the years 1979–2016, addressing issues of bias and completeness that muddy the analysis of multi-decadal landslide inventories. The seasonal cycle was strong along the west coast, with a peak in the winter, but weaker east of the Cascade Range. This lagging indicator can fill gaps in the observational record to identify the seasonality of landslides over a large spatiotemporal domain and show how landslide hazard has responded to a changing climate.

XGBoost↗
DOE OSTI2024

Evaporated CdSe for Efficient Polycrystalline CdSeTe Thin-Film Solar Cells

Recent progress has shown that alloying cadmium telluride (CdTe) with cadmium selenide (CdSe) to create a CdSexTe1-x (CdSeTe) gradient region can significantly boost the performance of polycrystalline CdSeTe thin-film solar cells. However, improper CdSeTe alloying might introduce problematic band alignment and deleterious voids at the front interface, limiting the benefit maximization of this technique. Here, we show that the CdSe layers deposited by thermal evaporation result in CdSeTe cells with a higher performance than the sputtered CdSe. This is because evaporated CdSe can avoid the formation of voids at the front interface, producing improved front junction quality with suppressed front junction nonradiative recombination. The champion cell using evaporated CdSe demonstrated a power conversion efficiency (PCE) of 19.7%, much higher than 18.1% in the cell using sputtered CdSe.

cadmium selenide↗
DOE OSTI2025

Accelerating iterative ptychography with an integrated neural network

Electron ptychography is a powerful and versatile tool for high-resolution and dose-efficient imaging. Iterative reconstruction algorithms are powerful but also computationally expensive due to their relative complexity and the many hyperparameters that must be optimised. Gradient descent-based iterative ptychography is a popular method, but it may converge slowly when reconstructing low spatial frequencies. Here, in this work, we present a method for accelerating a gradient descent-based iterative reconstruction algorithm by training a neural network (NN) that is applied in the reconstruction loop. The NN works in Fourier space and selectively boosts low spatial frequencies, thus enabling faster convergence in a manner similar to accelerated gradient descent algorithms. We discuss the difficulties that arise when incorporating a NN into an iterative reconstruction algorithm and show how they can be overcome with iterative training. We apply our method to simulated and experimental data of gold nanoparticles on amorphous carbon and show that we can significantly speed up ptychographic reconstruction of the nanoparticles.

4DSTEM↗
DOE OSTI2024

High-gradient performance of a prototype accelerator cavity for a 3 GeV proton radiography booster

This paper reports the design, fabrication, and results of the high-gradient conditioning and testing for a two-cell, π -mode, standing wave normal-conducting prototype booster cavity for the proposed 3 GeV proton linac upgrade at Los Alamos Neutron Science Center. Increasing the energy of proton beam from the existing 800 MeV to 3 GeV will improve resolution of the proton radiography by up to 10 times. The proposed energy boost can be achieved with a compact normal-conducting high-gradient radio-frequency (rf) linac section. The C-band section of the booster linac was designed with optimized-shaped copper accelerator cavities with distributed rf coupling. A short two-cell test prototype structure was designed for the frequency of 5.712 GHz, fabricated, and tested at the C-band Engineering Research Test Facility in New Mexico (CERF-NM) at Los Alamos National Laboratory. The maximum klystron power coupled into the test structure was 8.3 MW with 1 μ s pulse length and 100 Hz repetition rate. The breakdown probabilities were recorded as functions of the accelerating gradient and peak surface fields. Operation of the test cavity at accelerating gradients of up to 100 MV / m was demonstrated. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
NASA NTRS2011

Saturn Neutron Exosphere as Source for Inner and Innermost Radiation Belts

Energetic proton and electron measurements by the ongoing Cassini orbiter mission are expanding our knowledge of the highest energy components of the Saturn magnetosphere in the inner radiation belt region after the initial discoveries of these belts by the Pioneer 11 and Voyager 2 missions. Saturn has a neutron exosphere that extends throughout the magnetosphere from the cosmic ray albedo neutron source at the planetary main rings and atmosphere. The neutrons emitted from these sources at energies respectively above 4 and 8 eV escape the Saturn system, while those at lower energies are gravitationally bound. The neutrons undergo beta decay in average times of about 1000 seconds to provide distributed sources of protons and electrons throughout Saturn's magnetosphere with highest injection rates close to the Saturn and ring sources. The competing radiation belt source for energetic electrons is rapid inward diffusion and acceleration of electrons from the middle magnetosphere and beyond. Minimal losses during diffusive transport across the moon orbits, e.g. of Mimas and Enceladus, and local time asymmetries in electron intensity, suggest that drift resonance effects preferentially boost the diffusion rates of electrons from both sources. Energy dependences of longitudinal gradient-curvature drift speeds relative to the icy moons are likely responsible for hemispheric differences (e.g., Mimas, Tethys) in composition and thermal properties as at least partly produced by radiolytic processes. A continuing mystery is the similar radial profiles of lower energy (<10 MeV) protons in the inner belt region. Either the source of these lower energy protons is also neutron decay, but perhaps alternatively from atmospheric albedo, or else all protons from diverse distributed sources are similarly affected by losses at the moon' orbits, e.g. because the proton diffusion rates are extremely low. Enceladus cryovolcanism, and radiolytic processing elsewhere on the icy moon and ring surfaces, are additional sources of protons via ionization and charge exchange from breakup of water molecules. But one must then account somehow for local acceleration to the observed keV-MeV energies, since moon sweeping and E-ring absorption would remove protons diffusing inward from the middle magnetosphere. Although the main rings block further inward diffusion from the inner radiation belts, the exospheric neutron-decay source, combined with much slower diffusion of protons relative to electrons, may produce an innermost radiation belt in the gap between the upper atmosphere and the D-ring. This innermost belt will first be explored in-situ during the final proximal orbits of the Cassini mission.

Cooper, John↗
DOE OSTI2021

Enabling deeper learning on big data for materials informatics applications

Abstract The application of machine learning (ML) techniques in materials science has attracted significant attention in recent years, due to their impressive ability to efficiently extract data-driven linkages from various input materials representations to their output properties. While the application of traditional ML techniques has become quite ubiquitous, there have been limited applications of more advanced deep learning (DL) techniques, primarily because big materials datasets are relatively rare. Given the demonstrated potential and advantages of DL and the increasing availability of big materials datasets, it is attractive to go for deeper neural networks in a bid to boost model performance, but in reality, it leads to performance degradation due to the vanishing gradient problem. In this paper, we address the question of how to enable deeper learning for cases where big materials data is available. Here, we present a general deep learning framework based on Individual Residual learning (IRNet) composed of very deep neural networks that can work with any vector-based materials representation as input to build accurate property prediction models. We find that the proposed IRNet models can not only successfully alleviate the vanishing gradient problem and enable deeper learning, but also lead to significantly (up to 47%) better model accuracy as compared to plain deep neural networks and traditional ML techniques for a given input materials representation in the presence of big data.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Global flow structure and exact formal transseries of the Gubser flow in kinetic theory

In this work we introduce the generic conditions for the existence of a non-equilibrium attractor that is an invariant manifold determined by the long-wavelength modes of the physical system. We investigate the topological properties of the global flow structure of the Gubser flow for the Israel-Stewart theory and a kinetic model for the Boltzmann equation by employing Morse-Smale theory. We present a complete classification of the invariant submanifolds of the flow and determine all the possible flow lines connecting any pair of UV/IR fixed points. The formal transseries solutions to the Gubser dynamical system around the early-time (UV) and late-time (IR) fixed points are constructed and analyzed. It is proven that these solutions are purely perturbative (or power-law asymptotic) series with a finite radius of convergence. Based on these analyses, we find that Gubser-like expanding kinetic systems do not hydrodynamize owing to the failure of the hydrodynamization process which heavily relies on the classification of (non)hydrodynamic modes in the IR regime. This is in contrast to longitudinal boost-invariant plasmas where the asymptotic dynamics is described by a few terms of the hydrodynamic gradient expansion. We finally compare our results for both Bjorken and Gubser conformal kinetic models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1981

Design and fabrication of brazed Rene 41 honeycomb sandwich structural panels for advanced space transportation systems

The design and fabrication of two large brazed Rene 41 honeycomb panels, the establishment of a test plan, the design and fabrication of a test fixture to subject the panels to cyclic thermal gradients and mechanical loads equivalent to those imposed on an advanced space transportation vehicle during its boost and entry trajectories are discussed. The panels will be supported at four points, creating three spans. The outer spans are 45.7 cm (18 in.) and the center span 76.2 cm (30 in). Specimen width is 30.5 cm (12 in.). The panels were primarily designed by boost conditions simulated by subjecting the panels to liquid nitrogen, 77K (-320 F) on one side and 455K (360 F) on the other side and by mechanically imposing loads representing vehicle fuel pressure loads. Entry conditions were simulated by radiant heating to 1034K (1400 F). The test program subjected the panels to 500 boost thermal conditions. Results are presented.

Hepler, A. K.↗
NASA NTRS1984

Shield Boosts Silicon-Growth Rate

Radiation shield permits faster growth--by 20 percent--of singlecrystal silicon from molten silicon by producting sharper thermal gradients near growth front.

Lane, R. L.↗
DOE OSTI2014

SU-E-T-124: Dosimetric Comparison of HDR Brachytherapy and Intensity Modulated Proton Therapy

Purpose: Brachytherapy is known to be able to deliver more radiation dose to tumor while minimizing radiation dose to surrounding normal tissues. Proton therapy also provides superior dose distribution due to Bragg peak. Since both HDR and Intensity Modulated Proton Therapy (IMPT) are beneficial for their quick dose drop off, our goal in this study is to compare the pace of dose gradient drop-off between HDR and IMPT plans based on the same CT image data-set. In addition, normal tissues sparing were also compared among HDR, IMPT and SBRT. Methods: Five cervical cancer cases treated with EBRT + HDR boost combination with Tandem and Ovoid applicator were used for comparison purpose. Original HDR plans with prescribed dose of 5.5 Gy x 5 fractions were generated and optimized. The 100% isodose line of HDR plans was converted to a dose volume, and treated as CTV for IMPT and SBRT planning. The same HDR CT scans were also used for IMPT plan and SBRT plan for direct comparison. The philosophy of the IMPT and SBRT planning was to create the same CTV coverage as HDR plans. All three modalities treatment plans were compared to each other with a set of predetermined criteria. Results: With similar target volume coverage in cervix cancer boost treatment, HDR provides a slightly sharper dose drop-off from 100% to 50% isodose line, averagely in all directions compared to IMPT. However, IMPT demonstrated more dose gradient drop-off at the junction of the target and normal tissues by providing more normal tissue sparing and superior capability to reduce integral dose. Conclusion: IMPT is capable of providing comparable dose drop-off as HDR. IMPT can be explored as replacement for HDR brachytherapy in various applications.

60 APPLIED LIFE SCIENCES↗