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DOE OSTI2014

Invited Article: SUBGLACIOR: An optical analyzer embedded in an Antarctic ice probe for exploring the past climate

This article describes the advances made in the development of a specific optical spectrometer based on the Optical Feedback-Cavity Enhanced Absorption Spectroscopy technique for exploring past climate by probing the original composition of the atmosphere stored in the ice sheet of a glacier. Based on significant technological progresses and unconventional approaches, SUBGLACIOR will be a revolutionary tool for ice-core research: the optical spectrometer, directly embedded in the drilling probe, will provide in situ real-time measurements of deuterium isotopic variations (δ{sup 2}H ) and CH{sub 4} concentrations down to 3500 m of ice depth within a single Antarctic season. The instrument will provide simultaneous and real-time vertical profiles of these two key climate signatures in order to evaluate if a target site can offer ice cores as old as 1.5 million years by providing direct insight into past temperatures and climate cycles. The spectrometer has a noise equivalent absorption coefficient of 2.8 × 10{sup −10} cm{sup −1} Hz{sup −1/2}, corresponding to a detection limit of 0.2 ppbv for CH{sub 4} and a precision of 0.2‰ on the δ{sup 2}H of H{sub 2}O within 1 min acquisition time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2014

Technical Report: Final

The objective of this work was to develop catalyzed nanoporous materials that have superior hydrogen uptake between 300K and 400K and moderate pressures. Platinum nanoparticles were introduced to both activated carbons (ACs) and microporous metal organic frameworks (MMOFs) in order to dissociate molecular hydrogen into an active hydrogen species that diffuses from the catalyst to weakly chemisorbs to the AC/MMOF support; this combined sequence is referred to as the hydrogen spillover mechanism. For all materials studied, maximum excess hydrogen uptake was 1-1.4 wt% (excess) at 300K, falling short of DOE storage goals (5.5 wt% by 2015). Select Pt/AC materials (after in situ catalyst activation) had high uptake (up to 1.4 wt%) at low pressure which significantly exceeded that expected for physisorption. The uptake was not correlated to size of Pt catalyst, but appeared to be associated with high surface activity of the AC support and the methodology of catalyst doping. Multiple techniques were explored to introduce Pt nanoparticles into MMOFs, but most led to significant structural degradation. Ultimately, a ‘pre-bridge’ (PB) technique was used to introduce Pt/AC catalysts into MMOFs, as the PB technique led to virtually non-detectable changes in structure. At high pressure, hydrogen spillover of ~1 wt% (excess) to a PB-MMOF was very slow (i.e. >80 hours at 70-80 bar), which can be attributed to high diffusion barriers in a complex three-surface domain material (Pt, AC, MMOF) as well as unexpected evidence for mechanical instability of the undoped MMOF precursor. In a low-pressure comparison study of three PB-MMOFs, we found evidence that the doping technique may introduce defects which may contribute to enhanced adsorption at 300K. However, we could not rule out the effect of active Pt sites, as common predictors of adsorption generally favored the materials without Pt. Furthermore, spectroscopic evidence provided definitive evidence of weak hydrogen chemisorption to two MMOFs and AC, and was found only for materials containing Pt catalyst. Overall, high uptake via hydrogen spillover requires high catalytic activity and an energy neutral surface landscape for ready diffusion, with little to no correlation to the size of the Pt nanoparticle or textural properties (i.e. surface area or porosity) of the AC or MMOF support.

08 HYDROGEN↗

DOE OSTI2023

Temperature Uncertainty Modeling with Proxy Structural Data as Geostatistical Constraints for Well Siting: An Example Applied to Granite Springs Valley, NV, USA

Utilizing existing temperature and structural information around Granite Springs Valley, Nevada, we build 3D stochastic temperature models with the aim of evaluating the 3D uncertainty of temperature and choosing between candidate exploration well locations . The data used to support the modeling are measured temperatures and structural proxies from 3D geologic modeling, the latter considered "secondary" data. Two stochastic geostatistical techniques are explored for incorporating the structural proxies: cosimulation and local varying mean. With both the cosimulation and local varying mean methods, many equally likely temperature models (i.e., realizations) are produced, from which temperature probability profiles are calculated at candidate well locations. To aid in choosing between the candidate locations, two quantities summarize the temperature probabilities: Vprior and entropy. Vprior quantifies the likelihood for economic temperatures at each candidate location, whereas entropy identifies where new information has the most potential to reduce uncertainty. In general, the cosimulation realizations have smoother spatial structure, and extrapolate high temperatures at candidate locations that are located along the direction of the longest spatial correlation, which are down dip from existing temperature logs. The smooth realizations result in tight temperature probability profiles that are easier to interpret, but they have unrealistic temperature reversals in some locations because the cosimulation technique does not enforce a conductive geothermal gradient as a baseline (i.e., linearly increasing temperature with depth). The local varying mean results produce realizations with more realistic geothermal gradients, with temperatures increasing downward since a depth-temperature relationship is included. However, because they have much noisier spatial nature compared to cosimulation, it is harder to interpret the temperature probability profiles. The different local varying mean results allow the geologist to determine which proxy (e.g., dilation versus distance to fault termination) should be used given the specific geothermal system. In general, Vprior from local varying mean results identify locations that are close to high values for the structural proxies: areas with highe r probabilities for higher temperatures. The entropy results identify where uncertainty is greatest and therefore new drilling information could be most useful. Though these techniques provide useful information, even when applied to areas of sparse data, our comp arison of these two techniques demonstrates the need for new geothermal geostatistics techniques that combine the advantages of these two methods and that are tailored to the spatial uncertainty issues inherent in geothermal exploration.

3D temperature modeling↗

NASA NTRS2009

Spaceflight Systems Training: A Comparison and Contrasting of Techniques for Training Ground Operators and Onboard Crewmembers

When developing techniques and products for instruction on manned spaceflight systems, training organizations are often faced with two very different customers: ground operators and onboard crewmembers. Frequently, instructional development focuses on one of these customers with the assumption that the other s needs will be met by default. Experience teaches us that differing approaches are required when developing training tailored to the specific needs of each customer. As a rule, ground operators require focused instruction on specific areas of expertise. Their knowledge should be of the details of the hardware, software, and operational techniques associated with that system. They often benefit from historical knowledge of how their system has operated over its lifetime. Since several different ground operators may be interfacing with the same system, each individual operator must understand the agreed-to principles by which that system will be run. In contrast, onboard crewmembers require a more broad, hands-on awareness of their operational environment. Their training should be developed with an understanding of the physical environment in which they live and work and the day-to-day tasks they are most likely to perform. Rarely do they require a deep understanding of the details of a system; it is often sufficient to teach them just enough to maintain situational awareness and perform basic tasks associated with maintenance and operation of onboard systems. Crewmembers may also develop unique onboard operational techniques that differ from preceding crews. They should be taught what flexibility they have in systems operations and how their specific habits can be communicated to ground support personnel. This paper will explore the techniques that can be employed when developing training for these unique customers. We will explore the history of International Space Station training development and how past efforts can guide us in creating training for users of future manned spaceflight efforts.

Balmain, Clinton↗

DOE OSTI2017

Insights from Synthetic Star-forming Regions. I. Reliable Mock Observations from SPH Simulations

Through synthetic observations of a hydrodynamical simulation of an evolving star-forming region, we assess how the choice of observational techniques affects the measurements of properties that trace star formation. Testing and calibrating observational measurements requires synthetic observations that are as realistic as possible. In this part of the series (Paper I), we explore different techniques for mapping the distributions of densities and temperatures from the particle-based simulations onto a Voronoi mesh suitable for radiative transfer and consequently explore their accuracy. We further test different ways to set up the radiative transfer in order to produce realistic synthetic observations. We give a detailed description of all methods and ultimately recommend techniques. We have found that the flux around 20 μ m is strongly overestimated when blindly coupling the dust radiative transfer temperature with the hydrodynamical gas temperature. We find that when instead assuming a constant background dust temperature in addition to the radiative transfer heating, the recovered flux is consistent with actual observations. We present around 5800 realistic synthetic observations for Spitzer and Herschel bands, at different evolutionary time-steps, distances, and orientations. In the upcoming papers of this series (Papers II, III, and IV), we will test and calibrate measurements of the star formation rate, gas mass, and the star formation efficiency using our realistic synthetic observations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Luminous red galaxies: selection and classification by combining optical and infrared photometry

We describe a new method of combining optical and infrared photometry to select luminous red galaxies (LRGs) at redshifts z>0.6. We explore this technique using a combination of optical photometry from CFHTLS and Hubble Space Telescope, infrared photometry from the Wide-Field Infrared Survey Explorer satellite, and spectroscopic or photometric redshifts from the DEEP2 Galaxy Redshift Survey or COSMOS. We present a variety of methods for testing the success of our selection and present methods for optimization given a set of rest-frame color and redshift requirements. We have tested this selection in two different regions of the sky, the COSMOS and Extended Groth Strip (EGS) fields, to reduce the effect of cosmic/sample variance. We have used these methods to assemble large samples of LRGs for two different ancillary programs as a part of the Sloan Digital Sky Survey (SDSS)-III/Baryon Oscillation Spectroscopic Survey. This technique is now being used to select ∼600,000 LRG targets for SDSS-IV/Extended Baryon Oscillation Spectroscopic Survey, which began observations in Fall 2014, and will be adapted for the proposed Dark Energy Spectroscopic Instrument survey. We have found that these methods can select high-redshift LRGs efficiently with minimal stellar contamination; this is extremely difficult to achieve with selections that rely on optical photometry alone.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

QC-GN 2 oMS 2 : a Graph Neural Net for High Resolution Mass Spectra Prediction

Predicting the mass spectrum of a molecular ion is often accomplished via three generalized approaches: rules-based methods for bond breaking, deep learning, or quantum chemical (QC) modeling. Rules-based approaches are often limited by the conditions for different chemical subspaces and perform poorly under chemical regimes with few defined rules. QC modeling is theoretically robust but requires significant amounts of computational time to produce a spectrum for a given target. Among deep learning techniques, graph neural networks (GNNs) have performed better than previous work with fingerprint-based neural networks in mass spectra prediction. To explore this technique further, we investigate the effects of including quantum chemically derived information as edge features in the GNN to increase predictive accuracy. The models we investigated include categorical bond order, bond force constants derived from extended tight-binding (xTB) quantum chemistry, and acyclic bond dissociation energies. Throughout this work, we evaluated these models against a control GNN with no edge features in the input graphs. Bond dissociation enthalpies yielded the best improvement with a cosine similarity score of 0.462 relative to the baseline model (0.437). In this work we also apply dynamic graph attention which improves performance on benchmark problems and supports the inclusion of edge features. Between implementations, we investigate the nature of the molecular embedding for spectra prediction and discuss the recognition of fragment topographies in distinct chemistries for further development in tandem mass spectrometry prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Quantifying the Variation in the Number of Donors in Quantum Dots Created Using Atomic Precision Advanced Manufacturing

Atomic-precision advanced manufacturing enables unique silicon quantum electronics built on quantum dots fabricated from small numbers of phosphorus dopants. The number of dopant atoms comprising a dot plays a central role in determining the behavior of charge and spin confined to the dots and thus overall device performance. Here in this work, we use both theoretical and experimental techniques to explore the combined impact of lithographic variation and stochastic kinetics on the number of P incorporations in quantum dots made using these techniques and how this variation changes as a function of the size of the dot. Using a kinetic model of PH3 dissociation augmented with novel reaction barriers, we demonstrate that for a 2 × 3 silicon dimer window the probability that no donor incorporates goes to zero, allowing for certainty in the placement of at least one donor. However, this still comes with some uncertainty in the precise number of incorporated donors (either one or two), and this variability may still impact certain applications. We also examine the impact of the size of the initial lithographic window, finding that the incorporation fraction saturates to δ-layer-like coverage as the circumference-to-area ratio decreases. We predict that this incorporation fraction depends strongly on the dosage of the precursor and that the standard deviation of the number of incorporations scales as ~√n, as would be expected for a sequence of largely independent incorporation events. Finally, we characterize an array of 36 experimentally prepared multidonor 3 × 3 nm lithographic windows with scanning tunneling microscopy, measuring the fidelity of the lithography to the desired array and the final location of PH x fragments within these lithographic windows. We use our kinetic model to examine the expected variability due to the observed lithographic error, predicting a negligible impact on incorporation statistics. We find good agreement between our model and the inferred incorporation locations in these windows from scanning tunneling microscope measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Mapping Permafrost Variability and Degradation Using Seismic Surface Waves, Electrical Resistivity, and Temperature Sensing: A Case Study in Arctic Alaska

Subsurface processes significantly influence surface dynamics in permafrost regions, necessitating utilizing diverse geophysical methods to reliably constrain permafrost characteristics. This research uses multiple geophysical techniques to explore the spatial variability of permafrost in undisturbed tundra and its degradation in disturbed tundra in Utqiagvik, Alaska. Here, we integrate multiple quantitative techniques, including multichannel analysis of surface waves (MASW), electrical resistivity tomography (ERT), and ground temperature sensing, to study heterogeneity in permafrost’s geophysical characteristics. MASW results reveal active layer shear wave velocities (V s ) between 240 and 370 m/s, and permafrost V s between 450 and 1,700 m/s, typically showing a low-high-low velocity pattern. Additionally, we find an inverse relationship between in situ V s and ground temperature measurements. The V s profiles along with electrical resistivity profiles reveal cryostructures such as cryopeg and ice-rich zones in the permafrost layer. The integrated results of MASW and ERT provide valuable information for characterizing permafrost heterogeneity and cryostructure. Corroboration of these geophysical observations with permafrost core samples’ stratigraphies and salinity measurements further validates these findings. This combination of geophysical and temperature sensing methods along with permafrost core sampling confirms a robust approach for assessing permafrost’s spatial variability in coastal environments. Our results also indicate that civil infrastructure systems such as gravel roads and pile foundations affect permafrost by thickening the active layer, lowering the V s , and reducing heterogeneity. We show how the resulting V s profiles can be used to estimate key parameters for designing buildings in permafrost regions and maintaining existing infrastructure in polar regions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

High-throughput screening of tribological properties of monolayer films using molecular dynamics and machine learning

Monolayer films have shown promise as a lubricating layer to reduce friction and wear of mechanical devices with separations on the nanoscale. These films have a vast design space with many tunable properties that can affect their tribological effectiveness. For example, terminal group chemistry, film composition, and backbone chemistry can all lead to films with significantly different tribological properties. This design space, however, is very difficult to explore without a combinatorial approach and an automatable, reproducible, and extensible workflow to screen for promising candidate films. Here, using the Molecular Simulation Design Framework (MoSDeF), a combinatorial screening study was performed to explore 9747 unique monolayer films (116 964 total simulations) and a machine learning (ML) model using a random forest regressor, an ensemble learning technique, to explore the role of terminal group chemistry and its effect on tribological effectiveness. The most promising films were found to contain small terminal groups such as cyano and ethylene. The ML model was subsequently applied to screen terminal group candidates identified from the ChEMBL small molecule library. Approximately 193 131 unique film candidates were screened with approximately a five order of magnitude speed-up in analysis compared to simulation alone. The ML model was thus able to be used as a predictive tool to greatly speed up the initial screening of promising candidate films for future simulation studies, suggesting that computational screening in combination with ML can greatly increase the throughput in combinatorial approaches to generate in silico data and then train ML models in a controlled, self-consistent fashion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Quantitative Methods for Comparing Different Polyline Stream Network Models

Two techniques for exploring relative horizontal accuracy of complex linear spatial features are described and sample source code (pseudo code) is presented for this purpose. The first technique, relative sinuosity, is presented as a measure of the complexity or detail of a polyline network in comparison to a reference network. We term the second technique longitudinal root mean squared error (LRMSE) and present it as a means for quantitatively assessing the horizontal variance between two polyline data sets representing digitized (reference) and derived stream and river networks. Both relative sinuosity and LRMSE are shown to be suitable measures of horizontal stream network accuracy for assessing quality and variation in linear features. Both techniques have been used in two recent investigations involving extracting of hydrographic features from LiDAR elevation data. One confirmed that, with the greatly increased resolution of LiDAR data, smaller cell sizes yielded better stream network delineations, based on sinuosity and LRMSE, when using LiDAR-derived DEMs. The other demonstrated a new method of delineating stream channels directly from LiDAR point clouds, without the intermediate step of deriving a DEM, showing that the direct delineation from LiDAR point clouds yielded an excellent and much better match, as indicated by the LRMSE.

58 GEOSCIENCES↗

NASA NTRS1996

Meteoritic Sulfur Isotopic Analysis

Funds were requested to continue our program in meteoritic sulfur isotopic analysis. We have recently detected a potential nucleosynthetic sulfur isotopic anomaly. We will search for potential carriers. The documentation of bulk systematics and the possible relation to nebular chemistry and oxygen isotopes will be explored. Analytical techniques for delta(sup 33), delta(sup 34)S, delta(sup 36)S isotopic analysis were improved. Analysis of sub milligram samples is now possible. A possible relation between sulfur isotopes and oxygen was detected, with similar group systematics noted, particularly in the case of aubrites, ureilites and entstatite chondrites. A possible nucleosynthetic excess S-33 has been noted in bulk ureilites and an oldhamite separate from Norton County. High energy proton (approximately 1 GeV) bombardments of iron foils were done to experimentally determine S-33, S-36 spallogenic yields for quantitation of isotopic measurements in iron meteorites. Techniques for measurement of mineral separates were perfected and an analysis program initiated. The systematic behavior of bulk sulfur isotopes will continue to be explored.

Thiemens, Mark H.↗

DOE OSTI2021

Characterizing Signatures of Geothermal Exploration Data with Machine Learning Techniques: An Application to the Nevada Play Fairway Analysis

We are introducing machine learning methods to the play fairway analysis to generate geothermal potential maps to support the evaluation of geothermal resource potential and the exploration for undiscovered blind geothermal systems in the Nevada Great Basin region. Our project aims to identify new ways to combine the play fairway data and empirically organize relationships between feature weights and labels in an improved workflow. As a means of doing this, we introduce machine learning methods to evaluate the influence of certain geological and geophysical features/feature sets in predicting geothermal favorability. This report highlights promising approaches based on supervised and unsupervised learning methods. First, we demonstrate a filter method applied to supervised classification modeling. The supervised filter method is based on permutation analysis to evaluate every possible feature combination/drop out scenario and rank feature influence based on the performance variance of supervised classification models. Additionally, we present an unsupervised factor analysis based on principal component analysis coupled with a semi-supervised kmeans clustering algorithm. This analysis allows us to identify the optimal number of groups/clusters for training sites and structural settings to identify feature patterns including correlation, variance, and latent and dominant feature relationships. The results from these methods offer a promising avenue for identifying favorable sources of predictive information to identify the locations of blind geothermal systems and furthering our understanding of complex geothermal feature and label relationships in the Great Basin region and beyond.

15 GEOTHERMAL ENERGY↗

DOE OSTI2017

Nanoscale Probing of Elastic–Electronic Response to Vacancy Motion in NiO Nanocrystals

Measuring the diffusion of ions and vacancies at nanometer length scales is crucial to understanding fundamental mechanisms driving technologies as diverse as batteries, fuel cells, and memristors; yet such measurements remain extremely challenging. Here in this paper, we employ a multimodal scanning probe microscopy (SPM) technique to explore the interplay between electronic, elastic, and ionic processes via first-order reversal curve I–V measurements in conjunction with electrochemical strain microscopy (ESM). The technique is employed to investigate the diffusion of oxygen vacancies in model epitaxial nickel oxide (NiO) nanocrystals with resistive switching characteristics. Results indicate that opening of the ESM hysteresis loop is strongly correlated with changes to the resonant frequency, hinting that elastic changes stem from the motion of oxygen (or cation) vacancies in the probed volume of the SPM tip. These changes are further correlated to the current measured on each nanostructure, which shows a hysteresis loop opening at larger (~2.5 V) voltage windows, suggesting the threshold field for vacancy migration. This study highlights the utility of local multimodal SPM in determining functional and chemical changes in nanoscale volumes in nanostructured NiO, with potential use to explore a wide variety of materials including phase-change memories and memristive devices in combination with site-correlated chemical imaging tools.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Atomic Layer Deposition of Aluminum Sulfide: Growth Mechanism and Electrochemical Evaluation in Lithium-Ion Batteries

This work describes the synthesis of aluminum sulfide (AlS x ) thin films by atomic layer deposition (ALD) using tris(dimethylamido)aluminum and hydrogen sulfide. We employed a suite of in situ measurement techniques to explore the ALD AlS x growth mechanism, including quartz crystal microbalance, quadrupole mass spectrometry, and Fourier transform infrared spectroscopy. A variety of ex situ characterization techniques were used to determine the growth characteristics, morphology, elemental composition, and crystallinity of the resultant AlS x films. This study revealed that the AlS x growth was self-limiting in the temperature range 100–250 °C, and the growth per cycle decreased linearly with increasing temperature from ~0.45 Å/cycle at 100 °C to ~0.1 Å/cycle at 250 °C. The AlSx films were amorphous in this temperature range. We conducted electrochemical testing to evaluate the ALD AlS x as a potential anode material for lithium-ion batteries (LIBs). Finally, the ALD AlS x exhibited reliable cyclability over 60 discharge–charge cycles with a sustainable discharge capacity of 640 mAh/g at a current density of 100 mA/g in the voltage window of 0.6–3.5 V.

25 ENERGY STORAGE↗

NASA NTRS1993

Determination of use of a real time tone tracker to obtain same beam interferometry data

The radio metric tracking technique known as Same-Beam Interferometry (SBI) has been shown to improve orbit determination accuracy for the Magellan and Pioneer 12 orbiter. Previous efforts to explore the technique were carried out by making open loop recordings of the carrier signals from the two spacecraft and extracting their phases through post processing. This paper reports on the use of a closed loop receiver to simultaneously measure the carrier signals from two spacecraft in order to produce SBI data in near real time. The Experiment Tone Tracker is a digital closed loop receiver installed in two of NASA's Deep Space Network stations which can simultaneously extract the phase of up to eight tones. The receivers were used in late September and October of 1992 to collect Doppler and SBI data from Pioneer 12 and Magellan. The demise of the Pionner 12 on October 8th during the start-up phase of our tests precluded the collection of an extensive set of SBI data, however two passes of SBI and several arcs of single spacecraft Doppler data were recorded. The SBI data were analyzed and determined to have statistical errors consistent with error models and similar to open loop data.

Nandi, S.↗

DOE OSTI2023

Temperature uncertainty modelling with proxy structural data as geostatistical constraints for well siting: an example applied to Granite Springs Valley, NV, USA

Utilizing existing temperature and structural geology information around Granite Springs Valley, Nevada, we build 3D stochastic temperature models with the aims of evaluating the 3D uncertainty of temperature and choosing between candidate exploration well locations. The data used to support the modelling are measured temperatures and structural proxies from 3D geologic modelling (distance to fault, distance to fault intersections and terminations, Coulomb stress change and dilation tendency), the latter considered ‘secondary’ data. Two stochastic geostatistical techniques are explored for incorporating the structural proxies: cosimulation and local varying mean. With both the cosimulation and local varying mean methods, many equally-likely temperature models (i.e. realizations) are produced, from which temperature probability profiles are calculated at candidate well locations. To aid in choosing between the candidate locations, two quantities summarize the temperature probabilities: V prior and entropy. V prior quantifies the likelihood for economic temperatures at each candidate location, whereas entropy identifies where new information has the most potential to reduce uncertainty. In general, the cosimulation realizations have smoother spatial structure, and extrapolate high temperatures at candidate locations that are located along the direction of the longest spatial correlation, which are down dip from existing temperature logs. The smooth realizations result in tight temperature probability profiles that are easier to interpret, but they have unrealistic temperature reversals in some locations because of the dipping ellipsoid shape created and that the cosimulation technique does not enforce a conductive geothermal gradient as a baseline (i.e. linearly increasing temperature with depth). The local varying mean results produce realizations with more realistic geothermal gradients, with temperatures increasing downward since a depth-temperature relationship is included. However, because they have much noisier spatial nature compared to cosimulation, it is harder to interpret the temperature probability profiles. The different local varying mean results allow the geologist to determine which proxy (e.g. dilation v. distance to fault termination) should be used given the specific geothermal system. In general, V prior from local varying mean results identify locations that are close to high values for the structural proxies: areas with higher probabilities for higher temperatures. The entropy results identify where uncertainty is greatest and therefore new drilling information could be most useful. Though these techniques provide useful information, even when applied to areas of sparse data, our comparison of these two techniques demonstrates the need for new geothermal geostatistics techniques that combine the advantages of these two methods and that are tailored to the spatial uncertainty issues inherent in geothermal exploration.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Towards Low-Overhead Resilience for Data Parallel Deep Learning

Data parallel techniques have been widely adopted both in academia and industry as a tool to enable scalable training of deep learning models. At scale, DL training jobs can fail due to software or hardware bugs, may need to be preempted or terminated due to unexpected events, or may perform suboptimally because they were misconfigured. Under such circumstances, there is a need to recover and/or reconfigure data-parallel DL training jobs on-the-fly, while minimizing the impact on the accuracy of the DNN model and the runtime overhead. In this regard, state-of-art techniques adopted by the HPC community mostly rely on checkpoint-restart, which inevitably leads to loss of progress, thus increasing the runtime overhead. In this paper we explore alternative techniques that exploit the properties of modern deep learning frameworks (overlapping of gradient averaging and weight updates with local gradient computations through pipeline parallelism) to reduce the overhead of resilience/elasticity. To this end we introduce a failure simulation framework and two resilience strategies (immediate mini-batch rollback and lossy forward recovery), which we study compared with checkpoint-restart approaches in a variety of settings in order to understand the trade-offs between the accuracy loss of the DNN model and the runtime overhead.

data-parallel training↗