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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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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Elephant range and population, strontium isotopes, and genetics combine to give local-scale specificity to ivory hotspot tracking

We use Sr isotopes to increase the precision of DNA-based origin estimates of wildlife products. Population information is used to develop Sr isotope Elephant Polygons that are overlaid onto the region of origin identified by DNA assignment to determine the sources of seized ivory samples. Our approach is cognizant of isotope mixing due to isotope turnover within animals and also of the large home range of elephants or other mobile species. Genetic information from 3 different law enforcement ivory seizures suggests a region of origin confined to Kenya and Tanzania in eastern Africa. We determine characteristic 87 Sr/ 86 Sr ratios for each of 25 different Elephant Polygons within this region using analyses of more the 600 known-origin reference samples. Using both the 87 Sr/ 86 Sr ratios of the seized ivory samples and elephant population estimates from individual Elephant Polygons we find that at least 75 % of the samples likely came from a single Elephant Polygon which includes the Tsavo National Parks in Kenya and the Mkomazi National Park in Tanzania. A few samples may have come from other regions, most likely from Tanzania. This study illustrates the value of combining genetics, isotope geochemistry, and population surveys in wildlife forensics studies.

Africa↗
DOE OSTI2025

High-field side scrape-off layer density profile measurements and implications for high-field side LHCD coupling in DIII-D

The high-field side (HFS) scrape-off layer (SOL) is an often under-diagnosed region of tokamak plasmas. Situated in a region with favorable curvature, the HFS SOL has minimal turbulence-induced radial transport compared to the low-field side. Using profile reflectometry, the HFS SOL density profile is measured with high temporal resolution for a wide range of DIII-D plasma discharges for the first time. The magnetic configuration, particularly the location of the secondary separatrix (SS) largely determines the HFS SOL density profile. Additionally, density perturbations induced by edge-localized modes (ELMs) are observed in the HFS SOL, and the impact of ELMs on the SOL density profile is characterized for a range of magnetic configurations. It is found that ELM-induced HFS SOL perturbations are generally localized to within the SS and ELM-induced changes to the HFS wall density are minimized in near double-null configurations. The characterization of the HFS SOL density profile on DIII-D is crucial for the effective coupling of the planned HFS lower hybrid current drive (LHCD) launcher. Experimental HFS SOL measurements are used as input to full-wave simulations of LHCD coupling. Furthermore, this work simulates the effect of the magnetic configuration on LHCD coupling. It also simulates LHCD coupling in ELMs and high q min discharges. During such discharges, the LHCD coupling is predicted to be resilient to ELM-induced SOL density perturbations.

high-field side↗
DOE OSTI2025

Loosely bound composite dark matter

We investigate loosely bound composite states made of dark matter, where the binding energy for constituent particles is considerably less than the constituent mass. We focus on models of nuclear and molecular dark matter, where constituents are separated by length scales larger than the inverse constituent mass, just like nuclei and atoms in the Standard Model. The cosmology, structure, and interactions at underground experiments are described. We find that loosely bound composites can have a very large cross section for scattering with nuclei that scales with nucleon number like ∼ A 4 . For some couplings, these composites produce extremely soft (≪ keV) individual atomic recoils while depositing a large amount of total recoil energy (≫ keV) in a single passage through a detector, implying an interesting new class of signatures for low threshold direct detection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Deep inference of simulated strong lenses in ground-based surveys

The large number of strong lenses discoverable in future astronomical surveys will likely enhance the value of strong gravitational lensing as a cosmic probe of dark energy and dark matter. However, leveraging the increased statistical power of such large samples will require further development of automated lens modeling techniques. We show that deep learning and simulation-based inference (SBI) methods produce informative and reliable estimates of parameter posteriors for strong lensing systems in ground-based surveys. We present the examination and comparison of two approaches to lens parameter estimation for strong galaxy-galaxy lenses — Neural Posterior Estimation (NPE) and Bayesian Neural Networks (BNNs). We perform inference on 1-, 5-, and 12-parameter lens models for ground-based imaging data that mimics the Dark Energy Survey (DES). We find that NPE outperforms BNNs, producing posterior distributions that are more accurate, precise, and well-calibrated for most parameters. For the 12-parameter NPE model, the calibration is consistently within <10% of optimal calibration for all parameters, while the BNN is rarely within 20% of optimal calibration for any of the parameters. Similarly, residuals for most of the parameters are smaller (by up to an order of magnitude) with the NPE model than the BNN model. This work takes important steps in the systematic comparison of methods for different levels of model complexity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Silicon pinhole strip defects and their impact on ATLAS Inner Tracker HV current measurement

In preparation for the High-Luminsity LHC (HL-LHC) [1], the ATLAS detector will undergo major detector upgrades, including the replacement of the current Inner Detector with the new all-silicon Inner Tracker (ITk) [2]. The ITk consists of a pixel detector close to the beamline surrounded by a large-area strip detector. During detector production, the electrical properties of silicon sensors and readout electronics must be characterized through a series of quality control (QC) and quality assurance tests. These tests ensure any defect is captured at the earliest possible stage. One such defect, callled a pinhole, occurs when the strip implant and the metal readout electrode are shorted through the intermediary dielectric layer. Notably, the introduction of pinholes during module assembly and pinhole effects on completed modules, especially on leakage current measurement circuitry, have never been studied. In this paper, we investigate the effect of such connections on the sensor leakage current measurements of completed modules and introduce new ways to locate pinholed strips. Here, with minor modifications to testing procedures, such defects are shown not to impede module testing or performance.

FOS: Physical sciences↗
DOE OSTI2025

Exploring the energy landscape of RBMs: reciprocal space insights into bosons, hierarchical learning and symmetry breaking

Deep generative models have become ubiquitous due to their ability to learn and sample from complex distributions. Despite the proliferation of various frameworks, the relationships among these models remain largely unexplored, a gap that hinders the development of a unified theory of AI learning. In this work, we address two central challenges: clarifying the connections between different deep generative models and deepening our understanding of their learning mechanisms. We focus on Restricted Boltzmann Machines (RBMs), a class of generative models known for their universal approximation capabilities for discrete distributions. By introducing a reciprocal space formulation for RBMs, we reveal a connection between these models, diffusion processes, and systems of coupled bosons. Our analysis shows that at initialization, the RBM operates at a saddle point, where the local curvature is determined by the singular values of the weight matrix, whose distribution follows the Marc̆enko-Pastur law and exhibits rotational symmetry. During training, this rotational symmetry is broken due to hierarchical learning, where different degrees of freedom progressively capture features at multiple levels of abstraction. This leads to a symmetry breaking in the energy landscape, reminiscent of Landau’s theory. This symmetry breaking in the energy landscape is characterized by the singular values and the weight matrix eigenvector matrix. We derive the corresponding free energy in a mean-field approximation. We show that in the limit of infinite size RBM, the reciprocal variables are Gaussian distributed. Our findings indicate that in this regime, there will be some modes for which the diffusion process will not converge to the Boltzmann distribution. To illustrate our results, we trained replicas of RBMs with different hidden layer sizes using the MNIST dataset. Our findings not only bridge the gap between disparate generative frameworks but also shed light on the fundamental processes underpinning learning in deep generative models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Resolving turbulent magnetohydrodynamics: a hybrid operator-diffusion framework

We present a hybrid machine learning framework that combines physics-informed neural operators (PINOs) with score-based generative diffusion models to simulate the full spatio-temporal evolution of two-dimensional, incompressible, resistive magnetohydrodynamic turbulence across a broad range of Reynolds numbers (Re). The framework leverages the equation-constrained generalization capabilities of PINOs to predict coherent, low-frequency dynamics, while a conditional diffusion model stochastically corrects high-frequency residuals, enabling accurate modeling of fully developed turbulence. Trained on a comprehensive ensemble of high-fidelity simulations with Re ϵ {100, 250, 500, 750, 1000, 3000, 10000}, the approach achieves state-of-the-art accuracy in regimes previously inaccessible to deterministic surrogates. At Re = 1000 and 3000, the model faithfully reconstructs the full spectral energy distributions of both velocity and magnetic fields late into the simulation, capturing non-Gaussian statistics, intermittent structures, and cross-field correlations with high fidelity. At extreme turbulence levels (Re = 10 000), it remains the first surrogate capable of recovering the high-wavenumber evolution of the magnetic field, preserving large-scale morphology and enabling statistically meaningful predictions.

Diffusion-Integrated Neural Operators↗
DOE OSTI2025

Generative diffusion model surrogates for mechanistic agent-based biological models

Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these models. CPMs become computationally expensive at large space- and time- scales making application and investigation of developed models difficult. Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems. However, the stochastic nature of these models means each set of parameters may give rise to different model configurations, complicating surrogate model development. In this work, we leverage denoising diffusion probabilistic models (DDPMs) to train a generative AI surrogate of a CPM used to investigate in vitro vasculogenesis. We describe the use of an image classifier to learn the characteristics that define unique areas of a 2-dimensional parameter space. We then apply this classifier to aid in surrogate model selection and verification. Our CPM model surrogate generates model configurations 20,000 timesteps ahead of a reference configuration and demonstrates approximately a 22x reduction in computational time as compared to native code execution. Our work represents a step towards the implementation of DDPMs to develop digital twins of stochastic biological systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

An atomic cluster expansion potential for twisted multilayer graphene

Twisted multilayer graphene, characterized by its moiré patterns arising from inter-layer rotational misalignment, serves as a rich platform for exploring quantum phenomena. Machine learning interatomic potentials (MLIPs) are a promising approach to model such systems. Our work develops a method to generate training and test datasets for fitting MLIPs that capture all possible misalignments but remain small-scale to facilitate efficient data generation and parameter estimation. To achieve this, we generate configurations with periodic boundary conditions suitable for density functional theory calculations, and then introduce an internal twist and shift within those supercell structures. Using this technique, supplemented with an active learning workflow, we fit an Atomic Cluster Expansion potential for simulating twisted multilayer graphene and test it for accuracy and robustness on a range of simulation tasks.

2D materials↗
DOE OSTI2024

MIBiG 4.0: advancing biosynthetic gene cluster curation through global collaboration

Specialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015. Since its conception, MIBiG has been regularly updated to expand data coverage and remain up to date with innovations in natural product research. Here, we describe MIBiG version 4.0, an extensive update to the data repository and the underlying data standard. In a massive community annotation effort, 267 contributors performed 8304 edits, creating 557 new entries and modifying 590 existing entries, resulting in a new total of 3059 curated entries in MIBiG. Particular attention was paid to ensuring high data quality, with automated data validation using a newly developed custom submission portal prototype, paired with a novel peer-reviewing model. MIBiG 4.0 also takes steps towards a rolling release model and a broader involvement of the scientific community. MIBiG 4.0 is accessible online at https://mibig.secondarymetabolites.org/.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Discovery of 98 Sn Produced by the Projectile Fragmentation of a 345-MeV/Nucleon 124 Xe Beam

We present the discovery of a new isotope of $^{98}$Sn beyond the double-magic N = Z = 50 nucleus $^{100}$Sn. $^{98}$Sn was identified among the projectile-fragmentation products of a $^{124}$Xe beam at 345 MeV/nucleon at the RI Beam Factory, RIKEN, Japan. Additionally, we have confirmed the production of $^{96}$In and $^{94}$Cd, previously reported as new isotopes at RIKEN. These highly proton-rich nuclei were separated and identified using the large-acceptance two-stage separator BigRIPS. Furthermore, we have determined the cross sections of $^{98}$Sn and its neighboring nuclei. These experimental values were compared to the semi-empirical cross-section formula EPAX3.1a, resulting in a reduction of the predicted cross sections by roughly a factor of 5.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2025

Including frameworks of public health ethics in computational modelling of infectious disease interventions

Decisions on public health interventions to control infectious diseases are often informed by computational models. Interpreting the predicted outcomes of a public health decision requires not only high-quality modelling but also an ethical framework for assessing the benefits and harms associated with different options. The design and specification of ethical frameworks matured independently of computational modelling, so many values recognized as important for ethical decision-making are missing from computational models. We demonstrate a proof-of-concept approach to incorporate multiple public health values into the evaluation of a simple computational model for vaccination against a pathogen such as SARS-CoV-2. By examining a bounded space of alternative prioritizations of three values relevant to public health ethics (aggregate clinical burden, equity in clinical burden, equity in adverse effects from vaccination), we identify value trade-offs, where the outcomes of optimal strategies differ depending on the ethical framework. This work demonstrates an approach to incorporating diverse values into decision criteria used to evaluate outcomes of models of infectious disease interventions.

"Mathematical Biology"↗
DOE OSTI2025

Constraining nuclear mass models using 𝑟-process observables with multiobjective optimization

Modeling nuclear masses, particularly for nuclei far from stability, remains a key objective in nuclear physics. One contemporary approach is machine learning (ML), which trains on experimental data, but can suffer large errors when extrapolating toward neutron-rich species. In nature, such masses shape observables for the rapid neutron capture process (𝑟 process), which in principle could inform ML models. Here, we introduce a multiobjective optimization approach using the Pareto front algorithm. We show that this technique, capable of identifying models that generate 𝑟-process abundances aligning with both solar and stellar data, is a promising method to select ML models with reliable extrapolation power.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Modular-Invariant Random Matrix Theory and AdS 3 Wormholes

We develop a nonperturbative definition of RMT 2 : a generalization of random matrix theory that is compatible with the symmetries of two-dimensional conformal field theory. Given any random matrix ensemble, its 𝑛-point spectral correlations admit a prescribed modular-invariant lift to RMT 2 , which moreover reduce to the original random matrix correlators in a near-extremal limit. Central to the prescription is a presentation of random matrix theory in Mellin space, which lifts to two dimensions via the SL⁡(2,ℤ) spectral decomposition employed in previous work. As a demonstration we perform the explicit RMT 2 lift of two-point correlations of the GUE Airy model. We propose that in AdS 3 pure gravity, semiclassical amplitudes for off-shell 𝑛-boundary torus wormholes with topology Σ 0,𝑛 × 𝑆 1 are given by the RMT 2 lift of JT gravity wormhole amplitudes. For the three-boundary case, we identify a gravity calculation which matches the RMT 2 result.

conformal field theory↗
DOE OSTI2025

Unconventional Superconductivity Mediated by Exciton Density Wave Fluctuations

Synthetic platforms afford an unparalleled degree of controllability in realizing strongly correlated phases of matter. In this Letter, we study the possibility of electrically tunable exciton-mediated superconductivity arising in charge-imbalanced bilayer semiconductors. Focusing on the case of a bilayer semiconductor heterostructure, we identify the gating conditions required to achieve exciton density wave order within a self-consistent Hartree-Fock approximation. We analyze the role of the coupling of excitonic fluctuations to the fermionic charge carriers to find that the Goldstone mode of the density wave order can mediate attractive interactions leading to superconductivity. Furthermore, when the system is close to the density wave ordering, the interactions mediated by low-energy exciton modes can support an interlayer pair-density wave superconductor of anisotropic character. We discuss experimental signatures associated with these phenomena.

excitons↗
DOE OSTI2025

Measurements of β -delayed proton emitters in the region of 100 Sn

An experiment on neutron-deficient nuclei in the region of doubly magic 100 Sn was carried out at the Radioactive Ion Beam Factory (RIBF) at the RIKEN Nishina Center in Japan. 𝑁 ≈ 𝑍 ≈ 50 nuclei were produced following fragmentation of an intense primary 124 Xe beam and implanted in the Advanced Implantation Detector Array silicon setup for the first study of proton-rich nuclei with this system at RIKEN. Measurements of the 𝛽-delayed proton emitter decay half-lives, branching ratios and proton energy spectra of nuclei in the region of 100 Sn were performed, with improved precision in many cases. New results for the energy spectrum of 101 Sn are presented, where a 2.03(5)-MeV peak was observed. In conclusion, these results are compared to shell-model calculations, using the recent precise measurement of the 101 In proton separation energy are presented.

Appleton, C. J. [Univ. of Edinburgh, Scotland (Uni↗
DOE OSTI2026

High-spin states in 60 Cu and implications for states in the mirror nucleus 60 Ga

High-spin states in 60 Cu are investigated on the basis of reaction-channel-selected 𝛾-ray spectroscopy. Data stem from three experiments that used the same fusion-evaporation reaction at similar beam energies. The Gammasphere 𝛾-ray spectrometer was combined with the Microball CsI(Tl) detector array and sets of liquid-scintillator neutron detectors which allowed us to identify and tag on evaporated light charged particles and neutrons, respectively. A level scheme is proposed which comprises more than 300 𝛾-ray transitions connecting more than 100 excited states. Predictions from cranked Nilsson-Strutinsky calculations describe the observed high-spin collective structures in 60 Cu. Predictions of shell-model calculations are probed with a suite of low- to medium-spin states of both parities. Taking into account isospin-symmetry-breaking terms in these calculations, a case study of the yrast line of the weakly bound mirror nucleus 60 Ga is presented.

Rudolph, Dirk [Lund University (Sweden)] (ORCID:00↗
DOE OSTI2025

Binding energies, charge radii, spins, and moments: Odd-odd Ag isotopes and discovery of a new isomer

We report on the masses and hyperfine structure of ground and isomeric states in 114,116,118,120 Ag isotopes, measured with the phase-imaging ion-cyclotron-resonance technique (PI-ICR) with the JYFLTRAP mass spectrometer and the collinear laser spectroscopy beamline at the Ion Guide Isotope Separator On-Line facility, Jyväskylä, Finland. We measured the masses and excitation energies, electromagnetic moments, and charge radii, and firmly established the nuclear spins of the long-lived states. A new isomer was discovered in 118 Ag and the half-lives of 118 Ag long-lived states were reevaluated. We unambiguously pinned down the level ordering of all long-lived states, placing the inversion of the 𝐼 = 0 − and 𝐼 = 4 + states at 𝐴 = 118 (𝑁 = 71). As a result, we compared the electromagnetic moments of each state to empirical single-particle moments to identify the dominant configuration where possible.

90 ≤ A ≤ 149↗