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DOE OSTI2026

Dense autoencoders, clustering techniques, and semi-supervised learning for HPGe $γ$-spectra

Classifying high-resolution gamma spectra by their isotopic content is an essential task in nuclear forensics and other applications. Traditional analysis methods are often time-intensive, but machine learning (ML) may help analysts quickly process many spectra. Such methods tend to rely on abundant, well-labeled data for training. Historical gamma data exists in various fields but is not uniformly useful for supervised ML due to inconsistent labeling. Here, to address some of these challenges, we present a method to classify and organize unlabeled data from high-purity germanium detectors using an autoencoding neural network (autoencoder). We trained dense autoencoders to compress gamma data into latent representations that enable efficient data characterization. By clustering the encoded spectra or lower-dimensional mappings of them, we identified and removed portions of over-abundant data categories, resulting in a more balanced dataset and improved autoencoder performance. This encoding and clustering pipeline also enabled the organization of spectra into self-consistent categories. Finally, we found that encoded representations showed potential as inputs for semi-supervised learning of nuclide identification (NID) labels, achieving an average F1 score of 0.85 ± 0.03 when mapping encodings to a set of 65 isotope labels.

Autoencoders↗

DOE OSTI2023

Direct prediction of inelastic neutron scattering spectra from the crystal structure*

Abstract Inelastic neutron scattering (INS) is a powerful technique to study vibrational dynamics of materials with several unique advantages. However, analysis and interpretation of INS spectra often require advanced modeling that needs specialized computing resources and relevant expertise. This difficulty is compounded by the limited experimental resources available to perform INS measurements. In this work, we develop a machine-learning based predictive framework which is capable of directly predicting both one-dimensional INS spectra and two-dimensional INS spectra with additional momentum resolution. By integrating symmetry-aware neural networks with autoencoders, and using a large scale synthetic INS database, high-dimensional spectral data are compressed into a latent-space representation, and a high-quality spectra prediction is achieved by using only atomic coordinates as input. Our work offers an efficient approach to predict complex multi-dimensional neutron spectra directly from simple input; it allows for improved efficiency in using the limited INS measurement resources, and sheds light on building structure-property relationships in a variety of on-the-fly experimental data analysis scenarios.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

A mesoscale 3D model of irradiated concrete informed via a 2.5 U-Net semantic segmentation

The concrete biological shield in light-water reactors is exposed to neutron and gamma irradiation, which deteriorates the concrete’s mechanical properties in the long term. To assess the irradiation-induced damage, predictive mechanical models are developed and used in parallel with the characterization of irradiated concrete samples. Realistic 3D simulation domains can drastically improve a model’s prediction. In this work, we utilized x-ray computed tomography (XCT) data of a concrete specimen to reconstruct its 3D microstructure. The XCT data shows low contrast between the concrete’s aggregates and cement paste, resulting in poor image segmentation when using traditional unsupervised techniques. To address this issue, we developed and trained a 2.5D U-Net model on only 24 pre-labeled XCT layers to segment 651 layers of the XCT data. The overall F1-score of the model is approximately 96%. Then, we created a 3D finite element (FE) mesh based on the stack of segmented images. The FE model contains radiation-induced expansion, damage, and creep. The constitutive equations are adapted to each phase (aggregates and cement paste). Here, we simulated the effects of neutron irradiation in the concrete specimen as well as the specimen’s mechanical response to uniaxial compression. Finally, model validation was performed using experimental data on similar concrete specimens in the literature.

2.5D U-Net↗

DOE OSTI2020

Real-time damage characterization for GFRCs using high-speed synchrotron X-ray phase contrast imaging

Here, we report the application of high-speed synchrotron X-ray phase contrast imaging (PCI) in real-time damage characterization for glass fiber reinforced composites (GFRCs) subjected to dynamic loading. Dynamic single-edge notched bending (DSENB) experiments on pre-notched S-2 GFRCs were performed on a modified Kolsky compression bar. During loading, the synchrotron X-ray beam penetrated through the composite specimen from the side to detect damage evolution inside the material. Entire dynamic events were recorded by a high-speed camera as image sequences.0° and 90° unidirectional and cross-ply composites were investigated. An optical imaging technique was also employed to capture similar dynamic events in comparison with the radiographic imaging. It is demonstrated that high-speed X-ray PCI had sufficient phase contrast to characterize a crack initiation at a 20-μm spatial resolution within 920 ns and track the crack geometry during propagation, thereby providing reliable data to quantify the dynamic damage resistance of GFRCs. Furthermore, being capable of recognizing microscopic damage-related features at a sub-10-μm resolution, high-speed X-ray PCI provided fundamental material failure mechanisms to reveal the essential of macroscale structural failure of composites. It can also track the damage evolution inside and between individual plies of laminated composites. However, current high-speed X-ray PCI technique only supports in-situ observation and the high timing and spatial resolutions are limited within a field of view of ~2.5 mm in square, preventing its application in the three-dimensional and larger-area damage detection for GFRC structures.

42 ENGINEERING↗

DOE OSTI2023

A co-kurtosis PCA based dimensionality reduction with nonlinear reconstruction using neural networks

For turbulent reacting flow systems, identification of low-dimensional representations of the thermo-chemical state space is vitally important, primarily to significantly reduce the computational cost of device-scale simulations. Principal component analysis (PCA), and its variants, are a widely employed class of methods. Recently, an alternative technique that focuses on higher-order statistical interactions, co-kurtosis PCA (CoK-PCA), has been shown to effectively provide a low-dimensional representation by capturing the stiff chemical dynamics associated with spatiotemporally localized reaction zones. While its effectiveness has only been demonstrated based on a priori analyses with linear reconstruction, in this work, we employ nonlinear techniques to reconstruct the full thermo-chemical state and evaluate the efficacy of CoK-PCA compared to PCA. Specifically, we combine a CoK-PCA-/PCA-based dimensionality reduction (encoding) with an artificial neural network (ANN) based reconstruction (decoding) and examine, a priori, the reconstruction errors of the thermo-chemical state. In addition, we evaluate the errors in species production rates and heat release rates, which are nonlinear functions of the reconstructed state, as a measure of the overall accuracy of the dimensionality reduction technique. We employ four datasets to assess CoK-PCA/PCA coupled with ANN-based reconstruction: zero-dimensional (homogeneous) reactor for autoignition of an ethylene/air mixture that has conventional single-stage ignition kinetics, a dimethyl ether (DME)/air mixture which has two-stage (low and high temperature) ignition kinetics, a one-dimensional freely propagating premixed ethylene/air laminar flame, and a two-dimensional dataset representing turbulent autoignition of ethanol in a homogeneous charge compression ignition (HCCI) engine. Finally, results from the analyses demonstrate the robustness of the CoK-PCA based low-dimensional manifold with ANN reconstruction in accurately capturing the data, specifically from the reaction zones.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Shock compression of liquid helium to 360 GPa

Data for the shock equation of state of helium are obtained up to 360⁢G⁡Pa, nearly doubling the pressure of previous experimental measurements. The helium samples are first precompressed to 2.7 g⁡c⁢m −3 in a diamond anvil cell prior to laser-driven shock compression at the Omega Laser Facility. Time-resolved Doppler velocimetry and pyrometry reveal significant reflectivity and greater compressibility compared to the predictions of existing broad-range tabular equation of state models, which may be caused by the onset of ionization. These experimental observations, however, are largely captured with molecular-dynamics simulations based on density functional theory, affirming the ability of first-principles techniques to capture complex physics, while enabling critical insight for the behavior of warm dense helium in Jovian interiors and white dwarf atmospheres.

Physics - Plasma physics↗

DOE OSTI2023

Crystal plasticity finite element simulation of lattice rotation and x-ray diffraction during laser shock compression of tantalum

Here we present a crystal plasticity model tailored for high-pressure, high-strain-rate conditions that uses a multiscale treatment of dislocation-based slip kinetics. We use this model to analyze the pronounced plasticity-induced lattice rotations observed in shock-compressed polycrystalline tantalum via in situ x-ray diffraction. By making direct comparisons between experimentally measured and simulated texture evolution, we can explain how the details of the underlying slip kinetics control the degree of lattice rotation that ensues. Specifically, we show that only the highly nonlinear kinetics caused by dislocation nucleation can explain the magnitude of the rotation observed under shock compression. We demonstrate a good fit between our crystal plasticity model and x-ray diffraction data and exploit the data to quantify the dislocation nucleation rates that are otherwise poorly constrained by experiment in the dynamic compression regime.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Enhanced detection of paramagnetic fluorine-19 magnetic resonance imaging agents using zero echo time sequence and compressed sensing

Fluorine-19 ( 19 F) magnetic resonance imaging (MRI) is an emerging technique offering specific detection of labeled cells in vivo. Lengthy acquisition times and modest signal-to-noise ratio (SNR) makes three-dimensional spin-density–weighted 19 F imaging challenging. Recent advances in tracer paramagnetic metallo-perfluorocarbon (MPFC) nanoemulsion probes have shown multifold SNR improvements due to an accelerated 19 F T 1 relaxation rate and a commensurate gain in imaging speed and averages. However, 19 F T 2 -reduction and increased linewidth limit the amount of metal additive in MPFC probes, thus constraining the ultimate SNR. To overcome these barriers, we describe a compressed sampling (CS) scheme, implemented using a “zero” echo time (ZTE) sequence, with data reconstructed via a sparsity-promoting algorithm. Our CS-ZTE scheme acquires k-space data using an undersampled spherical radial pattern and signal averaging. Image reconstruction employs off-the-shelf sparse solvers to solve a joint total variation and -norm regularized least square problem. To evaluate CS-ZTE, we performed simulations and acquired 19 F MRI data at 11.7 T in phantoms and mice receiving MPFC-labeled dendritic cells. For MPFC-labeled cells in vivo, we show SNR gains of ~6.3 × with 8-fold undersampling. We show that this enhancement is due to three mechanisms including undersampling and commensurate increase in signal averaging in a fixed scan time, denoising attributes from the CS algorithm, and paramagnetic reduction of T 1 . Importantly, 19 F image intensity analyses yield accurate estimates of absolute quantification of 19 F spins. Overall, the CS-ZTE method using MPFC probes achieves ultrafast imaging, a substantial boost in detection sensitivity, accurate 19 F spin quantification, and minimal image artifacts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Development of low temperature fuel cell holders for Operando x-ray micro and nano computed tomography to visualize water distribution

Abstract Synchrotron x-ray imaging techniques, like x-ray computed tomography (CT) and radiography have proven instrumental in expanding the communities knowledge of complex transport and reaction kinetics in electrochemical devices such as fuel cells and electrolyzers. This work presents the development of novel x-ray CT imaging techniques for operando visualization of water within low temperature fuel cells at spatial resolutions spanning the micro and nano scales. The design of operando sample holders, for both micro x-ray CT and nano CT experiments is described in depth, and prototypes of these sample holders were evaluated across a set of requirements, the most important of which are x-ray transmissibility, electrical conductivity and mechanical stability. Water segmentation from micro x-ray CT data was enabled by an image subtraction method, where the image without water is subtracted from the one with water. Through iterative experimentations, the operando nano CT cell was developed to optimize mechanical compression, electric conductivity and gas flow. While three-dimensional fuel cell reconstructions were shown possible, there remain challenges to overcome at typical lower energies (8 keV) due to beam damage, whereas it is not as significant for higher energies (>17.5 keV).

Kulkarni, Devashish↗

DOE OSTI2025

Understanding and Estimating Error Propagation in Neural Networks for Scientific Data Analysis

Neural networks are increasingly integrated into scientific discovery, where input data reduction and model quantization play a key role in accelerating inference. However, understanding and mitigating the impact of these techniques on output error is critical for ensuring reliable results, particularly in tasks demanding high numerical precision. This paper introduces a comprehensive framework for optimizing neural network inference in scientific computing by combining data reduction and weight quantization while maintaining error-controlled outcomes. We develop theoretical analyses to bound error propagation under these reductions and propose a framework that balances computational performance with error constraints. Evaluation on real-world learning-based combustion simulations and satellite image classification demonstrates that our derived error bounds accurately predict observed errors while enabling significant computational speedup under our framework. This work highlights the potential for further leveraging advancements in modern lossy compression algorithms and hardware accelerators that support lower-precision formats.

He, Weiming [New Jersey Institute of Technology]↗

DOE OSTI2022

PLA Renewable Bio Polymer Based Solid-State Gamma Radiation Detector-Dosimeter for Biomedical and Nuclear Industry Applications

Polylactic acid (PLA) as a “green,” renewable corn-soy based polymer resin was assessed as a novel solid-state detector for rapid-turnaround gamma radiation dosimetry in the 1–100 kGy range–of significant interest in biomedical and general nuclear industry applications. Co-60 was used as the source of gamma photons. It was found that PLA resin responds well in terms of rheology and porosity metrics with an absorbed gamma dose (Dg). In this work, rheological changes were ascertained via measuring the differential mass loss ratio (MLR) of irradiated PLA placed within PTFE-framed (40 mm × 20 mm × 0.77 mm) cavities bearing ~0.9 g of PLA resin and pressed for 12–16 min in a controlled force hot press under ~6.6 kN loading and platens heated to 227 °C for the low Dg range: 0–11 kGy; and to 193 °C for the extended Dg range: 11–120 kGy. MLR varied quadratically from 0.05 to ~0.2 (1σ ~ 0.007) in the 0–11 kGy experiments, and from 0.05 to ~0.5 (1σ ~0.01) in the 0–120 kGy experiments. Rheological changes from gamma irradiation were modeled and simultaneously correlated with void-pocket formations, which increase with Dg. A single PLA resin bead (~0.04 g) was compressed 5 min at 216 °C in 0–16 kGy experiments, and compressed 2 min at 232 °C in the 16–110 kGy experiments, to form sturdy ~100 µm thick wafers in the same press. Aggregate coupon porosity was then readily measurable with conventional optical microscope imaging and analyzed with standard image processing; this provided complementary data to MLR. Average porosity vs. dose varied quadratically from ~0 to ~15% in the 0–16 kGy range and from ~0 to ~18% over the 16–114 kGy range. These results provide evidence for utilizing “green”/renewable (under $0.01) PLA resin beads for rapid and accurate (+/-5–10%) gamma dosimetry over a wide 0–120 kGy range, using simple to deploy mass and void measuring techniques using common laboratory equipment.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2024

StOKeDMD: Streaming Occupation kernel dynamic mode decomposition

Dynamic mode decomposition (DMD) has become a common technique for constructing surrogate models for dynamical systems from observed system states. The Occupation Kernel DMD (OKDMD) method proposed in (Rosenfeld et al., 2022) and (Rosenfeld et al., 2024) is a Liouville operator based method that builds surrogate models from system state trajectories. Here, this paper proposes an extension of OKDMD to the case when the system states are observed in a streaming fashion, i.e., only a small fraction of the state trajectory is available at a given time. The developed method, Streaming Occupation Kernel DMD (StOKeDMD), accommodates the streaming data input by leveraging properties of specific choices of kernel functions and occupation kernels. We apply the StoKeDMD method as a compression method for streaming data, analyze the memory complexity, and demonstrate the performance of StoKeDMD in the compression of streaming data generated from a Lorenz system and a fluid flow simulation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Amorphization and Plasticity of Olivine During Low-Temperature Micropillar Deformation Experiments

Experimentally quantifying the viscoplastic rheology of olivine at the high stresses and low temperatures of the shallow lithosphere is challenging due to olivine's propensity to deform by brittle mechanisms at these conditions. In this study, we use microscale uniaxial compression tests to investigate the rheology of an olivine single crystal at room pressure and temperature. Pillars with nominal diameters of 1.25 μm were prepared using a focused ion beam milling technique and were subjected to sustained axial stresses of several gigapascal. The majority of the pillars failed after dwell times ranging from several seconds to a few hours. However, several pillars exhibited clear evidence of plastic deformation without failure after 4–8 hr under load. The corresponding creep strain rates are estimated to be on the order of 10-6 to 10 -7 s -1 . The uniaxial stresses required to achieve this deformation (4.1–4.4 GPa) are in excellent agreement with complementary data obtained using nanoindentation techniques. Scanning transmission electron microscopy observations indicate that deformation occurred along amorphous shear bands within the deformed pillars. Electron energy loss spectroscopy measurements revealed that the bands are enriched in Fe and depleted in Mg. We propose that inhomogeneities in the cation distribution in olivine concentrate stress and promote the amorphization of the Fe-rich regions. Finally, the time dependence of catastrophic failure events suggests that the amorphous bands must grow to some critical length scale to generate an unstable defect, such as a shear crack.

58 GEOSCIENCES↗

DOE OSTI2020

Report on local data recovery approaches suitable for weather and climate prediction (Deliverable 1.3) (V.1.0)

Numerical weather and climate prediction rates as one of the scientific applications whose accuracy improvements greatly depend on the growth of the available computing power. As the number of cores in top computing facilities pushes into the millions, increasing average frequency of hardware and software failures forces users to review their algorithms and systems in order to protect simulations from breakdown. This report surveys approaches for fault-tolerance in numerical algorithms and system resilience in parallel simulations from the perspective of numerical weather and climate prediction systems. A selection of existing strategies is analyzed, featuring interpolation-restart and compressed checkpointing for the numerics, in-memory checkpointing, user-level failure mitigation-based and backup-based methods for the systems. Numerical examples showcase the performance of the techniques in addressing faults, with particular emphasis on iterative solvers for linear systems, a staple of atmospheric fluid flow solvers. The potential impact of these strategies is discussed in relation to current development of numerical weather prediction algorithms and systems towards the exascale. Trade-offs between performance, efficiency and effectiveness of resiliency strategies are analyzed and some recommendations outlined for future developments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Equation of State Measurements on Iron Near the Melting Curve at Planetary Core Conditions by Shock and Ramp Compressions

The outer core of the Earth is composed primarily of liquid iron, and the inner core boundary is governed by the intersection of the melt line and the geotherm. While there are many studies on the thermodynamic equation of state for solid iron, the equation of state of liquid iron is relatively unexplored. In this work, we use dynamic compression to diagnose the high-pressure liquid equation of state of iron by utilizing the shock-ramp capability at Sandia National Laboratories’ Z-Machine. This technique enables measurements of material states off the Hugoniot by initially shocking samples and subsequently driving a further, shockless compression. Planetary studies benefit greatly from isentropic, off-Hugoniot experiments since they can cover pressure-temperature (P-T) conditions that are close to adiabatic profiles found in planetary interiors. We used this method to drive iron to P-T conditions similar to those of the Earth’s outer-inner core boundary, along an elevated-temperature isentrope in the liquid from 275 GPa to 400 GPa. We derive the equation of state using a hybrid backward integration – forward Lagrangian technique on particle velocity traces to determine the pressure-density history of the sample. Our results are in excellent agreement with SESAME 92141, a previously published equation of state table. With our data and previous experimental data on liquid iron we provide new information on the iron melting line and derive new parameters for a Vinet-based equation of state. The table and our parameterized equation of state are applied to provide an updated means of modeling the pressure, mass, and density of liquid iron cores in exoplanetary interiors.

58 GEOSCIENCES↗

DOE OSTI2020

Evidence of shock-compressed stishovite above 300 GPa

SiO 2 is one of the most fundamental constituents in planetary bodies, being an essential building block of major mineral phases in the crust and mantle of terrestrial planets (1–10 M E ). Silica at depths greater than 300 km may be present in the form of the rutile-type, high pressure polymorph stishovite (P4 2 /mnm) and its thermodynamic stability is of great interest for understanding the seismic and dynamic structure of planetary interiors. Previous studies on stishovite via static and dynamic (shock) compression techniques are contradictory and the observed differences in the lattice-level response is still not clearly understood. Here, laser-induced shock compression experiments at the LCLS- and SACLA XFEL light-sources elucidate the high-pressure behavior of stishovite on the lattice-level under in situ conditions on the Hugoniot to pressures above 300 GPa. We find stishovite is still (meta-)stable at these conditions, and does not undergo any phase transitions. This contradicts static experiments showing structural transformations to the CaCl 2 , α-PbO 2 and pyrite-type structures. However, rate-limited kinetic hindrance may explain our observations. These results are important to our understanding into the validity of EOS data from nanosecond experiments for geophysical applications.

58 GEOSCIENCES↗

DOE OSTI2020

Biaxial steel plated concrete constitutive models for composite structures: Implementation and validation

The Steel-plated Concrete (SC) technique is an alternative construction technique with faster onsite construction speed, reduced construction time, and increased structural performance. Aiming to predict the force transfer mechanism of SC elements, an innovative constitutive model package is proposed by implementing experimental-based biaxial steel plate concrete models into the nonlinear finite element (FE) model “Membrane Model of SC (MM-CS).” First, the formulation and implementation of the MM-SC is illustrated in detail, including the equilibrium and compatibility equations and the implementation of constitutive models. Next, various experimental data of SC members are selected and simulated using the proposed MM-SC model. In this research, two types of structures, SC panels and framed SC shear walls, and three types of loading conditions, including uniaxial compression, pure shear, and combined axial-flexural-shear tests, are analyzed respectively. Good agreements were obtained between the reported results and the FE simulation results in terms of yield capacity and ultimate capacity, proving the reliability of the implemented analytical constitutive material models and the biaxial membrane model formulations.

42 ENGINEERING↗

DOE OSTI2026

Dark Energy Survey Year 3 results: Simulation-based 𝑤CDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design

Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. Here, this work presents the first simulation-based inference (SBI) pipeline that combines weak lensing and galaxy clustering maps in a realistic Dark Energy Survey Year 3 (DES Y3) configuration and serves as preparation for a forthcoming analysis of the survey data. We develop a scalable forward model based on the CosmoGridV1 suite of N-body simulations to generate over one million self-consistent mock realizations of DES Y3 at the map level. Leveraging this large dataset, we train deep graph convolutional neural networks on the full survey footprint in spherical geometry to learn low-dimensional features that approximately maximize mutual information with target parameters. These learned compressions enable neural density estimation of the implicit likelihood via normalizing flows in a ten-dimensional parameter space spanning cosmological 𝑤CDM, intrinsic alignment, and linear galaxy bias parameters, while marginalizing over baryonic, photometric redshift, and shear bias nuisances. To ensure robustness, we extensively validate our inference pipeline using synthetic observations derived from both systematic contaminations in our forward model and independent Buzzard galaxy catalogs. Our forecasts yield significant improvements in cosmological parameter constraints, achieving 2−3× higher figures of merit in the 𝛺 𝑚 − 𝑆 8 plane relative to our implementation of baseline two-point statistics and effectively breaking parameter degeneracies through probe combination. These results demonstrate the potential of SBI analyses powered by deep learning for upcoming Stage-IV wide-field imaging surveys.

Thomsen, A. [Zurich, ETH] (ORCID:0000000203099021)↗