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DOE OSTI2026

Artificial Intelligence in Nuclear Safeguards; Evaluating Safeguards and Security Risks and Benefits for Advanced and Small Modular Reactor Deployments

Rapidly growing interest in advanced and small modular reactor (A/SMR) technologies presents challenges as well as opportunities for implementing international safeguards and security. A/SMR deployments are expected to be more numerous, more geographically dispersed, and more varied in their designs, placing new demands on the data systems and analytical tools used to support oversight (Alberti et al., 2023; Canadian Nuclear Safety Commission et al., 2024). Because of this variability, the importance and reliance on data systems for A/SMR deployments is expected to be higher than for previous reactor generations. Artificial Intelligence and Machine Learning (AI/ML) offer potential capabilities to address the high variability inherent in A/SMR technology. The beneficiaries of AI-assisted tools include facility operators, government regulators, IAEA inspectors, and A/SMR vendors. This report analyzes how AI/ML-assisted technologies can strengthen the implementation of IAEA safeguards and security measures. It also identifies AI-assisted tools to strengthen operator, facility, and regulator knowledge management practices and examines the potential risks AI/ML-based tools may introduce to IAEA safeguards and security efforts. It concludes with a set of hypothetical, standards-style requirements for AI/ML systems used in safeguards contexts, grounded in an inspector-centric view of system verification. Despite the potential benefits of AI/ML systems, understanding potential intentional and unintentional failure modes is critical for ensuring adequate protection of nuclear materials and facilities. Unique features of A/SMRs including sealed cores, remote and novel paradigms of operation, off-site reactor fabrication, novel fuel forms, and varied refueling requirements, introduce challenges for traditional safeguards technological approaches (Pensado et al., 2024; Federation of American Scientists, 2025). AI/ML systems deployed to address these challenges may introduce new risks requiring systematic evaluation rooted in both AI-specific risk frameworks, such as the NIST AI Risk Management Framework (NIST AI RMF), and established cyber risk management standards such as NIST SP 800-30 (National Institute of Standards and Technology [NIST], 2023; NIST, 2012).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Exploring for Superhot Geothermal Targets in Magmatic Settings: 2022 Field Campaign at Newberry Volcano

This paper presents preliminary results from a subset of work carried out as part of a multinational research project entitled DErisking Exploration for multiple geothermal Plays in magmatic ENvironments (DEEPEN), supported by the U.S. Department of Energy (DOE) and Geothermica, a joint effort by EU member states and associated countries. The DEEPEN project will develop a customized approach to exploration for supercritical and superhot geothermal plays in magmatic systems, which will be applied to two demonstration sites. This paper summarizes field activities carried out at the U.S. demonstration site, Newberry Volcano in central Oregon. The objective of this work effort is to refine the subsurface model of Newberry Volcano, with special focus on deeper zones including the magmatic plumbing system and other key geologic elements. New data collection included gravity and wideband magnetotelluric (MT) surveys, as well as reinstallation of a seismic network. The National Renewable Energy Laboratory (NREL) and Enthalpion Energy LLC (Enthalpion) worked with the Deschutes National Forest Fort Rock District to use a low ground disturbance method of MT deployment to collect MT data inside the caldera and other restricted areas inside the National Volcanic Monument. This opened these areas to geophysical exploration for the first time in decades. Sites along and adjacent to the south rim of the volcano constituted the primary survey objectives. A team from Lawrence Berkeley National Laboratory (LBNL), the U.S. Geological Survey (USGS), and AltaRock also began the process of reinstalling the seismic network from the AltaRock enhanced geothermal system (EGS) demonstration in anticipation of further development activities at the site. The data ingestion, reduction, and analysis phase of the project is ongoing. We are currently processing the MT and gravity data and are developing a new, highly GPU-accelerated, 3D joint MT and gravity inversion to better localize the south rim/south flank conductive target and better understand its relationship to deep heat, fluid sources, and surface extrusive features. Joint inversions, which have not yet been undertaken at Newberry, will allow us to obtain constraints on the geologic model that cannot be determined from each method in isolation, improving our ability to image key geologic features at depth.

geophysics↗

DOE OSTI2024

Quantum magnetism in the frustrated square lattice oxyhalides YbBi 2 IO 4 and YbBi 2 ClO 4

Square-lattice systems offer a direct route for realizing two-dimensional (2D) quantum magnetism with frustration induced by competing interactions. In this work, the square lattice materials YbBi 2 IO 4 and YbBi 2 ClO 4 were investigated using a combination of magnetization and specific-heat measurements on polycrystalline samples. Specific-heat measurements provide evidence for long-range magnetic order below $T$ N = 0.21 K (0.25 K) for YbBi 2 IO 4 (YbBi 2 ClO 4 ). On the other hand, a rather broad maximum is found in the temperature-dependent magnetic susceptibility, located at $T$ max = 0.33 K (0.38 K) in YbBi 2 IO 4 (YbBi 2 ClO 4 ), consistent with the quasi-2D magnetism expected for the large separation between the magnetic layers. Estimation of the magnetic entropy supports the expected Kramers' doublet ground state for Yb 3+ and the observed paramagnetic behavior is consistent with a well-isolated doublet. Roughly two-thirds of the entropy is consumed above $T$ N , due to a combination of the quasi-2D behavior and magnetic frustration. The impact of frustration is examined from the viewpoint of a simplified $J$ 1 -$J$ 2 square lattice model, which is frustrated for antiferromagnetic interactions. Specifically, a high-temperature series expansion analysis of the temperature-dependent specific-heat and magnetization data yields $J$ 2 /$J$ 1 = 0.30 (=0.23) for YbBi 2 IO 4 (YbBi 2 ClO 4 ). In conclusion, this simplified analysis suggests strong frustration that should promote significant quantum fluctuations in these compounds, and thus motivates future work on the static and dynamic magnetic properties of these materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Excited State Dynamics of a Conformationally Fluxional Copper Coordination Complex

The conversion of solar energy into chemical fuel represents a capstone goal of the 21st century and has the potential to supply terawatts of power in a globally distributed manner. However, the disparate time scales of photodriven charge separation (~fs) and steps in chemical reactions (~μs) represent an inherent bottleneck in solar-to-fuels technology. Here to address this discrepancy, we are developing earth-abundant coordination complexes that undergo light-induced conformational rearrangements such that charge separation (CS) is hastened, while charge recombination (CR) is slowed. To these ends, we report the preparation and characterization of a new series of conformationally fluxional copper coordination complexes that contain a twisted intramolecular charge transfer (TICT) fluorophore as part of their ligand scaffold. Structural and spectroscopic characterization of the Cu(I) and Cu(II) complexes formed with these ligands in their ground states establish oxidation state-dependent conformational dynamicity, while time-resolved emission and transient absorption spectroscopies define the photopysical parameters of photo-induced excited states. Building on initial reports with a related set of molecules, the improved ligand design presented here greatly simplifies the observed photophysics, effectively shutting down unwanted ligand-centered excited states previously observed. Finally, time-dependent density functional theory (TDDFT) analyses reveal an unusual metal-to-TICT electronic transition only reported once before, and though the formation of a CS state is not observed directly through experiments, TDDFT geometry optimizations in the excited states support the formation of transient Cu(II) CS species, lending credence to the potential success of our approach. These studies establish a clear model for the excited state dynamics at play in proof-of-concept systems and clarify key design parameters for future optimizations toward achieving long-lived CS via photoinduced conformational gating.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Permafrost Thaw, Uneven Subsidence and Projected Drying of Ice-wedge Polygon Tundra: Modeling Archive

This dataset is a model archive of the paper Permafrost Thaw, Uneven Subsidence and Projected Drying of Ice-wedge Polygon Tundra (in prep) to support a modeling study investigating how projected increases in Arctic temperature and precipitation will jointly influence hydrologic conditions in ice-rich tundra landscapes. With this dataset, this study is to address the research question: Will Arctic tundra landscapes become wetter or drier with increasing precipitation and temperature in the future when thaw-induced ground subsidence and associated microtopographic evolution are represented? The simulations focus on ice-wedge polygon tundra, a widespread form of ice-rich permafrost terrain that is highly sensitive to thaw-driven landscape change. This dataset contains model input and output data for four study watersheds in Alaska: Anaktuvuk, Utqiagvik (formerly Barrow), Brooks Foothills, and Prudhoe Bay. Simulations were performed using the Advanced Terrestrial Simulator (ATS, v1.5), a physics-rich integrated surface–subsurface hydrologic model. For each watershed, ten modeling cases were performed representing two landscape evolution conditions (with subsidence and without subsidence) combined with five climate forcing scenarios derived from Shared Socioeconomic Pathways (SSP5, SSP5 with precipitation trend, SSP2, SSP2 with precipitation trend, and SSP2 with double precipitation trend). Particularly, for each watershed under the forcing SSP2 with precipitation trend, there are two additional simulations considering spatially heterogeneous subsidence distributions: one assumes randomly distributed scaling and the other includes elevation dependent distribution scaling. These simulations span 1980–2099 and include spin-up runs (1980–2009) followed by transient projections (2010–2099). To facilitate reproducibility of simulations, all datasets are organized by watershed. For each study watershed, the dataset contains: (1) Pre-partitioned mesh files for 32-core modeling (.par.32.XX), located in EACH_WATERSHED/mesh/basin; and also a non-partitioned mesh file (.exo) located in EACH_WATERSHED/mesh; (2) Climate forcings corresponding to the five SSP scenarios (.h5), located in EACH_WATERSHED/data; (3) Final states (.h5) from column spin-up modeling used to initialize historical watershed-scale spin-up runs from 1980 to 2009, located in EACH_WATERSHED/PreSpinupHistorical; (4) Final states (.h5) of historical watershed-scale spin-up runs from 1980 to 2009 used to initialize projection runs, located in EACH_WATERSHED/Spinup_daymetERA5; (5) ATS modeling input files (.xml), located in EACH_WATERSHED/EACH_SIMULATION_SCENARIO/inputfiles; (6) ATS modeling output files (.dat), located in in EACH_WATERSHED/EACH_SIMULATION_SCENARIO/combined_obs; (7) For the Brooks Foothills watershed, additional spatial model outputs are provided (.h5) for selected years (2033 and 2093) used to generate spatial figures in this study, located in Brooksfoothills/EACH_SIMULATION_SCENARIO/results-WITH/WITHOUT_SUBSIDENCE-year2033/2093. All data files with suffix .h5 can be accessible through Python h5py, and all data files with suffix of .dat can be imported by Python pandas. Mesh file with .exo can be visualized through Paraview or read by Python netCDF. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

EARTH SCIENCE > ATMOSPHERE > PRECIPITATION↗

DOE OSTI2024

Chemical Design of Spin Frustration to Realize Topological Spin Glasses

Patterning spins to generate collective behavior is at the core of condensed matter physics. Physicists develop techniques, including the fabrication of magnetic nanostructures and precision layering of materials specifically to engender frustrated lattices. As chemists, we can access such exotic materials through targeted chemical synthesis and create new lattice types by chemical design. Here, in this study, we introduce a new approach to induce magnetic frustration on a modified honeycomb lattice through a competition of alternating antiferromagnetic (AFM) and ferromagnetic (FM) nearest-neighbor interactions. By subtly modulating these two types of interactions through facile synthetic modifications, we created two systems: (1) a topological spin glass and (2) a frustrated spin-canted magnet with low-temperature exchange bias. To design this unconventional magnetic lattice, we used a metal–organic framework (MOF) platform, Ni 3 (pymca) 3 X 3 (NipymcaX where pymca = pyrimidine-2-carboxylato and X = Cl, Br). We isolated two MOFs, NipymcaCl and NipymcaBr, featuring canted Ni 2+ -based moments. Despite this similarity, differences in the single-ion anisotropies of the Ni 2+ spins result in distinct magnetic properties for each material. NipymcaCl is a topological spin glass, while NipymcaBr is a rare frustrated magnet with low-temperature exchange bias. Density functional theory calculations and Monte Carlo simulations on the NipymcaX lattice support the presence of magnetic frustration as a result of alternating AFM and FM interactions. Our calculations enabled us to determine the ground-state spin configuration and the distribution of spin–spin correlations relative to paradigmatic kagomé and triangular lattices. This modified honeycomb lattice is similar to the electronic Kekulé-O phase in graphene and provides a highly tunable platform to realize unconventional spin physics.

Exchange bias↗

DOE OSTI2023

Machine learning predictions of near-surface permafrost extent at Teller 27, Teller 47, and the Kougarok 64 Hillslope sites on the Seward Peninsula, Alaska: Supporting Data

Geophysical surveys were conducted at the NGEE Arctic Teller mile marker 27 site, Teller mile marker 47 site, and Kougarok mile marker 64 site during the summers of 2018, 2019, and 2021. Additional data was collected at Teller mile marker 47 during September 2021 and August 2022. These surveys were used to identify locations of near-surface permafrost during the period of maximum seasonal thaw depth for ground truth data used in machine learning predictions of near-surface permafrost extent at each site. This dataset contains CSV files of ground truth observations of near-permafrost presence or absence for each site, where PF = 1 indicates permafrost presence and PF = 0 indicates permafrost absence. The dataset also includes 2 sets of binary rasters (WGS84 UTM zone 3) of permafrost extent for each site using 1) all of the training data and 2) the transferred model. For both sets of rasters, 0 = non-permafrost and 1 = permafrost. Included are 6 *.tif files and 5 *.csv files that include a data dictionary (dd.csv) and file-level metadata (flmd.csv). This dataset is in support of the paper "Machine learning-derived high-resolution maps of near-surface permafrost for three watersheds on the Seward Peninsula, Alaska" that is in review (May 2023). The Next-Generation Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Magnetic dilution in the triangular lattice antiferromagnet NaYb 1−𝑥 ⁢Lu 𝑥 ⁢O 2

The delafossitelike compound NaYbO 2 hosts a triangular lattice of Yb 3+ moments and is a promising candidate for the realization of a quantum spin liquid ground state—an exotic, quantum-disordered magnetic phase featuring long-range entanglement of spins. Tuning this system away from this quantum-disordered regime toward classical order or spin freezing is a powerful approach to shed light on the nature of the parent ground state. Here we leverage the substitution of nonmagnetic Lu 3+ onto the Yb 3+ sites to study the effects of magnetic disorder in NaYbO 2 using low-temperature ac susceptibility, heat capacity, and muon spin relaxation (𝜇⁢SR) measurements. Here, our 𝜇⁢SR measurements reveal resilient, correlated magnetic fluctuations that persist to at least 15% dilution, precluding conventional spin freezing and magnetic inhomogeneity. Heat capacity and magnetic susceptibility resolve a rapid suppression of the field-induced “up-up-down” magnetic order upon dilution and a crossover in the power-law behavior of the low-temperature magnetic excitations associated with the zero-field quantum disordered ground state. Taken together, these results support the notion of a robust network of entangled moments in NaYbO 2 and provide experimental validation of several models of a Heisenberg triangular lattice antiferromagnet in the presence of disorder.

Muon spin relaxation & rotation↗

DOE OSTI2025

127 Xe quantification method development and intercomparison exercise

Monitoring of the atmosphere for fission products ( 131m Xe, 133m Xe, 133 Xe, and 135 Xe) is performed by various laboratories to detect nuclear explosions. Quantification of 127 Xe is not routinely performed by laboratories measuring atmospheric radioxenon because it is not a fission product. 127 Xe was recently detected by a ground-based beta-gamma air monitoring system. When measured using beta-gamma coincidence detector systems, such as those in use on the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty (CTBT), 127 Xe can interfere with the quantification of fission product radioxenon due to overlap of the 127 Xe beta-gamma coincidence signatures with those of fission product radioxenon. Here, this work demonstrates quantification of 127 Xe at different laboratories with different measurement techniques. Production and purification of 127 Xe was performed by neutron activation of enriched 126 Xe. The purified 127 Xe was then split between laboratories, and detection and quantification methods were developed. At Idaho National Laboratory, a quantification method involving high purity germanium detectors was devised that included self-attenuation correction. At AWE, a beta-gamma coincidence counting method, as used in support of the IMS, was modified to enable the measurement and analysis of the 127 Xe samples. Corrections were made for self-attenuation, which showed a strong xenon volume dependency, for some coincidence signatures. The gas sample activity concentration was used as the comparison metric and it showed excellent agreement between the methods.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2022

Route Optimization for Energy Efficient Airport Shuttle Operations - A Case Study from Dallas Fort Worth International Airport

Air travel and requisite surface traffic supporting passenger arrival/departure constitutes a significant portion of travel and emissions in cities with large airports. An airport trip can segment into three parts namely: i) travel from a location in the city to the airport; ii) travel from a parking lot or rental car center to the terminal (i.e., within the airport premises), and iii) travel inside the terminal. Depending on the airport access mode all or a part of these legs comprise a traveler’s journey to the airport. The priority of airport ground transport management teams is to provide passengers with a seamless travel experience within the airport, so it is understandable that within airport shuttle routes might not be optimized for minimizing energy consumption. Solutions that meet the dual objective of reducing energy consumption from airport shuttle operations without compromising on passenger travel experience are key to improving system efficiency. There is currently a dearth of research and tools that can inform airports in making such decisions. Addressing this need, this research effort puts forth an optimization model that generates optimal shuttle routes for a given set of constraints, and a discrete-event simulator that evaluates the optimal solutions in a stochastic environment to understand the tradeoffs between passenger wait times, and within airport shuttle energy consumption. The proposed set of tools are tested in the context of optimizing airport shuttles routes within the Dallas Fort Worth International Airport (DFW). In addition to shuttle spatial positioning, and passenger demand information, high-fidelity vehicle data was collected using data loggers installed on DFW shuttles. Results show that 20% energy reduction in shuttle operations is possible with a modest two-minute increase in average passenger wait times. The tools developed in this research effort are designed to be generalizable and can help optimize shuttle operations planning at any major airport.

air travel↗

DOE OSTI2020

Long-Term Methane Emissions Rate Quantification and Alert System for Natural Gas Storage Wells and Fields

The Department of Energy (DOE) National Energy Technology Laboratory (NETL) and other critical stakeholders (e.g., U.S. Environmental Protection Agency (USEPA), state regulators, and industry) seek to more accurately characterize and quantify methane emissions from natural gas storage wells. These efforts will reduce uncertainties in estimates of these emissions in USEPA’s current Green House Gas Inventory (GHGI) under their Greenhouse Gas Reporting Program (GHGRP; EPA, 2016). Emission factors currently used to estimate methane emissions from gas storage wells are the same factors used for onshore natural gas production wells (USEPA, 2016). This study has developed a robust and representative dataset of emissions from disaggregated gas station wellhead components and related emission factors that will inform future enhancements to the GHGI and GHGRP. Extensive methane emissions measurements and meteorological data were collected from three active storage facilities representing two of the three types of storage facilities (i.e., depleted gas reservoir and salt cavern) to support development of emissions factors specifically for gas storage wells and associated components. Methane emissions were quantified from both above-ground components (e.g., pipe fittings and valves) and seepage from the ground surface in proximity to gas storage wellheads.

03 NATURAL GAS↗

DOE OSTI2023

LA100 Equity Strategies. Chapter 4: Lessons Learned and Options for Community Engagement in Los Angeles

The LA100 Equity Strategies project synthesizes community guidance with robust research, modeling, and analysis to identify strategy options that can increase equitable outcomes in Los Angeles' clean energy transition. Grounded in the analysis of past and ongoing energy inequities and engagement with underserved communities, the project presents community-guided strategies that aim to operationalize recognition and procedural justice. Building on the community-identified problems and solutions, and the analysis of the 11 strategies described in Chapter 3, this chapter continues to focus on the solution space through the lens of recognition and procedural justice. It centers the role of community engagement in energy utility planning and project development with a specific focus on how the Los Angeles Department of Water and Power (LADWP) can engage and work equitably with Los Angeles communities to cocreate a clean and just energy future for LA. LA100 Equity Strategies is rooted in the crucial role community engagement plays in restructuring the energy systems of cities, states, and nations. Scholarship on wind, solar, and other transitional energy technologies and projects has documented that such engagement is commonly used as a top-down mechanism for adapting social practices to fit new technological innovations. Yet, understanding how the clean energy transition - with related changes in technologies, infrastructures, practices, and costs - will fit equitably into the existing socio-political context is a challenge that requires substantive collaboration with local communities. Any form of community engagement opens up government officials and utilities to opposition from their public. Meaningful engagement methods turn such dissent into a strength, embracing critical feedback - particularly from communities historically excluded from decision-making - as contributing to more grounded design and effective implementation. Leveraging this collaborative model to further rectify past and ongoing inequities in the social, cultural, and institutional scaffolding of LA, this chapter presents options and methods to support LADWP in launching a just and equitable clean energy transition. We approach community engagement as a critical process linking recognition, procedural, and distributional justice, outlining how LADWP could learn from past engagement, coordinate such knowledge organization-wide, and use engagement as a key tool for achieving energy justice and equity.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

LA100 Equity Strategies. Chapter 5: Low-Income Energy Bill Equity and Affordability

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Valley-Coherent Quantum Anomalous Hall State in AB-Stacked MoTe 2 / W S e 2 Bilayers

Moiré materials provide fertile ground for the correlated and topological quantum phenomena. Among them, the quantum anomalous Hall (QAH) effect, in which the Hall resistance is quantized even under zero magnetic field, is a direct manifestation of the intrinsic topological properties of a material and an appealing attribute for low-power electronics applications. The QAH effect has been observed in both graphene and transition metal dichalcogenide (TMD) moiré materials. It is thought to arise from the interaction-driven valley polarization of the narrow moiré bands. Here, we show that the newly discovered QAH state in AB-stacked MoTe 2 / W S e 2 moiré bilayers is not valley polarized but valley coherent. The layer- and helicity-resolved optical spectroscopy measurement reveals that the QAH ground state possesses spontaneous spin (valley) polarization aligned (antialigned) in two TMD layers. In addition, saturation of the out-of-plane spin polarization in both layers occurs only under high magnetic fields, supporting a canted spin texture. Our results call for a new mechanism for the QAH effect and highlight the potential of TMD moiré materials with strong electronic correlations and spin-orbit interactions for exotic topological states. Published by the American Physical Society 2024

2-dimensional systems↗

DOE OSTI2021

Curvature-mediated spin textures in magnetic multi-layered nanotubes

The scientific and technological exploration of artificially designed three-dimensional magnetic nanostructures opens the path to exciting novel physical phenomena, originating from the increased complexity in spin textures, topology, and frustration in three dimensions. Theory predicts that the equilibrium magnetic ground state of two-dimensional systems which reflects the competition between symmetric (Heisenberg) and antisymmetric (Dzyaloshinskii-Moriya interaction (DMI)) exchange interaction is significantly modified on curved surfaces when the radius of local curvature becomes comparable to fundamental magnetic length scales. Here, we present an experimental study of the spin texture in an 8 nm thin magnetic multilayer with growth-induced in-plane anisotropy and DMI deposited onto the curved surface of a 1.8 {\mu}m long non-magnetic carbon nanowire with a 67 nm radius. Using magnetic soft x-ray tomography the three-dimensional spin configuration in this nanotube was retrieved with about 30nm spatial resolution. The transition between two vortex configurations on the two ends of the nanotube with opposite circulation occurs through a domain wall that is aligned at an inclined angle relative to the wire axis. Three-dimensional micromagnetic simulations support the experimental observations and represent a visualization of the curvature-mediated DMI. They also allow a quantitative estimate of the DMI value for the magnetic multilayered nanotube.

Josten, Elisabeth↗

DOE OSTI2025

An Open-source Llm Enhanced-tool Specialized In Helping Moose Related Problems And Tasks

MOOSEenger is an open-source, terminal-first chat application for the MOOSE ecosystem that couples specialized parsing of MOOSE documentation and “.i” input files with retrieval-augmented generation to deliver grounded answers about multiphysics modeling and workflows. It includes dedicated readers for MOOSE-style HTML and a pyhit-based parser that uses the MOOSE syntax tree to preserve block structure and attach retrieval metadata. A data-ingestion pipeline performs semantic chunking into atomic facts and stores them hierarchically in a local Chroma vector database that maintains parent–child relationships across documents; the system can ingest directories, individual files, and single-page web content, and it provides CRUD operations (insert, update, delete) to manage the corpus. At query time, relevant chunks are embedded, retrieved, and fused into the model context, with interactive features such as token streaming, persistent chat history, and dynamic RAG (retrieval triggered by user input or intermediate model output). Deployment is flexible: MOOSEenger runs with local Ollama models or remote Hugging Face/OpenAI backends—typically coordinating generation, lightweight tagging/summarization, and embeddings across three models—and it also supports a server mode and integration with the VS Code Continue interface.

Li, Mengnan [Idaho National Laboratory (INL), Idah↗

DOE OSTI2025

How Bright in Gravitational Waves are Millisecond Pulsars for the Galactic Center GeV Gamma-Ray Excess? A Systematic Study

The existence of dark matter (DM) is supported by various macroscopic observations, but its microscopic nature remains elusive. The Galactic Center GeV gamma-ray excess (GCE) has been a leading candidate signal for particle dark matter annihilation. However, an unresolved population of millisecond pulsars (MSPs) in the bulge provides the alternative explanation for the excess. Identifying these MSPs in electromagnetic bands is difficult due to source confusion, pulse broadening, and extinction. Gravitational waves (GWs) provide a complementary probe: a steadily rotating, non-axisymmetric MSP emits a nearly monochromatic GW signal in the sensitive band of ground-based detectors, with amplitude set by its ellipticity. In this work, we systematically investigate the GW emission from the MSP population proposed to explain the GCE and its detectability with current and future detectors. We consider three major scenarios for the origin of ellipticity and model the population properties of these MSPs. We also consider both isolated MSPs and MSPs in binary systems, as well as Doppler effects in the detection. We find that while the signal is below the reach of current interferometers, next-generation detectors such as the Einstein Telescope (ET) and Cosmic Explorer (CE) can detect a fraction of those MSPs, offering a novel test of the MSP interpretation of the GCE. Future directed searches toward the Galactic Center with continued improvements in sensitivities will either uncover this long-sought MSP population or place stringent limits on their ellipticities and abundance, with important implications for both the astrophysical and dark-matter interpretations of the GCE.

Lei, Ming-Yu [Purple Mountain Observ.; USTC, Hefei↗

DOE OSTI2024

Vehicle Position Detection Based on Machine Learning Algorithms in Dynamic Wireless Charging

Dynamic wireless charging (DWC) has emerged as a viable approach to mitigate range anxiety by ensuring continuous and uninterrupted charging for electric vehicles in motion. DWC systems rely on the length of the transmitter, which can be categorized into long-track transmitters and segmented coil arrays. The segmented coil array, favored for its heightened efficiency and reduced electromagnetic interference, stands out as the preferred option. However, in such DWC systems, the need arises to detect the vehicle’s position, specifically to activate the transmitter coils aligned with the receiver pad and de-energize uncoupled transmitter coils. This paper introduces various machine learning algorithms for precise vehicle position determination, accommodating diverse ground clearances of electric vehicles and various speeds. Through testing eight different machine learning algorithms and comparing the results, the random forest algorithm emerged as superior, displaying the lowest error in predicting the actual position.

47 OTHER INSTRUMENTATION↗