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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops

Over the past decade, fundamental changes in artificial intelligence (AI)—from foundational to applied—have delivered dramatic insights across a wide breadth of U.S. Department of Energy (DOE) mission space. AI is helping to augment and improve scientific and engineering workflows (e.g., for control, design, and dramatic performance gains through surrogate models) in national security, the Office of Science, and DOE’s applied energy programs. The progress and potential for AI in DOE science was captured in the 2020 “AI for Science” report from the DOE laboratory community in collaboration with academia and industry. Specific scientific areas ready to further leverage the power of AI ranged from the scale and performance of computational models to data analysis to creating new classes of observations using computer vision. Since that report, the scale and scope of scientific AI have accelerated, revealing new, emergent properties that yield insights that go beyond enabling opportunities to being potentially transformative in the way that scientific problems are posed and solved. Thus, under the guidance of both the Office of Science (SC) and the National Nuclear Security Administration (NNSA), the DOE national laboratories organized a series of workshops in 2022 to gather input on new and rapidly emerging opportunities and challenges of scientific AI. This 2023 report is a synthesis of those workshops. The scientific community believes AI can have a foundational impact on a broad range of DOE missions, including science, energy, and national security. Further, DOE has unique capabilities that enable the community to drive progress in scientific use of AI, building on long-standing DOE strengths and investments in computation, data, and communications infrastructure, spanning the Energy Sciences Network (ESnet), the Exascale Computing Project (ECP), and integrative programs such as the NNSA Office of Defense Programs Advanced Simulation and Computing (ASC) and the SC Scientific Discovery through Advanced Computing (SciDAC) programs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

CyBERT: Cybersecurity Claim Classification by Fine-Tuning the BERT Language Model

We introduce CyBERT, a cybersecurity feature claims classifier based on bidirectional encoder representations from transformers and a key component in our semi-automated cybersecurity vetting for industrial control systems (ICS). To train CyBERT, we created a corpus of labeled sequences from ICS device documentation collected across a wide range of vendors and devices. This corpus provides the foundation for fine-tuning BERT’s language model, including a prediction-guided relabeling process. We propose an approach to obtain optimal hyperparameters, including the learning rate, the number of dense layers, and their configuration, to increase the accuracy of our classifier. Fine-tuning all hyperparameters of the resulting model led to an increase in classification accuracy from 76% obtained with BertForSequenceClassification’s original architecture to 94.4% obtained with CyBERT. Furthermore, we evaluated CyBERT for the impact of randomness in the initialization, training, and data-sampling phases. CyBERT demonstrated a standard deviation of ±0.6% during validation across 100 random seed values. Finally, we also compared the performance of CyBERT to other well-established language models including GPT2, ULMFiT, and ELMo, as well as neural network models such as CNN, LSTM, and BiLSTM. The results showed that CyBERT outperforms these models on the validation accuracy and the F1 score, validating CyBERT’s robustness and accuracy as a cybersecurity feature claims classifier.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Leaf traits and canopy structure together explain canopy functional diversity: an airborne remote sensing approach

Plant functional diversity is strongly connected to photosynthetic carbon assimilation in terrestrial ecosystems. However, many of the plant functional traits that regulate photosynthetic capacity, including foliar nitrogen concentration and leaf mass per area, vary significantly between and within plant functional types and vertically through forest canopies, resulting in considerable landscape-scale heterogeneity in three dimensions. Hyperspectral imagery has been used extensively to quantify functional traits across a range of ecosystems but is generally limited to providing information for top-of-canopy leaves only. On the other hand, lidar data can be used to retrieve the vertical structure of forest canopies. Because these data are rarely collected at the same time, there are unanswered questions about the effect of forest structure on the 3-D spatial patterns of functional traits across ecosystems. In the United States, the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) provides an opportunity to address this structure-function relationship by collecting lidar and hyperspectral data together across a variety of ecoregions. With a fusion of hyperspectral and lidar data from the NEON AOP and field-collected foliar trait data, we assessed the impacts of forest structure on spatial patterns of N. In addition, we examine the influence of abiotic gradients and management regimes on top of canopy %N and total canopy N (i.e. the total amount of N (g/m 2 ) within a forest canopy) at a NEON site consisting of a mosaic of open longleaf pine and dense broadleaf deciduous forests. Here, our resulting maps suggest that in contrast with top-of-canopy values, total canopy N variation is dampened across this landscape resulting in relatively homogeneous spatial patterns. At the same time, we found that leaf functional diversity and canopy structural diversity showed distinct dendritic patterns related to the spatial distribution of plant functional types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Nano-enhanced solid-state hydrogen storage: Balancing discovery and pragmatism for future energy solutions

Nanomaterials have revolutionized the battery industry by enhancing energy storage capacities and charging speeds, and their application in hydrogen (H 2 ) storage likewise holds strong potential, though with distinct challenges and mechanisms. H 2 is a crucial future zero-carbon energy vector given its high gravimetric energy density, which far exceeds that of liquid hydrocarbons. However, its low volumetric energy density in gaseous form currently requires storage under high pressure or at low temperature. This review critically examines the current and prospective landscapes of solid-state H 2 storage technologies, with a focus on pragmatic integration of advanced materials such as metal-organic frameworks (MOFs), magnesium-based hybrids, and novel sorbents into future energy networks. These materials, enhanced by nanotechnology, could significantly improve the efficiency and capacity of H 2 storage systems by optimizing H 2 adsorption at the nanoscale and improving the kinetics of H 2 uptake and release. We discuss various H 2 storage mechanisms—physisorption, chemisorption, and the Kubas interaction—analyzing their impact on the energy efficiency and scalability of storage solutions. The review also addresses the potential of “smart MOFs”, single-atom catalyst-doped metal hydrides, MXenes and entropy-driven alloys to enhance the performance and broaden the application range of H 2 storage systems, stressing the need for innovative materials and system integration to satisfy future energy demands. High-throughput screening, combined with machine learning algorithms, is noted as a promising approach to identify patterns and predict the behavior of novel materials under various conditions, significantly reducing the time and cost associated with experimental trials. In closing, we discuss the increasing involvement of various companies in solid-state H 2 storage, particularly in prototype vehicles, from a techno-economic perspective. In conclusion, this forward-looking perspective underscores the necessity for ongoing material innovation and system optimization to meet the stringent energy demands and ambitious sustainability targets increasingly in demand.

25 ENERGY STORAGE↗
DOE OSTI2023

Impact of Disorder and Defects on Bond Energies in Highly Fluorinated Graphene

Fluorinated graphite materials are of interest for an assortment of applications and can be synthesized under a variety of synthetic conditions from many different types of carbon. Due to such variations, structural disorders in the form of defects and polymorphism are often present. Here, we investigate the impact of local structural variations on the C–F bond dissociation energies (BDEs) in carbon-based fluoride materials using density functional theory (DFT) computational methods. Employing fluorographene (FG) cluster models, we determine the impact of different C–F bonding configurations in the core of each platelet on the equilibrium BDEs for each C–F bond. The introduction of structural disorder decreases the first C–F BDE by approximately 1 eV compared to the canonical arrangement of axial C–F bonds ordered as in a network of cyclohexane “chairs”. Variability of calculated BDEs among the different polymorphs decreases upon subsequent F removal. Common structural tendencies of the adiabatic defluorination pathways for each polymorph are identified. In conclusion, our analysis suggests that at F/C ratios near 1.0, disorder in the local structure can play a significant role in the energetics of the initial carbon fluoride defluorination and that the influence of this configurational disorder diminishes with decreasing F/C ratios.

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

Experimental observation of localized interfacial phonon modes

Interfaces impede heat flow in micro/nanostructured systems. Conventional theories for interfacial thermal transport were derived based on bulk phonon properties of the materials making up the interface without explicitly considering the atomistic interfacial details, which are found critical to correctly describing thermal boundary conductance. Recent theoretical studies predicted the existence of localized phonon modes at the interface which can play an important role in understanding interfacial thermal transport. However, experimental validation is still lacking. Through a combination of Raman spectroscopy and high-energy-resolution electron energy-loss spectroscopy in a scanning transmission electron microscope, we report the experimental observation of localized interfacial phonon modes at ~12 THz at a high-quality epitaxial Si-Ge interface. These modes are further confirmed using molecular dynamics simulations with a high-fidelity neural network interatomic potential, which also yield thermal boundary conductance agreeing well with that measured in time-domain thermoreflectance experiments. Simulations find that the interfacial phonon modes have an obvious contribution to the total thermal boundary conductance. Our findings significantly contribute to the understanding of interfacial thermal transport physics and have impact on engineering thermal boundary conductance at interfaces in applications such as electronics thermal management and thermoelectric energy conversion.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Probabilistic Prediction of Geomagnetic Storms and the K p index

Geomagnetic activity is often described using summary indices to summarize the likelihood of space weather impacts, as well as when parameterizing space weather models. The geomagnetic index K p in particular, is widely used for these purposes. Current state-of-the-art forecast models provide deterministic K p predictions using a variety of methods – including empirically-derived functions, physics-based models, and neural networks – but do not provide uncertainty estimates associated with the forecast. This paper provides a sample methodology to generate a 3-hour-ahead K p prediction with uncertainty bounds and from this provide a probabilistic geomagnetic storm forecast. Specifically, we have used a two-layered architecture to separately predict storm (K p ≥ 5 – ) and non-storm cases. As solar wind-driven models are limited in their ability to predict the onset of transient-driven activity we also introduce a model variant using solar X-ray flux to assess whether simple models including proxies for solar activity can improve the predictions of geomagnetic storm activity with lead times longer than the L1-to-Earth propagation time. By comparing the performance of these models we show that including operationally-available information about solar irradiance enhances the ability of predictive models to capture the onset of geomagnetic storms and that this can be achieved while also enabling probabilistic forecasts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Review of machine learning and deep learning models for toxicity prediction

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

Research & Experimental Medicine↗
DOE OSTI2025

Data Structure Alchemy

In an increasingly more data-driven world, the project set out to uncover the first principles of data-structure design, chart the immense design space they form, and build automation that can synthesize an optimal structure, or even a whole storage engine, for any given workload, hardware platform, and cost target. Data structures are at the center of every computational system and are directly responsible for its performance. Two core technical thrusts were defined: 1) Mapping design spaces for key data-centric abstractions (filters, hash functions, storage-engine layouts, neural-network topologies, blockchain protocols, image layouts, etc.). 2) Developing search & synthesis algorithms, initially analytical cost models, later neural-guided bi-level optimisers that navigate sextillions of candidate designs in seconds and materialise the best one as ready‐to-run code. This report distills the key insights, accomplishments, and impact.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A multirate mass transfer model to represent the interaction of multicomponent biogeochemical processes between surface water and hyporheic zones (SWAT-MRMT-R 1.0)

Abstract. Surface water quality along river corridors can be modulated by hyporheic zones (HZs) that are ubiquitous and biogeochemically active. Watershed management practices often ignore the potentially important role of HZs as a natural reactor. To investigate the effect of hydrological exchange and biogeochemical processes on the fate of nutrients in surface water and HZs, a novel model, SWAT-MRMT-R, was developed coupling the Soil and Water Assessment Tool (SWAT) watershed model and the reaction module from a flow and reactive transport code (PFLOTRAN). SWAT-MRMT-R simulates concurrent nonlinear multicomponent biogeochemical reactions in both the channel water and its surrounding HZs, connecting the channel water and HZs through hyporheic exchanges using multirate mass transfer (MRMT) representation. Within the model, HZs are conceptualized as transient storage zones with distinguished exchange rates and residence times. The biogeochemical processes within HZs are different from those in the channel water. Hyporheic exchanges are modeled as multiple first-order mass transfers between the channel water and HZs. As a numerical example, SWAT-MRMT-R is applied to the Hanford Reach of the Columbia River, a large river in the United States, focusing on nitrate dynamics in the channel water. Major nitrate contaminants entering the Hanford Reach include those from the legacy waste, irrigation return flows (irrigation water that is not consumed by crops and runs off as point sources to the stream), and groundwater seepage resulting from irrigated agriculture. A two-step reaction sequence for denitrification and an aerobic respiration reaction is assumed to represent the biogeochemical transformations taking place within the HZs. The spatially variable hyporheic exchange rates and residence times in this example are estimated with the basin-scale Networks with EXchange and Subsurface Storage (NEXSS) model. Our simulation results show that (1), given a residence time distribution, how the exchange fluxes to HZs are approximated when using MRMT can significantly change the amount of nitrate consumption in HZs through denitrification and (2) source locations of nitrate have a different impact on surface water quality due to the spatially variable hyporheic exchanges.

58 GEOSCIENCES↗
DOE OSTI2022

Mitigating the effects of kickback noise on a comparator

Embodiments herein describe placing a filter network at one of the inputs of the comparator to avoid injecting unequal amounts of kickback noise into the inputs of the comparator. In one embodiment, the filter network matches the impedance seen at the inputs of the comparator. As a result, the amount of kickback noise is essentially equal at the inputs even though the input signals may be at different frequencies. Thus, the kickback noise is essentially cancelled out so that this noise has little to no impact on the output of the comparator.

Hsieh, Kai-An↗
DOE OSTI2023

Neural network-based single material beam-hardening correction for X-ray CT in Additive Manufacturing

Beam-hardening (BH) artifacts are ubiquitous in X-ray CT scans of dense metal additively manufactured (AM) parts. While linearization approaches are useful for correcting beam-hardened data from single material objects, they either require a calibration scan or detailed system and material composition information. In this paper, we introduce a neural network-based, material-agnostic method to correct beam-hardening artifacts. We train a neural network to map the acquired beam-hardened projection values and the corresponding estimated thickness of the part based on an initial segmentation to beam-hardening related parameters, which can be used to compute the coefficients of a linearizing correction polynomial. A key strength of our approach is that, once the network is trained, it can be used for correcting beam hardening from a variety of materials without any calibration scans or detailed system and material composition information. Furthermore, our method is robust to errors in the estimated thickness due to the typical challenge of obtaining an accurate initial segmentation from reconstructions impacted by BH artifacts. We demonstrate the utility of our method to obtain high-quality CT reconstructions from a collection of AM parts -- suppressing cupping and streaking artifacts

Rahman, Obaid↗
DOE OSTI2021

Impact of selenium addition to the cadmium-zinc-telluride matrix for producing high energy resolution X-and gamma-ray detectors

Abstract Both material quality and detector performance have been steadily improving over the past few years for the leading room temperature radiation detector material cadmium-zinc-telluride (CdZnTe). However, although tremendous progress being made, CdZnTe still suffers from high concentrations of performance-limiting defects, such as Te inclusions, networks of sub-grain boundaries and compositional inhomogeneity due to the higher segregation coefficient of Zn. Adding as low as 2% (atomic) Se into CdZnTe matrix was found to successfully mitigate many performance-limiting defects and provide improved compositional homogeneity. Here we report record-high performance of Virtual Frisch Grid (VFG) detector fabricated from as-grown Cd 0.9 Zn 0.1 Te 0.98 Se 0.02 ingot grown by the Traveling Heater Method (THM). Benefiting from superior material quality, we achieved superb energy resolution of 0.77% at 662 keV (as-measured without charge-loss correction algorithms) registered at room temperature. The absence of residual thermal stress in the detector was revealed from white beam X-ray topographic images, which was also confirmed by Infra-Red (IR) transmission imaging under cross polarizers. Furthermore, neither sub-grain boundaries nor their networks were observed from the X-ray topographic image. However, large concentrations of extrinsic impurities were revealed in as-grown materials, suggesting a high likelihood for further reduction in the energy resolution after improved purification of the starting material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Grid-Forming Storage Networks: Analytical Characterization of Damping and Design Insights

Grid-forming storage resources are critical to the operation of power grids with high renewable penetration. Over the years, there has been considerable emphasis on understanding the benefits these inverter-based storage resources bring towards managing volatility and enhancing grid reliability but little is studied about the supplementary advantages latent in their operation. This paper investigates one such application in which we explore the impact of grid-forming distributed storage in damping low-frequency inter-area oscillations. We present a detailed analysis characterizing the impact of inverter-droop and storage size on the slower eigenvalues, highlighting potential design considerations for enhancing system stability.

Chatterjee, Kaustav [BATTELLE (PACIFIC NW LAB)] (O↗
DOE OSTI2025

Numerical simulation of flow and mixing in fracture intersections

Fluid transport through fractured geological formations is strongly influenced by the redistribution of solutes at fracture intersections. In this study, we perform detailed numerical simulations of flow and scalar transport within the intersection of two smooth, planar fractures. The analysis focuses on the mixing ratio, the proportion of solute flux exiting along the outlet branch aligned with the primary inlet flow direction, relative to the total solute flux at the outlets. We systematically investigate how the mixing ratio varies with four key parameters: Peclet number, Reynolds number, flow rate ratio between outlet branches, and fracture intersection angle. Results show that the mixing ratio decreases with increasing Peclet number and outlet flow rate ratio, consistent with reduced diffusive spreading and enhanced streamline routing. While low Reynolds numbers have minimal impact, inertial effects at higher Reynolds numbers significantly increase the mixing ratio. Additionally, acute and obtuse intersection angles alter flow partitioning and modify the solute distribution at the outlets. These findings provide a quantitative basis for incorporating physically realistic mixing behavior—intermediate between complete mixing and streamline-following assumptions—into network-scale transport models. The results have direct relevance to subsurface energy systems, including geothermal energy production, carbon sequestration, and contaminant remediation.

58 GEOSCIENCES↗
DOE OSTI2020

Machine learning enabled discovery of application dependent design principles for two-dimensional materials

Abstract The unique electronic and mechanical properties of two-dimensional (2D) materials make them promising next-generation candidates for a variety of applications. Large-scale searches for high-performing 2D materials are limited to calculating descriptors with computationally demanding first-principles density functional theory. In this work, we alleviate this issue by extending and generalizing crystal graph convolutional neural networks to systems with planar periodicity and train an ensemble of models to predict thermodynamic, mechanical and electronic properties. We carry out a screening of nearly 45,000 structures for two separate applications: mechanical strength and photovoltaics. By collecting statistics of the screened candidates, we investigate structural and compositional design principles that impact the properties of the structures surveyed. Our approach recovers some well-accepted design rules: hybrid organic-inorganic perovskites with lead and tin tend to be good candidates for solar cell applications and titanium based MXenes usually have high stiffness coefficients. Interestingly, other members of the group 4 elements also contribute to increasing the mechanical strength of MXenes. For all-inorganic perovskites, we discover some compositions that have not been deeply studied in the field of photovoltaics and thus open up paths for further investigation. We open-source the code-base to spur further development in this space.

Venturi, Victor (ORCID:0000000156727549)↗
DOE OSTI2023

Adaptive Deep Reinforcement Learning Algorithm for Distribution System Cyber Attack Defense With High Penetration of DERs

With grid modernization, smart inverters are increasingly used to execute advanced controls for distribution network reliability. However, this also increases the cyber-attack space. Here this paper focuses on the defense approaches to restore the system to normal operation circumstances in the presence of cyber-attacks. A unique deep reinforcement learning (DRL) method is developed to minimize voltage violations and reduce power losses for impacted feeders. The defense problem is reformulated as a Markov decision-making process to dynamically control DERs while minimizing load shedding. This is achieved via an improved soft actor-critic (SAC)-based DRL algorithm, which can govern DER set points and load-shedding scenarios in discrete and continuous modes via the auto-tune entropy and Gaussian policy features. Numerical comparison results on the modified IEEE 123-node system with other control approaches, such as Volt-VAR (VV), Volt-Watt (VW), and model predictive control (MPC) show that the proposed method can eliminate voltage violations and provide feasible control actions that perform complete mitigation of cyber-threats.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

COMPASS-FME Synoptic Sites Level 1 Sensor Data v1-2

This is the version 1-2 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our synoptic field sites. COMPASS-FME is studying sites in two distinct regions, the Chesapeake Bay and the Western Lake Erie Basin. We established the network at seven "synoptic" (observational) sites along the Chesapeake Bay and Lake Erie coastlines, collectively generating over three million observations per month, to track and comprehend environmental changes where land and water intersect. Additionally, the two regions provide an interesting contrast of saltwater and freshwater coasts that allow us to differentiate the impacts of inundation and coastal water chemistries in two nationally important coastal systems.L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds and out-of-service flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project**This dataset includes:- An overall dataset README file that describes the current version, gives citation and contact information, etc.- Site- and year-specific folders, each holding up to 12 CSV (comma separated value) data files for each site and plot in that year.- Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, as well as a general description of the site.- Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v1-2 Synoptic L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning.

54 ENVIRONMENTAL SCIENCES↗