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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 OSTI2026

Identifying Anomalous DESI Galaxy Spectra with a Variational Autoencoder

The tens of millions of spectra being captured by the Dark Energy Spectroscopic Instrument (DESI) provide tremendous discovery potential. In this work we show how Machine Learning, in particular Variational Autoencoders (VAE), can detect anomalies in a sample of approximately 200,000 DESI spectra comprising galaxies, quasars and stars. We demonstrate that the VAE can compress the dimensionality of a spectrum by a factor of 100, while still retaining enough information to accurately reconstruct spectral features. We then detect anomalous spectra as those with high reconstruction error and those which are isolated in the VAE latent representation. The anomalies identified fall into two categories: spectra with artefacts and spectra with unique physical features. Awareness of the former can help to improve the DESI spectroscopic pipeline; whilst the latter can lead to the identification of new and unusual objects. To further curate the list of outliers, we use the Astronomaly package which employs Active Learning to provide personalised outlier recommendations for visual inspection. In this work we also explore the VAE latent space, finding that different object classes and subclasses are separated despite being unlabelled. We demonstrate the interpretability of this latent space by identifying tracks within it that correspond to various spectral characteristics. For example, we find tracks that correspond to increasing star formation and increase in broad emission lines along the Balmer series. In upcoming work we hope to apply the methods presented here to search for both systematics and astrophysically interesting objects in much larger datasets of DESI spectra.

Nicolaou, C. [University Coll. London] (ORCID:0000↗
DOE OSTI2023

K 2 Co 2 TeO 6 : A layered magnet with a S = 1 2 Co 2 + honeycomb lattice

Recent observations of Kitaev interactions in d 7 electron configurations have generated research interest in Co 2+ based honeycomb magnets. Here, we report the synthesis of a K 2 Co 2 TeO 6 single crystal. X-ray diffraction and electron microscopy experiments show that K 2 Co 2 TeO 6 crystallizes in a $P$6 3 /$mcm$ space group with undistorted honeycomb layers of Co 2+ ions. Anisotropic magnetic and thermodynamic characterizations demonstrate an effective S = $\frac{1}{2}$ state of Co 2+ at low temperature, competing magnetic phases, and weak magnetic transitions that can be suppressed by applying fields. Moreover, the interlayer K disorder level is tunable by thermal treatments, and it affects the magnetic features obviously. In conclusion, our findings provide a new and promising platform for studying Kitaev physics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Quantum fragmentation in the extended quantum breakdown model

We introduce a one-dimensional (1D) extended quantum breakdown model comprising a fermionic and a spin degree of freedom per site, and featuring a spatially asymmetric breakdown-type interaction between the fermions and spins. Furthermore, our model resembles the breakdown process of particles incident into a cloud chamber with nonzero quantum amplitudes of both exciting and not exciting the local vapor atoms. We analytically show that, in the absence of any magnetic field for the spins, the model exhibits Hilbert space fragmentation within each symmetry sector into exponentially many Krylov subspaces and hence displays nonthermal dynamics. Here, we demonstrate that the fragmentation naturally occurs in an entangled basis and thus provides an example of “quantum fragmentation.” Besides establishing the nature of fragmentation analytically, we also study the long-time behavior of the entanglement entropy and its deviation from the expected Page value as a probe of ergodicity in the system. Upon introducing a magnetic field for the spins, most of the Krylov subspaces merge and the model becomes chaotic. Finally, we study the effects of strong randomness on the system and observe behavior similar to that of many-body localized systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Signatures of an energetic charged body streaming in a plasma

A charged body moving in a plasma can excite a variety of linear and nonlinear waves in the form of trailing wakes, fore-wake shocks, and precursor solitons. These structures can further interact with the background plasma to create secondary effects that can serve as signatures of the passage of the charged body. Using particle-in-cell simulations, we carry out a basic investigation of the dynamics of a plasma system that is being traversed by an energetic charged body. Using two different shapes of this charged source, namely, an idealized infinite length planar source and a two-dimensional thin rectangular source, we examine the differences in the nature of the excited wave structures and their consequent impact on the background plasma. Our simulations reveal interesting features such as the dependence of the precursor speeds on the total charge on the driving source, local particle trapping, and energization of the trapped particles in various regions along the traversal path leading to the formation of energetic charged beamlets. So our basic findings could find practical applications such as in analyzing the trajectories of charged objects like space debris orbiting in the ionosphere.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Accelerating Bilevel Optimization With Hierarchical Many-Threaded Parallel Differential Evolution

Bilevel optimization is encountered in many relevant real-world applications. The main feature of this type of problem is that an upper-level optimization problem is constrained by a nested lower-level optimization problem. Because of this nested structure, bilevel problems (BLPs) are usually computationally expensive to solve. Differential evolution (DE) has demonstrated promising results in solving BLPs of relatively small scales. As the problem scale increases, the decision space becomes intrinsically larger, requiring a growing number of function evaluations for the method to work properly. In this context, heavy parallelization and high-performance computing techniques are indispensable to enable the resolution of more complex and challenging optimization problems. Hence, we propose a hierarchical many-threaded parallel DE approach for BLPs, where both levels are parallelized. The computational experiments demonstrate that the parallel implementation achieved runtime speeds ranging from 44 to 2559 times faster than the sequential version on a well-known scalable SMD benchmark test problem when executed on an NVIDIA A100 GPU. The findings indicate that the algorithm’s convergence is strongly influenced by the number of both upper- and lower-level generations. Moreover, the success of experiments with large-scale problems is closely linked to the choice of small population sizes.

Dufek, Amanda S↗
DOE OSTI2025

DEDUPKV: A Space-Efficient and High-Performance Key-Value Store via Fine-Grained Deduplication

Log-Structured Merge Tree (LSM-tree) based key-value stores excel in write-intensive environments but suffer from data duplication, consuming up to 49% of storage space in LSM-tree-based key-value store deployments. Traditional solutions like compression and coarse-grained file system-level deduplication introduce overhead or have limited effectiveness. In this study, we propose DedupKV, a fine-grained deduplication framework tailored for LSM-tree, maximizing data reduction efficiency while minimizing write stalls and read overheads. DedupKV features three key innovations: (1) FLUSH-integrated inline deduplication, which removes duplicates during memory-to-storage writes; (2) WAL file-based offline deduplication, repurposing write-ahead logs to avoid double writes; and (3) elastic execution, dynamically balancing inline and offline deduplication based on memory pressure and workload intensity. Additionally, dynamic granularity management reduces deduplication metadata overhead. We implemented these four ideas in RocksDB for the first time and conducted experiments in a Linux environment. Our evaluation shows that WAL file-based offline deduplication and DedupKV outperform BlobDB by 33% and 23%, respectively, in write-heavy workloads, while reducing write amplification by 1.2 ×, 2 ×, and 1.6 × for real KV datasets.

Jamil, Safdar [Sogang University]↗
DOE OSTI2025

Hyper Spectral Anomaly Detection

The HSA is a statistics based anomaly detection model. The model performs unsupervised anomaly detection, based on a datapoint's density and similarity within a dataset. Density and similarity data are encoded into an affinity matrix. The affinity matrix is evolved to summarize the data's structure on greater topographical scales within the data's function space. The set of evolved affinity matrices and an anomaly score vector are passed to a user defined penalized objective function. The penalized objective function of anomaly scores is then minimized. Data points where the absolute value of the z-scores of anomaly scores greater than a specified threshold are predicted as anomalies. A novel multi-filter feature has also been implemented. To reduce false positive rates, the multi-filter records the indexes of the HSA predictions. A new dataset and data loader are instantiated consisting of all the initial HSA predictions and non-anomalous data points in a 10% and 90% split respectively. The HSA is then run through this data set and a count of number of times a data point is predicted is kept. In this way the initial predictions may be compared with data spanning the entire dataset. After the multi-filter is complete, all datapoints will have an associated anomaly score, as well as a multi-filter prediction count to further filter the anomalous predictions.

Rogers, DempseyD [Idaho National Laboratory (INL),↗
DOE OSTI2021

Scalable augmented enumeration and metadata operations for large filesystems

Systems, apparatus, and methods are disclosed for performing scalable operations in a file system. Metadata entries in a namespace or directory tree are sharded across multiple file metadata servers. An augmented enumeration operation, such as listing a directory, is parallelized across the multiple file metadata servers, transparently to clients. Exemplary augmentation features can include filtering and sorting. Augmentation features can be executed concurrently with enumeration, prior to enumeration, after enumeration, or as a combination of these, and can utilize pre-built index structures or holding structures for intermediate results. Augmented enumeration operations can also include no-output operations such as changing file attributes or deleting a file, and cumulative operations such as counting total disk space usage. The parallelization is compatible with tree-level parallelization and storage-level parallelization. Disclosed technologies can be applied to other fields requiring scalable enumeration, such as database and network applications.

Grider, Gary A.↗
DOE OSTI2022

The Geothermal Data Repository: Ten Years of Supporting the Geothermal Industry with Open Access to Geothermal Data

The Department of Energy's (DOE) Geothermal Data Repository (GDR) is celebrating its tenth anniversary! Over the last decade it has grown from the simple idea of storing public data in a centralized location to a valuable tool at the center of the US geothermal scientific community and an integral part of the DOE Geothermal Technologies Office (DOE GTO) project management strategy. Researchers funded by the DOE GTO have contributed over 1,300 data submissions to the GDR. These data have been used to further advancements in geothermal science, economic analysis, exploration, research, development, and operational efficiency. The adoption of open data methodologies and a data management strategy that prioritizes universal open access and standardized, interoperable data have further increased the value of GDR data, making them available across a distributed network of data sharing partners and improving their utility to other industries and related fields, including material science and space exploration. Incorporating feedback from users has been critical to the GDR's success, allowing it to grow over the years to meet the evolving needs of the geothermal community. This paper will explore some of many changes that occurred throughout the GDRs tenure and the lessons learned along the way, as well as highlight some of the new features and recent improvements that been implemented to support innovation, reduce duplication of effort, and advance the geothermal industry as a whole.

access↗
DOE OSTI2023

Veneziano variations: how unique are string amplitudes?

String theory offers an elegant and concrete realization of how to consistently couple states of arbitrarily high spin. But how unique is this construction? In this paper we derive a novel, multi-parameter family of four-point scattering amplitudes exhibiting i) polynomially bounded high-energy behavior and ii) exchange of an infinite tower of high-spin modes, albeit with a finite number of states at each resonance. These amplitudes take an infinite-product form and, depending on parameters, exhibit mass spectra that are either unbounded or bounded, thus corresponding to generalizations of the Veneziano and Coon amplitudes, respectively. For the bounded case, masses converge to an accumulation point, a peculiar feature seen in the Coon amplitude but more recently understood to arise naturally in string theory [1]. Importantly, our amplitudes contain free parameters allowing for the customization of the slope and offset of the spin-dependence in the Regge trajectory. We compute all partial waves for this multi-parameter class of amplitudes and identify unitary regions of parameter space. For the unbounded case, we apply similar methods to derive new deformations of the Veneziano and Virasoro-Shapiro amplitudes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Accelerated impurity solver for DMFT and its diagrammatic extensions

Here, we present ComCTQMC, a GPU accelerated quantum impurity solver. It uses the continuous-time quantum Monte Carlo (CTQMC) algorithm wherein the partition function is expanded in terms of the hybridisation function (CT-HYB). ComCTQMC supports both partition and worm-space measurements, and it uses improved estimators and the reduced density matrix to improve observable measurements whenever possible. ComCTQMC efficiently measures all one and two-particle Green's functions, all static observables which commute with the local Hamiltonian, and the occupation of each impurity orbital. ComCTQMC can solve complex-valued impurities with crystal fields that are hybridized to both fermionic and bosonic baths. Most importantly, ComCTQMC utilizes graphical processing units (GPUs), if available, to dramatically accelerate the CTQMC algorithm when the Hilbert space is sufficiently large. We demonstrate acceleration by a factor of over 600 (100) in a simulation of δ-Pu at 600 K with (without) crystal fields. In easier problems, the GPU offers less impressive acceleration or even decelerates the CTQMC. Here we describe the theory, algorithms, and structure used by ComCTQMC in order to achieve this set of features and level of acceleration.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Metal–Oxo Cluster Formation Using Ammonium and Sulfate to Differentiate M IV (Th, U, Ce) Chemistries

Isolating isostructural compounds of tetravalent metals M IV (Zr, Hf, Ce, Th, U, Pu, Np) improves our understanding of metal hydrolysis and coordination behavior across the periodic table. These metals form polynuclear clusters typified by the hexamer [M IV 6 O 4 (OH) 4 ] 12+ . Exploiting the ammonium M IV -sulfate (Ce IV , Th IV , and U IV ) phase space targeting rapid crystallization, we isolate the common hexamer [M IV 6 (OH) 4 (O) 4 ] 12+ but with different numbers of capping sulfates and water molecules for Ce IV , Th IV , and U IV . Furthermore, these phases allowed a direct comparison of bonding trends across the series. Upon cocrystallization with the hexamers, higher complex structures can be identified. Thorium features assemblies with monomer-linked hexamer chains. Uranium features assemblies with sulfate-bridged hexamers and the supramolecular assembly of 14 hexamers into the U 84 , [U 6 (OH) 4 (O) 4 ) 14 (SO 4 ) 120 (H 2 O) 42 ] 72– . Last, cerium showcases the isolation from monomers to the Ce 62 , [Ce 62 (OH) 30 (O) 58 (SO 4 ) 71 (H 2 O) 33.25 ] 41– . Furthermore, small-angle X-ray scattering (room temperature) shows ammonium-induced cluster assembly for Ce IV but minimal reactivity for U IV and Th IV . In this study, because the phases crystallized at elevated temperature demonstrates favorable cluster assembly, these solution phase results were surprising and suggest some other characteristics such as Ce’s facile redox behavior, contributes to its solution-phase speciation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

First-Principles Dissociation Pathways of BCl 3 on the Si(100)-2 × 1 Surface

BCl 3 is a promising acceptor precursor for atomic-precision δ-doping of silicon, as it has been observed to rapidly dissociate into boron doped into the silicon surface and surface chlorine, which can be removed upon annealing. The chemical pathway and the resulting kinetics, through which BCl 3 adsorbs and dissociates on silicon, however, have only been partially explained. Here, in this work, we use density functional theory to expand the dissociation reactions of BCl 3 to include reactions that take place across multiple silicon dimer rows and reactions which end in a bare B atom either at the surface, substituted for a surface silicon, or in a subsurface position. We further simulate the resulting scanning tunneling microscopy images for each of these BCl x dissociation fragments, demonstrating that they often display distinct features that may allow for relatively confident experimental identification. Finally, we input the full dissociation pathway for BCl 3 into a kinetic Monte Carlo model, which simulates realistic reaction pathways as a function of environmental conditions, such as the pressure and temperature of dosing. We find that BCl 2 is broadly dominant at low temperatures, while high temperatures and ample space on the silicon surface for dissociation encourage the formation of bridging BCl fragments and B substitutions on the surface. This work provides the chemical mechanisms for understanding atomic-precision doping of Si with B, enabling a number of relevant quantum applications, such as bipolar nanoelectronics, acceptor-based qubits, and superconducting Si.

Campbell, Quinn T. [Sandia National Laboratories (↗
DOE OSTI2025

Shearing approach to gauge-invariant Trotterization

Universal quantum simulations of gauge field theories are exposed to the risk of gauge symmetry violations when it is not known how to compile the desired operations exactly using the available gate set. In this article, we show how time evolution can be compiled in an Abelian gauge theory—if only approximately—without compromising gauge invariance, by graphically motivating a block-diagonalization procedure. When gauge-invariant interactions are associated with a “spatial network” in the space of discrete quantum numbers, it is seen that cyclically shearing the spatial network converts simultaneous updates to many quantum numbers into conditional updates of a single quantum number; ultimately, this eliminates any need to pass through (and acquire overlap onto) unphysical intermediate configurations. Shearing is explicitly applied to gauge-matter and magnetic interactions of lattice quantum electrodynamics. The features that make shearing successful at preserving Abelian gauge symmetry may also be found in non-Abelian theories, bringing one closer to gauge-invariant simulations of quantum chromodynamics.

Gauge theories↗
DOE OSTI2023

A Graphical Model for Fusing Diverse Microbiome Data

This paper develops a Bayesian graphical model for fusing disparate types of count data. The motivating application is the study of bacterial communities from diverse high-dimensional features, in this case, transcripts, collected from different treatments. In such datasets, there are no explicit correspondences between the communities and each corresponds to different factors, making data fusion challenging. We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data. This latent variable model jointly characterizes the observed data through a common multivariate Gaussian latent space that parameterizes the set of multinomial probabilities of the transcriptome counts. The covariance matrix of the latent variables induces a covariance matrix of co-dependencies between all the transcripts, effectively fusing multiple data sources. We present a computationally scalable variational Expectation-Maximization (EM) algorithm for inferring the latent variables and the parameters of the model. Here, the inferred latent variables provide a common dimensionality reduction for visualizing the data and the inferred parameters provide a predictive posterior distribution. In addition to simulation studies that demonstrate the variational EM procedure, we apply our model to a bacterial microbiome dataset.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

SAS4A/SASSYS-1 Lead Verification Test Suite

SAS4A/SASSYS-1 is a simulation tool used to perform deterministic analyses of anticipated events as well as design basis and beyond design basis accidents for advanced liquid-metal-cooled nuclear reactors. With its origin as SAS1A in the late 1960s, the SAS series of codes has been under continuous use and development for over forty-five years and represents a critical investment in safety analysis capabilities for the U.S. Department of Energy. Although SAS4A/SASSYS-1 was developed to support the analysis of any liquid-metal-cooled nuclear reactor, it has primarily been utilized to design and analyze Sodium Fast Reactors (SFRs). As a result, most of the verification basis for SAS4A/SASSYS-1 utilizes sodium as a coolant and geometry descriptions that are typical of a SFR facility. In this work, new verification problems are defined to extend the applicability of the SAS4A/SASSYS-1 verification basis into Lead Fast Reactor (LFR) design space. A review of the existing SAS4A/SASSYS-1 SFR verification test suite is performed to determine the number of test cases that are SFR specific. SFR specific test cases are replicated using lead as a coolant and system layouts that are representative of LFRs. This report provides definitions and reference solutions for seven new test cases that scope lead-specific features and capabilities of the software. For a verification test to be considered acceptable, comparisons of analytical solutions to SAS4A/SASSYS-1 predictions must produce negligible or justifiable errors. All LFR specific test cases are found to be acceptable.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

How structural differences influence cross-model consistency: An electric sector case study

Multiple models are often employed to describe a range of possible outcomes for one or more scenarios, yielding insights into causal relationships and their uncertainties. Electric sector capacity expansion scenarios are a common topic of such efforts due to the economic influence of the electric sector, but model results typically span a broad solution space despite efforts to harmonize input assumptions, making decision implications difficult to discern. This study investigates the relationship between input harmonization and cross-model scenario consistency under disparate electric sector scenarios. We compare cross-model consistency between two state-of-the-art electric sector capacity expansion models (GCAM-USA and ReEDS) for six electric sector scenarios comprising alternate assumptions about fossil fuel resource availability, technology innovation, and long-term economy-wide transitions under four harmonization configurations varying model representations of electricity demand, fuel prices, renewable resources, and capacity retirements. These comparisons reveal that cross-model consistency can vary across scenarios under a given harmonization configuration, suggesting that harmonization efforts must often be scenario-specific if comparable cross-model consistency is desired. Model structural differences can hinder consistency, and the impact of these differences can depend on the scenario. Ultimately, thorough harmonization can reveal insights into cross-model consistency, which can be used to tighten uncertainty bounds and improve the decision-making implications of multi-model activities.

Cohen, Stuart↗
DOE OSTI2023

Exploring Classification of Topological Priors With Machine Learning for Feature Extraction

In many scientific endeavors, increasingly abstract representations of data allow for new interpretive methodologies and conceptualization of phenomena. For example, moving from raw imaged pixels to segmented and reconstructed objects allows researchers new insights and means to direct their studies toward relevant areas. Thus, the development of new and improved methods for segmentation remains an active area of research. With advances in machine learning and neural networks, scientists have been focused on employing deep neural networks such as U-Net to obtain pixel-level segmentations, namely, defining associations between pixels and corresponding/referent objects and gathering those objects afterward. Topological analysis, such as the use of the Morse-Smale complex to encode regions of uniform gradient flow behavior, offers an alternative approach: first, create geometric priors, and then apply machine learning to classify. This approach is empirically motivated since phenomena of interest often appear as subsets of topological priors in many applications. Using topological elements not only reduces the learning space but also introduces the ability to use learnable geometries and connectivity to aid the classification of the segmentation target. Here, in this article, we describe an approach to creating learnable topological elements, explore the application of ML techniques to classification tasks in a number of areas, and demonstrate this approach as a viable alternative to pixel-level classification, with similar accuracy, improved execution time, and requiring marginal training data.

97 MATHEMATICS AND COMPUTING↗