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At least 649 records · Page 36

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DOE OSTI2022

Development of lean, efficient, and fast physics-framed deep-learning-based proxy models for subsurface carbon storage

In this work, we present deep-learning-based surrogate models for CCUS developed with four different algorithms and a physics-framed two-phase flow problem involving displacement of water by CO 2 . The deep-learning models were trained using 3D datasets describing the pressure plume, CO 2 saturation plume, and water extraction rate generated by numerical simulation. The hyperparameters defining the architecture of the neural networks were optimized to determine the slimmest network size and training parameters that give the most efficient performance at the least training cost. To develop a robust model that closely mimics the governing physical laws, the discretized form of the two-phase fluid transport equation was used to formulate the supervised deep-learning task. The algorithms investigated in this study predicted the data to above 95% accuracy, with the multi-layer perceptron model demonstrating the best performance by balancing training speed, prediction time, and prediction accuracy with lean network capacity. Furthermore, the surrogate models simultaneously predict reservoir pressure and CO 2 saturation in every grid block, including the surface well extraction rate and bottomhole pressure, at all simulation times for a given static model realization in just a few seconds on a standard desktop computer. A key outcome of this study is that limits can be placed on network design parameters to avoid over designing neural networks, with associated efficiencies in training and prediction times. This is very useful because large volumes of data may be generated in CCUS projects and over-design of neural network architectures imposes penalties that are antithetical to the goal of near-real time forecasting.

58 GEOSCIENCES↗

DOE OSTI2024

Unveiling mesoscopic structures in distorted lamellar phases through deep learning-based small angle neutron scattering analysis

Hypothesis: The formation of distorted lamellar phases, distinguished by their arrangement of crumpled, stacked layers, is frequently accompanied by the disruption of long-range order, leading to the formation of interconnected network structures commonly observed in the sponge phase. Nevertheless, traditional scattering functions grounded in deterministic modeling fall short of fully representing these intricate structural characteristics. Our hypothesis posits that a deep learning method, in conjunction with the generalized leveled wave approach used for describing structural features of distorted lamellar phases, can quantitatively unveil the inherent spatial correlations within these phases. Experiments and Simulations: This report outlines a novel strategy that integrates convolutional neural networks and variational autoencoders, supported by stochastically generated density fluctuations, into a regression analysis framework for extracting structural features of distorted lamellar phases from small angle neutron scattering data. To evaluate the efficacy of our proposed approach, we conducted computational accuracy assessments and applied it to the analysis of experimentally measured small angle neutron scattering spectra of AOT surfactant solutions, a frequently studied lamellar system. Findings: The findings unambiguously demonstrate that deep learning provides a dependable and quantitative approach for investigating the morphology of wide variations of distorted lamellar phases. It is adaptable for deciphering structures from the lamellar to sponge phase including intermediate structures exhibiting fused topological features. In conclusion, this research highlights the effectiveness of deep learning methods in tackling complex issues in the field of soft matter structural analysis and beyond.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Coupled deep-mantle carbon-water cycle: Evidence from lower-mantle diamonds

Diamonds form in a variety of environments between subducted crust, lithospheric and deep mantle. Recently, deep source diamonds with inclusions of the high-pressure H 2 O-phase ice-VII were discovered. By correlating the pressures of ice-VII inclusions with those of other high-pressure inclusions, we assess quantitatively the pressures and temperatures of their entrapment. We show that the ice-VII-bearing diamonds formed at depths down to 800 ± 60 km but at temperatures 200–500 K below average mantle temperature that match the pressure-temperature conditions of decomposing dense hydrous mantle silicates. Furthermore, our work presents strong evidence for coupled recycling of water and carbon in the deep mantle based on natural samples.

58 GEOSCIENCES↗

DOE OSTI2025

Reproducibility of Radiokrypton in Deep Desert Aquifers: Insights from a Decade of Research

Great technical advances have been achieved since the first atom-trap trace analysis (ATTA) -based radiokrypton application in Egypt, where 1 Myr old groundwater was discovered. Beyond advances in ATTA measurement capabilities, including reduction in sample size, analysis duration, and analytical uncertainty, major progress has been achieved over the past two decades in the sample collection and preparation techniques. These advances paved the expansion of ATTA-based noble gas applications to many other aquifers worldwide, illuminating the nature and flow pattern of deep groundwater systems. While the potential of this new analytical technique for old groundwater dating is well recognized, another important aspect yet to be examined is the reproducibility of radiokrypton in aquifers over time, i.e., how representative is a discrete groundwater sample, collected at a specific time and location, for the natural groundwater system? The likelihood of a negative answer is increased by flow-field disturbance in aquifers following massive groundwater abstraction. Here, in this work, we present repeated 81 Kr sampling and measurements in twenty-one sites over Israel, mostly of deep (up to 1 km) wells tapping confined aquifers in the arid to hyperarid Negev desert. The results demonstrate that radiokrypton measurements are indeed reproducible, even in cases where samples were collected as long as nine years apart and from highly productive (∼1 Mm 3 /yr order) pumping wells. Furthermore, many of the repeated measurements in this study (17 out of the 21 sites) were conducted with different ATTA Instruments in two different laboratories using slightly different sampling, preparation, and analysis techniques, yet with an overall good agreement. The consistency in the ATTA-based 81 Kr-dating results over time highlights the robustness of this state-of-the-art technique as a tool to unravel groundwater flow patterns and encourages further applications to many other yet-to-be-explored deep aquifers.

atom-trap trace analysis↗

DOE OSTI2022

Stability of a Mixed–Valence Hydrous Iron–Rich Oxide: Implications for Water Storage and Dynamics in the Deep Lower Mantle

Incorporation of water into mantle compositions can have significant effects on the phase relations in the systems. Here, we synthesized an iron-rich hexagonal hydrous phase (referred to as "HH1-phase") under the high pressure-temperature (P-T) conditions of the deep lower mantle and determined the crystal structure of the HH1-phase at 79 GPa using the multigrain crystallography method. The chemical formula obtained was Fe 12.76 O 18 H x (x ~ to 4.5) in the Fe-O-H system. To demonstrate the role of HH1-phase for water storage in multicomponent systems relevant to mantle compositions, we investigated the stability of HH1-phase in both MgO-rich pyrolitic and SiO 2 -rich basaltic compositions. Our results indicate that the HH1-phase serves as major water storage in a pyrolitic composition, whereas the Al-rich CaCl 2 -type δ-phase and SiO 2 phase are major water storage phases in a SiO 2 -rich basaltic composition. Incorporation of considerable amounts of SiO 2 , MgO, and Al 2 O 3 into the HH1-phase expands its stability field from 98 GPa in the Fe-Al-O-H system to at least 108 GPa (corresponding to similar to 2,400 km depth) in the Mg-Si-Al-Fe-O-H system. Plumes of hot upwelling rock rooted at the base of the lower mantle have been proposed as a possible origin of hotspot volcanoes. The hydrous Fe-rich HH1-phase, if included into the material of upwelling plumes, will decompose on its rising to the upper part of the lower mantle and release water. Our results should provide constraints on water storage in the deep lower mantle and have implications for deep mantle dynamics.

58 GEOSCIENCES↗

DOE OSTI2026

Aragonite saturation horizon variability along North Pacific seamounts and implications for deep-sea coral reefs

The 2014 discovery of living deep-sea coral reefs along the Northwest Hawaiian Islands (NWHI) and lower Emperor Seamount Chain (ESC), despite the North Pacific’s shallow aragonite saturation horizon (ASH) and high CaCO3 dissolution rates, underscores the need to understand the local seawater chemistry that allows these reefs to persist. We investigated seawater carbonate chemistry using discrete samples along the NWHI and ESC from two cruises ~1 year apart (08/26/21 – 09/26/21, 09/09/22 – 10/24/22). Across the two cruises, ASH depth difference ranged from 15 to 77 m. Since the Pacific ASH shoals by 1–2 m yr?¹, this long-term trend cannot explain the magnitude of ASH change observed between cruises. Temperature-salinity plots similarly indicate no major shifts in intermediate water masses that could account for these changes. Instead, ASH depth variability was primarily governed by localized biogeochemical processes, namely changes in intermediate water respiration and CaCO3 dissolution. Indicators for dissolution (TA*) and respiration (AOU) suggest changes in ASH depth were driven by changes in dissolution at the northern- and southern-most sites, whereas respiration exerted stronger control at central sites. Combining 2021 and 2022 data with data from 2014 – 2019 (excluding 2018) revealed high interannual ASH variability, by as much as >200 m at one site. Deep-sea coral reefs across the NWHI and ESC currently reside close to the ASH depth and likely experience seasonal and interannual shifts between under- and supersaturation. As ocean acidification progresses however, persistent exposure to undersaturated seawater could further threaten these critical deep-sea ecosystems.

coral reefs↗

DOE OSTI2013

Investigation on origin of Z{sub 1/2} center in SiC by deep level transient spectroscopy and electron paramagnetic resonance

The Z{sub 1/2} center in n-type 4H-SiC epilayers-a dominant deep level limiting the carrier lifetime-has been investigated. Using capacitance versus voltage (C-V) measurements and deep level transient spectroscopy (DLTS), we show that the Z{sub 1/2} center is responsible for the carrier compensation in n-type 4H-SiC epilayers irradiated by low-energy (250 keV) electrons. The concentration of the Z{sub 1/2} defect obtained by C-V and DLTS correlates well with that of the carbon vacancy (V{sub C}) determined by electron paramagnetic resonance, suggesting that the Z{sub 1/2} deep level originates from V{sub C}.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2016

Origin and annealing of deep-level defects in GaNAs grown by metalorganic vapor phase epitaxy

Deep-level defects were investigated by deep level transient spectroscopy on the as-grown and annealed GaNAs layers of various nitrogen (N) contents. The unintentionally doped (uid) GaNAs layers were grown by metalorganic vapor phase epitaxy with N = 1.4%, 2.0%, 2.2%, and 2.4% on GaAs substrate. The possible origin and evolution of the deep-level defects upon annealing were analyzed with the use of the GaNAs band gap diagram concept [Kudrawiec et al., Appl. Phys. Lett. 101, 082109 (2012)], which assumes that the activation energy of donor traps decreases with N-related downward shift of the conduction band. On the basis of this diagram and in comparison with previous results, the N-related traps were associated with (N−As){sub As} or (N−N){sub As} split interstitials. It was also proposed that one of the electron traps and the hole trap, lying at the same level position in the bandgap of the annealed uid-GaNAs layers, can both act as one generation-recombination center partially responsible for poor optical properties of this alloy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2016

Donor and double-donor transitions of the carbon vacancy related EH{sub 6∕7} deep level in 4H-SiC

Using medium- and high-resolution multi-spectra fitting of deep level transient spectroscopy (DLTS), minority carrier transient spectroscopy (MCTS), optical O-DLTS and optical-electrical (OE)-MCTS measurements, we show that the EH{sub 6∕7} deep level in 4H-SiC is composed of two strongly overlapping, two electron emission processes with thermal activation energies of 1.49 eV and 1.58 eV for EH{sub 6} and 1.48 eV and 1.66 eV for EH{sub 7}. The electron emission peaks of EH{sub 7} completely overlap while the emission peaks of EH{sub 6} occur offset at slightly different temperatures in the spectra. OE-MCTS measurements of the hole capture cross section σ{sub p0}(T) in p-type samples reveal a trap-Auger process, whereby hole capture into the defect occupied by two electrons leads to a recombination event and the ejection of the second electron into the conduction band. Values of the hole and electron capture cross sections σ{sub n}(T) and σ{sub p}(T) differ strongly due to the donor like nature of the deep levels and while all σ{sub n}(T) have a negative temperature dependence, the σ{sub p}(T) appear to be temperature independent. Average values at the DLTS measurement temperature (∼600 K) are σ{sub n2+}(T) ≈ 1 × 10{sup −14} cm{sup 2}, σ{sub n+}(T) ≈ 1 × 10{sup −14} cm{sup 2}, and σ{sub p0}(T) ≈ 9 × 10{sup −18} cm{sup 2} for EH{sub 6} and σ{sub n2+}(T) ≈ 2 × 10{sup −14} cm{sup 2}, σ{sub n+}(T) ≈ 2 × 10{sup −14} cm{sup 2}, σ{sub p0}(T) ≈ 1 × 10{sup −20} cm{sup 2} for EH{sub 7}. Since EH{sub 7} has already been identified as a donor transition of the carbon vacancy, we propose that the EH{sub 6∕7} center in total represents the overlapping first and second donor transitions of the carbon vacancy defects on both inequivalent lattice sites.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2018

Influence of deep levels on the electrical transport properties of CdZnTeSe detectors

Here, we investigated the influence of deep levels on the electrical transport properties of CdZnTeSe radiation detectors by comparing experimental data with numerical simulations based on the simultaneous solution of drift-diffusion and Posisson equations, including the Shockley-Read-Hall model of the carrier trapping. We determined the Schottky barrier heights and the Fermi level position from I-V measurements. We measured the time evolution of the electric field and the electrical current after the application of a voltage bias. We observed that the electrical properties of CZTS are fundamentally governed by two deep levels close to the mid-bandgap - one recombination and one hole trap. We show that the hole trap indirectly increases the mobility-lifetime product of electrons. We conclude that the structure of deep levels in CZTS are favorable for high electrical charge transport.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Digital twins and deep learning segmentation of defects in monolayer MX 2 phases

Developing methods to understand and control defect formation in nanomaterials offers a promising route for materials discovery. Monolayer MX 2 phases represent a particularly compelling case for defect engineering of nanomaterials due to the large variability in their physical properties as different defects are introduced into their structure. However, effective identification and quantification of defects remain a challenge even as high-throughput scanning transmission electron microscopy methods improve. This study highlights the benefits of employing first principles calculations to produce digital twins for training deep learning segmentation models for defect identification in monolayer MX 2 phases. Around 600 defect structures were obtained using density functional theory calculations, with each monolayer MX 2 structure being subjected to multislice simulations for the purpose of generating the digital twins. Several deep learning segmentation architectures were trained on this dataset, and their performances evaluated under a variety of conditions such as recognizing defects in the presence of unidentified impurities, beam damage, grain boundaries, and with reduced image quality from low electron doses. Further, this digital twin approach allows benchmarking different deep learning architectures on a theory dataset, which enables the study of defect classification under a broad array of finely controlled conditions. It thus opens the door to resolving the underpinning physical reasons for model shortcomings and potentially chart paths forward for automated discovery of materials defect phases in experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Deep learning to represent subgrid processes in climate models

The representation of nonlinear subgrid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric subgrid processes in a climate model by learning from a multiscale model in which convection is treated explicitly. The trained neural network then replaces the traditional subgrid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multiyear simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth system model development could play a key role in reducing climate prediction uncertainty in the coming decade.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Geological activity shapes the microbiome in deep-subsurface aquifers by advection

Subsurface environments host diverse microorganisms in fluid-filled fractures; however, little is known about how geological and hydrological processes shape the subterranean biosphere. Here, we sampled three flowing boreholes weekly for 10 mo in a 1478-m-deep fractured rock aquifer to study the role of fracture activity (defined as seismically or aseismically induced fracture aperture change) and advection on fluid-associated microbial community composition. We found that despite a largely stable deep-subsurface fluid microbiome, drastic community-level shifts occurred after events signifying physical changes in the permeable fracture network. The community-level shifts include the emergence of microbial families from undetected to over 50% relative abundance, as well as the replacement of the community in one borehole by the earlier community from a different borehole. Null-model analysis indicates that the observed spatial and temporal community turnover was primarily driven by stochastic processes (as opposed to deterministic processes). We, therefore, conclude that the observed community-level shifts resulted from the physical transport of distinct microbial communities from other fracture(s) that outpaced environmental selection. Given that geological activity is a major cause of fracture activity and that geological activity is ubiquitous across space and time on Earth, our findings suggest that advection induced by geological activity is a general mechanism shaping the microbial biogeography and diversity in deep-subsurface habitats across the globe.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Inferring safety critical events from vehicle kinematics in naturalistic driving environment: Application of deep learning Algorithms

Advances in sensing technology has enabled the collection of countless terabytes of second-by-second kinematics data. Such data provides opportunities for real-time monitoring of driving behavior and identification of safety critical events (SCEs) including crashes and near crashes. The concept of volatility is relevant in this context, which identifies instability and erratic variations in driving behavior prior to involvement in SCEs. This study utilized vehicle kinematics from a large-scale naturalistic driving data to develop a deep learning approach based on 1D convolutional neural networks (CNN) for inferring SCEs. The data are unique in the sense that such accurate pre-crash data at high fidelity are not available in traditional crash repositories. This study contributes to the literature by providing a first attempt at predicting responses to SCEs by developing deep learning-based CNN architectures using novel driving volatility based kinematic thresholds for a sample of 9553 events. The key contribution lies in developing a volatility-based CNN input layout that is acceptable to CNN schemes and represents the motion kinematics such as speed, acceleration and volatility measures. Several 1D-CNN architectures were developed using layers, numbers of convolutions, layer patterns, and kernels. Shallow and deep architectures were tested, revealing higher accuracy of shallow architectures in detecting SCEs. The optimal number of epochs were identified using an early stopping method while the CNN performance was improved by increasing the number of epochs. The ensemble CNN had the highest predictive accuracy of 95.6% for detection of crashes and near crashes, which was 2.5% higher than the optimal CNN using 20% hold out test data. The ensemble CNN also outperformed classical machine learning models and model performance reported in past studies on detection of SCEs. Finally, these results have implications for identification of safety hotspots and providing real-time alerts and warnings in connected and highly automated vehicle environment including society of automotive engineers levels 3–5.

42 ENGINEERING↗

DOE OSTI2016

Deep level defects in dilute GaAsBi alloys grown under intense UV illumination

Dilute GaAs1-xBix alloys exhibiting narrow band edge photoluminescence (PL) were recently grown by molecular beam epitaxy (MBE) with the growth surface illuminated by intense UV radiation. To investigate whether the improved optical quality of these films results from a reduction in the concentration of deep level defects, p+/n and n+/p junction diodes were fabricated on both the illuminated and dark areas of several samples. Deep Level Transient Spectroscopy (DLTS) measurements show that the illuminated and dark areas of both the n- and p-type GaAs1-xBix epi-layers have similar concentrations of near mid-gap electron and hole traps, in the 1015 cm-3 range. Thus the improved PL spectra cannot be explained by a reduction in non-radiative recombination at deep level defects. We note that carrier freeze-out above 35 K is significantly reduced in the illuminated areas of the p-type GaAs1-xBix layers compared to the dark areas, allowing the first DLTS measurements of defect energy levels close to the valence band edge. These defect levels may account for differences in the PL spectra from the illuminated and dark areas of un-doped layers with a similar Bi fraction.

14 SOLAR ENERGY↗

DOE OSTI2022

DeepAdversaries: examining the robustness of deep learning models for galaxy morphology classification

With increased adoption of supervised deep learning methods for work with cosmological survey data, the assessment of data perturbation effects (that can naturally occur in the data processing and analysis pipelines) and the development of methods that increase model robustness are increasingly important. In the context of morphological classification of galaxies, we study the effects of perturbations in imaging data. In particular, we examine the consequences of using neural networks when training on baseline data and testing on perturbed data. We consider perturbations associated with two primary sources: (a) increased observational noise as represented by higher levels of Poisson noise and (b) data processing noise incurred by steps such as image compression or telescope errors as represented by one-pixel adversarial attacks. We also test the efficacy of domain adaptation techniques in mitigating the perturbation-driven errors. We use classification accuracy, latent space visualizations, and latent space distance to assess model robustness in the face of these perturbations. For deep learning models without domain adaptation, we find that processing pixel-level errors easily flip the classification into an incorrect class and that higher observational noise makes the model trained on low-noise data unable to classify galaxy morphologies. On the other hand, we show that training with domain adaptation improves model robustness and mitigates the effects of these perturbations, improving the classification accuracy up to 23% on data with higher observational noise. Domain adaptation also increases up to a factor of ${\approx}2.3$ the latent space distance between the baseline and the incorrectly classified one-pixel perturbed image, making the model more robust to inadvertent perturbations. Successful development and implementation of methods that increase model robustness in astronomical survey pipelines will help pave the way for many more uses of deep learning for astronomy.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Inferring effective electrostatic interaction of charge-stabilized colloids from scattering using deep learning

In this article, an innovative strategy is presented that incorporates deep auto-encoder networks into a least-squares fitting framework to address the potential inversion problem in small-angle scattering. To evaluate the performance of the proposed approach, a detailed case study focusing on charged colloidal suspensions was carried out. The results clearly indicate that a deep learning solution offers a reliable and quantitative method for studying molecular interactions. The approach surpasses existing deterministic approaches with respect to both numerical accuracy and computational efficiency. Overall, this work demonstrates the potential of deep learning techniques in tackling complex problems in soft-matter structures and beyond.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging

Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse that volume. Applications include seismic exploration of the Earth’s subsurface, acoustic imaging and nondestructive testing (NDT) in material science, and ultrasound computed tomography (USCT) in medicine. Current approaches for solving CWI problems can be divided into two categories: those rooted in traditional physics and those based on deep learning. Physics-based methods stand out for their ability to provide high-resolution and quantitatively accurate estimates of acoustic properties within the medium. However, they can be computationally intensive and are susceptible to ill-posedness and nonconvexity typical of CWI problems. Machine learning (ML)-based computational methods have recently emerged, offering a different perspective to address these challenges. Diverse scientific communities have independently pursued the integration of deep learning in CWI. This review discusses how contemporary scientific ML techniques, and deep neural networks in particular, have been developed to enhance and integrate with traditional physics-based methods for solving CWI problems. We present a structured framework that consolidates existing research spanning multiple domains, including computational imaging, wave physics, and data science. This study concludes with important lessons learned from existing ML-based methods and identifies technical hurdles and emerging trends through a systematic analysis of the extensive literature on this topic.

42 ENGINEERING↗