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DOE OSTI2025

Unraveling electronic correlations in warm dense quantum plasmas

The study of matter at extreme densities and temperatures has emerged as a highly active frontier at the interface of plasma physics, material science and quantum chemistry with relevance for planetary modeling and inertial confinement fusion. A particular feature of such warm dense matter is the complex interplay of Coulomb interactions, quantum effects, and thermal excitations, making its rigorous theoretical description challenging. Here, we demonstrate how ab initio path integral Monte Carlo simulations allow us to unravel this intricate interplay for the example of strongly compressed beryllium, focusing on two X-ray Thomson scattering data sets obtained at the National Ignition Facility. We find excellent agreement between simulation and experiment with a very high level of consistency between independent observations without the need for any empirical input parameters. Our results call into question previously used chemical models, with important implications for the interpretation of scattering experiments and radiation hydrodynamics simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Temperature and conductivity in shock compressed bridgmanite MgSiO 3 up to 2 TPa

The melting behavior and transport properties of MgSiO 3 at multi-megabar pressures remain poorly constrained despite their importance for high-pressure silicate physics. Here we report the first direct measurements of temperature and optical reflectivity in shock-compressed bridgmanite (MgSiO 3 ) using laser-driven decaying shock compression combined with velocimetry and optical pyrometry. Temperature and reflectivity data spanning approximately 4000–60 000 K were used to constrain the MgSiO 3 melting curve and to infer its electrical conductivity. We find that the MgSiO 3 melting curve becomes shallower than that of iron above 400 GPa, yielding lower melting temperatures in planetary mantles than predicted by several previous theoretical estimates. Across the solid-liquid transition, the inferred electrical conductivity increases significantly, reaching ∼2000 Ω cm −1 . These results provide experimental benchmarks for theoretical models of silicate melting and transport under extreme pressure-temperature conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Techno-Economic Analysis of Geologically Connected Seawater Air Conditioning (GeoSWAC) Concept for District Cooling at the University of Puerto Rico at Rio Piedras

At the University of Puerto Rico at Rio Piedras, a central chilled water plant supplies cooling to several campus buildings, contributing significantly to electricity demand during daytime peak hours, particularly in the summer months. These operational challenges are exacerbated by Puerto Rico's tropical rainforest climate and a power grid vulnerable to frequent disruptions caused by hurricanes and tropical storms. This study presents a techno-economic analysis of the existing chilled water plant serving four representative campus buildings and introduces a conceptual alternative: the Geologically connected Seawater Air Conditioning (GeoSWAC) system. GeoSWAC leverages stable low temperatures of deep ocean water (~1 km depth), hydraulically connected to an inland well, to deliver cooling without the use of vapor-compression refrigeration. Using modeled annual cooling loads and chiller performance data, capital costs, energy consumption, and levelized cost of cooling (LCOC) were evaluated for both systems. While GeoSWAC showed higher capital costs than the chiller-based scenario, operational costs were significantly lower at $26k-$53k annually, resulting in a lower LCOC between $2.3/MWh and $8.2/MWh compared to $30.2/MWh-$33.6/MWh for the chiller scenario. These results suggest that the GeoSWAC system offers a promising, low-energy, and climate-resilient alternative for large-scale cooling in tropical coastal environments, with significant potential to reduce peak electricity demand and improve long-term system reliability.

15 GEOTHERMAL ENERGY↗

DOE OSTI2026

Techno-Economic Analysis of Geologically Connected Seawater Air Conditioning (GeoSWAC) Concept for District Cooling at the University of Puerto Rico at Rio Piedras: Preprint

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Identifying Climate Patterns Using Clustering Autoencoder Techniques

Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Kurihana, Takuya↗

DOE OSTI2024

Machine learning visualization tool for exploring parameterized hydrodynamics

We are interested in the computational study of shock hydrodynamics, i.e. problems involving compressible solids, liquids, and gases that undergo large deformation. These problems are dynamic and nonlinear and can exhibit complex instabilities. Due to advances in high performance computing it is possible to parameterize a hydrodynamic problem and perform a computational study yielding $\mathscr{O} (TB)$ of simulation state data. We present an interactive machine learning tool that can be used to compress, browse, and interpolate these large simulation datasets. This tool allows computational scientists and researchers to quickly visualize 'what-if' situations, perform sensitivity analyses, and optimize complex hydrodynamic experiments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding

Interactive time-varying volume visualization is challenging due to its complex spatiotemporal features and sheer size of the dataset. Recent works transform the original discrete time-varying volumetric data into continuous Implicit Neural Representations (INR) to address the issues of compression, rendering, and super-resolution in both spatial and temporal domains. However, training the INR takes a long time to converge, especially when handling large-scale time-varying volumetric datasets. In this work, we proposed F-Hash, a novel feature-based multi-resolution Tesseract encoding architecture to greatly enhance the convergence speed compared with existing input encoding methods for modeling time-varying volumetric data. The proposed design incorporates multi-level collision-free hash functions that map dynamic 4D multi-resolution embedding grids without bucket waste, achieving high encoding capacity with compact encoding parameters. Our encoding method is agnostic to time-varying feature detection methods, making it a unified encoding solution for feature tracking and evolution visualization. Experiments show the F-Hash achieves state-of-the-art convergence speed in training various time-varying volumetric datasets for diverse features. We also proposed an adaptive ray marching algorithm to optimize the sample streaming for faster rendering of the time-varying neural representation.

deep learning↗

DOE OSTI2025

200-fold increase in dynamic strength of platinum at 430 GPa without a phase transformation

Here, the high-pressure strength of solid platinum is studied at the National Ignition Facility via Rayleigh-Taylor ripple growth under ramp compression. Laser-driven experiments reached pressures up to 430 GPa, collecting velocimetry and radiography data. Despite no phase transformation, the observed ripple growth aligns with hydrodynamic simulations using an amplified Steinberg-Guinan model, resulting in a 200-fold increase in strength. Molecular dynamics simulations of platinum ramp compression yield similar von Mises stress at comparable high pressure and high strain rate conditions. These findings represent the highest-pressure strength measurements ever achieved for platinum and offer critical insight into its significant strengthening behavior under high pressure and strain rate conditions.

Righi, Gaia [Lawrence Livermore National Laborator↗

DOE OSTI2025

Real Time implementation of Artificial Intelligence compression algorithm for High-Speed Streaming Readout signals

The new generation of high-energy physics experiments plans to acquire data in streaming mode. With this approach, it is possible to access the information of the whole detector (organized in time slices) for optimal and lossless triggering of data acquisitions. With this approach, data rates, especially in large detectors, are often very high, and the network is likely to be the bottleneck for the entire Streaming Read Out system. The aim of this work is to study the implementation of a lossy compression algorithm based on Artificial Intelligence: an Autoencoder. With Machine Learning it is possible to achieve a high compression ratio and fast inference time with only a small degradation of the signals, almost negligible for the specific application. This work explores different configurations of the Autoencoder and the implementation on different hardware. Different Autoencoder configurations are explored to find the best trade-off between compression ratio and reconstruction loss, both for signals and energy spectrum. Different hardware implementations are also explored to find the best platform to achieve real-time performance for the specific application.

Rossi, Fabio (ORCID:0009000385713885)↗

DOE OSTI2024

HPC-FAIR: A Framework Managing Data and AI Models for Analyzing and Optimizing Scientific Applications

The increasing reliance on machine learning (ML) to analyze and optimize large-scale scientific applications on supercomputers faces a significant bottleneck: the lack of readily available, high-quality training datasets and the difficulty in reusing existing AI models. This project was motivated by the urgent need to address the “FAIR” principles (Findability, Accessibility, Interoperability, Reusability) for both training datasets and AI models in the high-performance computing (HPC) domain. The project developed HPC-FAIR, a high-performance computing data management framework designed to centralize HPC-related datasets and AI models within a unified hub. To ensure interoperability, the framework established a standardized representation and vocabulary (ontology) for both data and models. HPC-FAIR also implemented automated workflows to streamline data processing, model access, and benchmarking. Additionally, the project focused on optimizing data harnessing efficiency through advanced techniques like deep reuse and compression-based analytics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Bridging material models across scales: An integrated approach to equation of state and molecular dynamics modeling of copper

New uncertainty-aware equation of state (EOS) and electrical conductivity (EC) models for copper have been developed. The multiphase EOS/EC models are fit to experimental solid/liquid EC isobar measurements as well as density-functional theory molecular dynamics (DFT-MD) EC calculations in both expanded and compressed regimes (0.1–16 g/ cm 3 ⁠). The liquid and solid EOS phases were fit to available experimental data along with additional DFT-MD data over the same range as the EC. Leveraging the DFT-MD data, a corresponding machine-learned interatomic potential (MLIAP) for copper was trained using genetic-algorithm optimization. The copper MLIAP was constrained by EOS shock points at high compressions. The final EOS bounded MLIAP proves to be stable over a large density range (approximately 0.1–20 g/ cm 3 ) with good agreement to an isothermal compression curve, shock Hugoniot, and liquid speed of sound measurements at high pressures (100s of GPa).

Acoustic measurements and instrumentation↗

DOE OSTI2024

Data-driven closure modeling for hypersonic turbulent flows

The Reynolds-averaged Navier–Stokes (RANS) equations remain a workhorse technology for simulating compressible fluid flows of practical interest. Due to model-form errors, however, RANS models can yield erroneous predictions that preclude their use on mission-critical problems. This report summarizes work performed from FY22-FY24 focused on improving RANS models for hypersonic flows using data-driven modeling and scientific machine learning. In this work we: 1. Investigate the current capabilities of RANS models in Sandia’s parallel aerodynamics and re-entry code (SPARC) for hypersonic flows with a focus on shock boundary layer interactions (SBLIs), 2. Assess several established corrections that exist in the literature aimed at improving predictions for SBLIs, 3. Develop improved models for the Reynolds stress tensor using tensor-basis neural networks, 4. Develop a neural-network-based variable turbulent Prandtl number model to reduce errors in wall heating in SBLIs. 5. Begin future investigations including employing the LIFE framework to improve wall heating predictions in SBLIs as well as the ensemble Kalman filter. We find that current RANS models in SPARC are deficient for complex SBLI flows. In particular, no current model jointly predicts wall heat flux, wall shear stress, and wall pressure with reasonable accuracy. Existing corrections help, but do not alleviate this issue altogether. The development of improved models for the Reynolds stress tensor via tensor-basis neural networks results in more predictive RANS models across a suite of low-speed and high-speed cases. For hypersonic boundary layers, the inclusion of the wall-normal Reynolds stress via TBNNs has an appreciable impact on the wall-normal momentum balance and wall quantities. However, we find that improvements to the Reynolds stress tensor do not address the over-prediction in wall heat flux in SBLIs. We find that a neural-network-based variable turbulent Prandtl number model systematically and substantially improves wall heating predictions for a range of SBLI cases.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Utah FORGE: 2024 Discrete Fracture Network Model Data

The Utah FORGE 2024 Discrete Fracture Network (DFN) Model dataset provides a set of files representing discrete fracture network modeling for the FORGE site near Milford, Utah. The dataset includes four distinct DFN model file sets, each corresponding to different time frames and modeling approaches in 2024. These models characterize both natural and induced fractures in the geothermal reservoir, which consists of crystalline granitic and metamorphic rock approximately 8,000 feet below the ground surface. The dataset includes a reference DFN model from February 2024 that incorporates planar fractures and well trajectories, as well as upscaled permeability, porosity, compressibility, and storage values on specified grids. Additionally, there are models based on new microseismic (MEQ) data from May and July 2024, including fracture planes fitted to the latest MEQ catalog datasets, tensile fractures from hydraulic stimulation, and an alternative connected DFN for modeling purposes. Coordinate data is provided in both global and local frames, with detailed instructions on the transformations used to align with principal stress orientations. The dataset also includes notes and calculation files for estimating fracture sizes and differences between various fracture sets. There are subfolders for Global Coordinates and Local Coordinates. To move from the global to the local coordinate frame, fractures and wells were a) rotated 20 degrees counterclockwise looking down about the global point (335376.400482041, 4263189.99998761, 250.093546450195) to better align with the principal stresses; and b) translated by (-335408.68, -4263010.9, 1150). Upscaled permeability values using the _XYZ suffix show directions with respect to the global XYZ coordinate frame, while those using the _IJK suffix are aligned with local coordinate frame.

15 GEOTHERMAL ENERGY↗

DOE OSTI2026

Development of an ab initio learned model of electron deposition range in deuterium-tritium plasmas through time-dependent density functional theory calculations and machine learning

Accurate hydrodynamic modeling for laser-direct-drive (LDD) inertial-confinement-fusion (ICF) relies on precise calculations of the electron thermal conduction in all target materials. The nonlocal stopping range of electrons in ICF plasmas directly influences thermal conduction; yet, no first principles model exists for the electron mean free path in the conduction-zone regime. This work utilized time-dependent stochastic density-functional theory (TD-sDFT) to calculate the electron stopping power in deuterium-tritium (DT) plasmas at (ρ, T) conditions relevant to the conduction zone and the compressed shell in ICF. Using a combination of our TD-sDFT data and already established analytical models, we developed and trained an artificial neural network to create a global model for the nonlocal electron deposition range, λ E . We compared our machine-learning (ML) based model for λ E to the currently-used modified-Lee-More model in LDD radiation-hydrodynamic codes, such as lilac, and saw an overall decrease in the deposition range. To understand the effects of λ E on LDD ICF implosion dynamics, we implemented the ML-based model into lilac; specifically, we looked at designs consistent with a current experiment on the OMEGA laser and for a newly designed LDD-ICF target for the future OMEGA-Next facility. In both cases, we saw an overall drop in predicted ablation pressure, peak areal density, and neutron yield due to the reduced thermal conduction (smaller λ E ) in DT plasmas. Comparisons with the experiment on OMEGA are also made.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Utah FORGE: Triaxial Direct Shear Results - February 2025

This dataset contains results from nine triaxial direct shear tests conducted by Los Alamos National Laboratory on samples from FORGE Well 16A(78)-32. The primary objectives of this work were to determine the shear strength in both intact and residual states, evaluate dilation against displacement, assess permeability in relation to displacement, time, and normal stress, understand the relationship between aperture and normal stress, and monitor the effluent chemistry as a function of time. The data includes time-series measurements of stress, displacement, permeability, and effluent chemistry, with and without experimental dilution corrections. Additional materials include profilometry data, photographic documentation of the experimental setups and apparatus, and test notes. The dataset is organized into folders corresponding to each test, containing hydromechanical data, effluent chemistry measurements, and images. The hydromechanical data consists of detailed time-series records capturing parameters such as shear force, confining pressure, permeability, and temperature. Effluent chemistry data tracks fluid composition changes over time. Also included are conference papers, presentation slides, and a summary document outlining the experiments.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Absolute Hugoniot measurements in low-density plastic foams on the NIKE shock compression platform

The propagation of Mbar-range shock waves in low-density foam materials is of interest to target design in all approaches to inertial confinement fusion, high energy density physics, and laboratory astrophysics. We report absolute Hugoniot measurements for CH 1.72 N 0.086 O 0.37 plastic foams with 73 and 94 mg/cm 3 densities in the 32–107 km/s shock velocity range. The experiments were performed on the shock compression platform developed on the NIKE KrF laser facility at the U.S. Naval Research Laboratory. NIKE's 4 or 8-ns long flat-top laser pulses drive steady shock waves into foam targets at the ablative pressures of 1–7 Mbar. The propagation of the ablation and shock fronts is tracked continuously in time using streaked side-on monochromatic x-ray imaging radiography. The straight x−t trajectories of the shock and ablation fronts in the recorded streak images confirmed their steadiness. The SESAME tabulated equation of state predictions generally agrees with our Hugoniot data within a 95% confidence band. The experimental uncertainty of the evaluated shock density compression ratios remains large, indicating the need for more experiments and improved theoretical understanding of the strong shock propagation mechanisms in dry foams.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Modeling a Sodium Heat Pipe Experiment at SPHERE Using Sockeye

The Single Primary Heat Extraction and Rejection Emulator (SPHERE) facility at Idaho Na- tional Laboratory was recently utilized to generate data for the startup and steady operation of a high-performance, sodium heat pipe over the course of 1000 hours, as a test of detrimental, long-term effects of heat pipe operation. The setup consists of a single, sodium heat pipe enclosed in a stainless-steel vacuum chamber, heated radiatively via a cylindrical ceramic fiber heater configuration and cooled via a water-cooled calorimeter. Measurements include temperatures at several axial locations along the outer surface of the heat pipe, the power provided to the heaters, and the heat removal rate of the calorimeter. In this work, this data is utilized to validate heat pipe models in the heat pipe application Sockeye, which is based upon the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. Sockeye provides various heat pipe models at an engineering scale appropriate for the multiphysics simulation of microreactors, which may feature several hundred heat pipes. This work details models of this experiment in SPHERE using various heat pipe models with Sockeye, including heat conduction-based models and compressible flow models of the heat pipe interior. These models are compared to the experimental data to assess the accuracy of several aspects of heat pipe modeling, including frozen startup, the effect of non-condensable gases, and the coupling of the heat pipe to its environment.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Open Specy 1.0: Automated (Hyper)spectroscopy for Microplastics

Microplastic spectral analysis is one of the most time-consuming processes in studying microplastic pollution, often requiring days per sample. Researchers are transitioning to automated batch and hyperspectral image analysis techniques to enhance efficiency. Open Specy, initially aimed at manual single-spectrum analysis, has now integrated automated methods. This updated version, Open Specy 1.0, introduces several new features, including two algorithms for automated processing (smoothing and particle compression), an extensive library containing over 40,000 open-source Raman and FTIR spectra, and two machine learning classifiers (logistic regression and k medoids) developed from this library. Furthermore, it includes a revamped user interface, an R package, and a benchmark data set for testing future advancements in automated techniques. Researchers evaluated various configurations for hyperspectral smoothing, particle identification, compression, and splitting, to achieve combined recovery rates between 50 and 150% particle counts, identities, and sizes with a coefficient of variation (CV) of less than 40% (the accredited standard). Mean absorbance times the standard deviation provided a consistent particle identification. Hyperspectral smoothing led to a 96% combined recovery rate and reduced variability (CV = 38%) compared to the 86% recovery (CV = 83%) of nonsmoothed controls. Additionally, compressing spectra for particles was significantly faster (>3x) and showed similar accuracy but with reduced variability than processing each pixel individually. Key challenges persist in automating spectral analysis, particularly in refining particle splitting algorithms, and improving identification routines to minimize false positives and negatives. In conclusion, new methods in sample preparation for better stabilization and dispersion of particles could overcome some of these issues.

13 HYDRO ENERGY↗