Microstructural classification of unirradiated LiAlO2 pellets by deep learning methods
Not Available
Pazdernik, Karl (ORCID:0000000314104006)↗
SEARCH · Engineering Papers
Results for “Deep”
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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Feedbacks among O2 and CO2 in deep soil gas, oxidation of ferrous minerals, and fractures: A hypothesis for steady-state regolith thickness
Not provided.
Geochemistry & Geophysics↗
Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride
Abstract It is well-known that the atomic-scale and nano-scale configuration of dopants can play a crucial role in determining the electronic properties of materials. However, predicting such effects is challenging due to the large range of atomic configurations that are possible. Here, we present a case study of how deep learning algorithms can enable bandgap prediction in hybridized boron–nitrogen graphene with arbitrary supercell configurations. A material descriptor that enables correlation of structure and bandgap was developed for convolutional neural networks. Bandgaps calculated by ab initio calculations, and corresponding structures, were used as training datasets. The trained networks were then used to predict bandgaps of systems with various configurations. For 4 × 4 and 5 × 5 supercells they accurately predict bandgaps, with a R 2 of >90% and root-mean-square error of ~0.1 eV. The transfer learning was performed by leveraging data generated from small supercells to improve the prediction accuracy for 6 × 6 supercells. This work will pave a route to future investigation of configurationally hybridized graphene and other 2D materials. Moreover, given the ubiquitous existence of configurations in materials, this work may stimulate interest in applying deep learning algorithms for the configurational design of materials across different length scales.
Dong, Yuan↗
Microbial lipid biomarkers detected in deep subsurface black shales
Evidence for microbes has been detected in extreme subsurface environments as deep as 2.5 km with temperatures as high as 90 °C, demonstrating that microbes can adapt and survive extreme environmental conditions.
Akondi, Rawlings N.↗
Electrical current leakage and open-core threading dislocations in AlGaN-based deep ultraviolet light-emitting diodes
Electrical current transport through leakage paths in AlGaN-based deep ultraviolet (DUV) light-emitting diodes (LEDs) and their effect on LED performance are investigated. Open-core threading dislocations, or nanopipes, are found to conduct current through nominally insulating Al{sub 0.7}Ga{sub 0.3}N layers and limit the performance of DUV-LEDs. A defect-sensitive phosphoric acid etch reveals these open-core threading dislocations in the form of large, micron-scale hexagonal etch pits visible with optical microscopy, while closed-core screw-, edge-, and mixed-type threading dislocations are represented by smaller and more numerous nanometer-scale pits visible by atomic-force microscopy. The electrical and optical performances of DUV-LEDs fabricated on similar Si-doped Al{sub 0.7}Ga{sub 0.3}N templates are found to have a strong correlation to the density of these nanopipes, despite their small fraction (<0.1% in this study) of the total density of threading dislocations.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
KOH based selective wet chemical etching of AlN, Al{sub x}Ga{sub 1−x}N, and GaN crystals: A way towards substrate removal in deep ultraviolet-light emitting diode
A controllable and smooth potassium hydroxide-based wet etching technique was developed for the AlGaN system. High selectivity between AlN and Al{sub x}Ga{sub 1−x}N (up to 12×) was found to be critical in achieving effective substrate thinning or removal for AlGaN-based deep ultraviolet light emitting diodes, thus increasing light extraction efficiency. The mechanism of high selectivity of AlGaN as a function of Al composition can be explained as related to the formation and dissolution of oxide/hydroxide on top of N-polar surface. Cross-sectional transmission electron microscopic analysis served as ultimate proof that these hillocks were not related to underlying threading dislocations.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
The Sloan Digital Sky Survey COADD: 275 deg{sup 2} of deep Sloan Digital Sky Survey imaging on stripe 82
We present details of the construction and characterization of the coaddition of the Sloan Digital Sky Survey (SDSS) Stripe 82 ugriz imaging data. This survey consists of 275 deg{sup 2} of repeated scanning by the SDSS camera over –50° ≤ α ≤ 60° and –1.°25 ≤ δ ≤ +1.°25 centered on the Celestial Equator. Each piece of sky has ∼20 runs contributing and thus reaches ∼2 mag fainter than the SDSS single pass data, i.e., to r ∼ 23.5 for galaxies. We discuss the image processing of the coaddition, the modeling of the point-spread function (PSF), the calibration, and the production of standard SDSS catalogs. The data have an r-band median seeing of 1.''1 and are calibrated to ≤1%. Star color-color, number counts, and PSF size versus modeled size plots show that the modeling of the PSF is good enough for precision five-band photometry. Structure in the PSF model versus magnitude plot indicates minor PSF modeling errors, leading to misclassification of stars as galaxies, as verified using VVDS spectroscopy. There are a variety of uses for this wide-angle deep imaging data, including galactic structure, photometric redshift computation, cluster finding and cross wavelength measurements, weak lensing cluster mass calibrations, and cosmic shear measurements.
79 ASTRONOMY AND ASTROPHYSICS↗
DeepMerge – II. Building robust deep learning algorithms for merging galaxy identification across domains
In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here we employ domain adaptation techniques— Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)— and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies. Additionally, we explore the use of Fisher loss and entropy minimization to enforce better in-domain class discriminability. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated datasets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey. The use of domain adaptation techniques in our experiments leads to an increase of target domain classification accuracy of up to ~20%. With further development, these techniques will allow astronomers to successfully implement neural network models trained on simulation data to efficiently detect and study astrophysical objects in current and future large-scale astronomical surveys.
galaxies: interactions↗
Single-spin asymmetries in semi-inclusive deep inelastic scattering and Drell-Yan processes
Not Available
Brodsky, Stanley J.↗
Deep-Learning Electron Diffractive Imaging
Here, we report the development of deep-learning coherent electron diffractive imaging at subangstrom resolution using convolutional neural networks (CNNs) trained with only simulated data. We experimentally demonstrate this method by applying the trained CNNs to recover the phase images from electron diffraction patterns of twisted hexagonal boron nitride, monolayer graphene, and a gold nanoparticle with comparable quality to those reconstructed by a conventional ptychographic algorithm. Fourier ring correlation between the CNN and ptychographic images indicates the achievement of a resolution in the range of 0.70 and 0.55 Å. We further develop CNNs to recover the probe function from the experimental data. The ability to replace iterative algorithms with CNNs and perform real-time atomic imaging from coherent diffraction patterns is expected to find applications in the physical and biological sciences.
47 OTHER INSTRUMENTATION↗
DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks
Hyperparameters employed by deep learning (DL) methods play a substantial role in the performance and reliability of these methods in practice. Unfortunately, finding performance-optimizing hyperparameter settings is a notoriously difficult task. Hyperparameter search methods typically have limited production-strength implementations or do not target scalability within a highly parallel machine, portability across different machines, experimental comparison between different methods, and tighter integration with workflow systems. In this paper, we present DeepHyper, a Python package that provides a common interface for the implementation and study of scalable hyperparameter search methods. It adopts the Balsam workflow system to hide the complexities of running large numbers of hyperparameter configurations in parallel on high-performance computing (HPC) systems. We implement and study asynchronous model-based search methods that consist of sampling a small number of input hyperparameter configurations and progressively fitting surrogate models over the input-output space until exhausting a user-defined budget of evaluations. We evaluate the efficacy of these methods relative to approaches such as random search, genetic algorithms, Bayesian optimization, and hyperband on DL benchmarks on CPU- and GPU-based HPC systems.
Balaprakash, Prasanna↗
Deep Learning for Vertex Reconstruction of Neutrino-nucleus Interaction Events with Combined Energy and Time Data
We present a deep learning approach for vertex reconstruction of neutrino-nucleus interaction events, a problem in the domain of high energy physics. In this approach, we combine both energy and timing data that are collected in the MIN-ERvA detector to perform classification and regression tasks. We show that the resulting network achieves higher accuracy than previous results while requiring a smaller model size and less training time. In particular, the proposed model outperforms the state-of-the-art by 4.00% on classification accuracy. For the regression task, our model achieves 0.9919 on the coefficient of determination, higher than the previous work (0.96).
Song, Linghao↗
Scaling deep learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing
Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors --- including NVIDIA, Intel, AMD, and IBM --- have architectural road-maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating large DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. This paper provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path or Cray Aries. Our evaluation consists of a cross section of convolutional neural net workloads: CifarNet, AlexNet, GoogLeNet, and ResNet50 topologies using the Cifar10 and ImageNet datasets. The workloads are vendor-optimized for each architecture. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and the KNL can be competitive in performance/watt. We find that NVLink facilitates scaling efficiency on GPUs. However, its importance is heavily dependent on neural network architecture. Furthermore, for weak-scaling --- sometimes encouraged by restricted GPU memory --- NVLink is less important.
Gawande, Nitin A.↗
LeTID-Affected Cells from a Utility-Scale Photovoltaic System Characterized by Deep Level Transient Spectroscopy
Photovoltaic modules from a utility-scale field experienced power loss by light- and elevated temperature-induced degradation (LeTID). Samples of one of the affected monocrystalline silicon cells were cored and extracted from the module packaging and encapsulation. One of the cell fragments was processed using a regeneration cycle of applying short-circuit-rated current in forward bias at 85 degrees C for 2 weeks, while the other fragment was kept in its outdoor-degraded LeTID state. Both samples were scribed to form 2-mm diameter isolated areas using a femtosecond-pulse-width laser micromachining system. Both isolated areas contained front grid line segments which were wire bonded to larger contact pads, and the samples were probed in a cryostat linked to a deep-level transient spectroscopy (DLTS) system. Using DLTS, a majority-carrier, hole-trap defect was detected on each sample with an activation energy of 0.42 eV. The LeTID-degraded sample, however, had a larger signal corresponding to a trap density of 1.1x10^13 cm-3, which was about five times larger than the 2.1x10^12 cm-3 trap density of the regenerated sample.
charge carrier lifetime↗
A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices
Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement. Despite its potential, existing quantum learning algorithms, such as Variational Quantum Circuits (VQCs), face challenges in handling more complex datasets, particularly those that are not linearly separable. What’s more, it encounters the deployability issue, making the learning models suffer a drastic accuracy drop after deploying them to the actual quantum devices. To overcome these limitations, this paper proposes a novel spatial-temporal design, namely “ST-VQC”, to integrate nonlinearity in quantum learning and improve the robustness of the learning model to noise. Specifically, ST-VQC can extract spatial features via a novel block-based encoding quantum sub-circuit coupled with a layer-wise computation quantum sub-circuit to enable temporal-wise deep learning. Additionally, a SWAP-Free physical circuit design is devised to improve robustness. These designs bring a number of hyperparameters. After a systematic analysis of the design space for each design component, an automated optimization framework is proposed to generate the ST-VQC quantum circuit. The proposed ST-VQC has been evaluated on two IBM quantum processors, ibm-cairo with 27 qubits and ibmq-lima with 7 qubits to assess its effectiveness. The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers. Moreover, on a non-linear synthetic dataset, the STVQC outperforms a linear classifier by 27.9%, while the linear classifier using classical computing outperforms the existing VQC by 15.58%.
Li, Jinyang↗
A laboratory nanoseismological study on deep-focus earthquake micromechanics
Global earthquake occurring rate displays an exponential decay down to ~300 km and then peaks around 550 to 600 km before terminating abruptly near 700 km. How fractures initiate, nucleate, and propagate at these depths remains one of the greatest puzzles in earth science, as increasing pressure inhibits fracture propagation. We report nanoseismological analysis on high-resolution acoustic emission (AE) records obtained during ruptures triggered by partial transformation from olivine to spinel in Mg 2 GeO 4 , an analog to the dominant mineral (Mg,Fe) 2 SiO 4 olivine in the upper mantle, using state-of-the-art seismological techniques, in the laboratory. AEs’ focal mechanisms, as well as their distribution in both space and time during deformation, are carefully analyzed. Microstructure analysis shows that AEs are produced by the dynamic propagation of shear bands consisting of nanograined spinel. These nanoshear bands have a near constant thickness (~100 nm) but varying lengths and self-organize during deformation. This precursory seismic process leads to ultimate macroscopic failure of the samples. Several source parameters of AE events were extracted from the recorded waveforms, allowing close tracking of event initiation, clustering, and propagation throughout the deformation/transformation process. AEs follow the Gutenberg-Richter statistics with a well-defined b value of 1.5 over three orders of moment magnitudes, suggesting that laboratory failure processes are self-affine. The seismic relation between magnitude and rupture area correctly predicts AE magnitude at millimeter scales. A rupture propagation model based on strain localization theory is proposed. Future numerical analyses may help resolve scaling issues between laboratory AE events and deep-focus earthquakes.
58 GEOSCIENCES↗
Dependence of Deep Convective Cell Properties on Meteorological and Aerosol Conditions during TRACER
Deep convective cells significantly influence Earth’s energy balance and water cycle. However, their accurate representation in numerical models remains challenging due to their small spatiotemporal scales and limited observational constraints. This study examines over ∼400 deep convective cells near Houston, observed by a dual-polarization C-band radar during the Tracking Aerosol Convection Interactions Experiment (TRACER) intensive observation period (June–September 2022). Cells are categorized by lifetime into short-lived (<40 min), intermediate-lived (40–80 min), and long-lived (80+ min) groups. Long-lived cells were broader (∼13.2 km at 2–4-km height) and deeper (∼11.4 km) than short-lived cells (∼6.4-km width, ∼7.31-km height). Using random forest (RF) modeling and correlation analyses, precipitable water vapor (PWV), 2–6-km lapse rate, 0–8-km bulk shear, and fine aerosol mass concentration (Mass_f) are identified as key predictors of cell lifetime. Higher PWV is associated with significantly longer convective cell lifetimes compared to the low-PWV group, particularly within low 2–6-km temperature lapse rate (LR_26km), moderate-to-higher 0–8-km bulk shear (BS_08km), and low-to-moderate Mass_f environments. RF analysis also identifies low-level (0–2 km) equivalent potential temperature, PWV, Mass_f, and surface latent heat flux as key predictors for cell width and height. Short-lived cells have higher aerosol number concentrations (500–1000-nm size range), linked to onshore wind conditions and marine aerosols; however, their low concentration suggests the sensitivity may reflect associated meteorological regimes rather than a direct aerosol effect. Long-lived cells have higher concentrations of organic and sulfate aerosols, while short-lived cells exhibit higher black carbon concentrations. These results highlight the intricate dependence of convective cell lifetimes and structure on environmental moisture, thermodynamics, wind shear, and aerosol characteristics.
54 ENVIRONMENTAL SCIENCES↗
High power picosecond green and deep ultraviolet generations with an all-fiberized MOPA
We demonstrate high power picosecond green and deep ultraviolet (DUV) lasers based on an all-fiberized master oscillator power amplifier (MOPA). The main power amplifier is fabricated with a highly Yb-doped large mode area (LMA) silicate glass fiber. It delivers 75.2-W laser output at 1029 nm with a pulse repetition rate of 10 MHz and a pulse duration of 70 ps. With a lithium triborate (LBO) crystal, a 43.0-W green output at 514.5 nm has been achieved with a pulse duration of 55 ps. With a caesium lithium borate (CLBO) crystal, a 14.5-W picosecond DUV output at 257 nm has been generated, which is the highest power for the all-fiber based DUV laser, to the best of our knowledge.
Pan, Lei (ORCID:0000000288896117)↗