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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

FAIR Surrogate Benchmarks Supporting AI and Simulation Research (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia (UVA). SBI repositories include data, code, and all relevant collateral artifacts that the science and engineering community need to use and reuse these data sets and surrogates. SBI repositories generate active research from both the participants in SBI and the broad community of AI and domain scientists. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and captures them as surrogate benchmarks with a rich set of metadata covering: Data; Model; Metrics specification; Machine specification; and Science, Speed, and Power Results. We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non-Surrogate benchmarks that have many common features and similar issues as regards FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, Benchmarks have datasets, models, and metadata and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Atomate2: modular workflows for materials science

High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Focus on monitoring and control of complex supply systems

The ongoing rapid transformation of our energy supply challenges the operation and stability of electric power grids and other supply networks. This focus issue comprises new ideas and concepts in the monitoring and control of complex networks to address these challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A new database website for nuclear level densities

We introduce a new open-access, web-based database (http://nld.ascsn.net), Current Archive of Nuclear Density of Levels (CANDL), that hosts experimental nuclear level density (NLD) datasets from a variety of techniques and energy ranges. Built using the Dash framework in Python, the database is designed to be interactive and user-friendly, allowing researchers to search, visualize, fit, and export NLD data with minimal effort. This resource includes data extracted from evaporation spectra, Oslo method variants, and other experimental techniques that cover excitation energies beyond the neutron resonance region. The database supports on-the-fly fitting with two widely-used phenomenological models—the Constant Temperature (CT) model and the Back-Shifted Fermi Gas (BSFG) model—selected for their simplicity and computational efficiency. Future versions aim to include additional datasets and model types, as well as easy-to-use interfaces to data science techniques. Here, this platform offers a vital tool for the nuclear physics, astrophysics, medicine, and reactor design communities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Optimal Transport as a Tool for Scientific Discovery in Radiation Biology

This report summarizes findings from research conducted for the “Exploration of the Poten tial for Artificial Intelligence and Machine Learning to Advance Low-Dose Radiation Biology Re search” (RadBio-AI) program, supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, under Awards KP1601011/FWP CC121 and KP1601017/FWP CC121. The research reported here was undertaken in an effort to assess the potential of optimal measure transport methods as components within the larger scope of a com putational framework envisioned to support research in the radiation biology domain. Within this effort, our interest centered on enabling a unified generic framework where probabilistic modeling, inference, and statistical learning can be carried out for a wide range of data distributions. As described next in Section 1 (and in more detail in our original publication), optimal measure transport offers the possibility of such unified approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Scaling microstructural processes in the sintering of ionic ceramics

A multi-scale framework, combining a multiphase field formulation and large deformation mechanics, was developed as a stepping stone to perform the data analytics of the microstructural level kinetics of a sintering solid. Relevant microstructural information from this framework, such as grain, stress, and porosity statistics, was scaled up to describe the macroscopic level sintering kinetics. Here, the developed formulation was applied to describe the electric field assisted sintering of Y 2 O 3 . Microstructural inhomogeneities in a multi-granular solid result in the formation of a field of compressive stress networks, which interleave with low compression and weakly tensile regions, defining a scaffolding for sintering concentration regions to develop. A Poisson effect-induced lateral stress network is also naturally self-induced as a result of the mechanical constraints imposed by the sintering apparatus. For long sintering times, localized shear stresses enhancing mass flow along grain boundaries and internal surfaces develop. Three-sided pores are removed by either vacancy transport to the surrounding pores, or move towards the external surfaces through grain boundary diffusion. Four- and higher order-sided pores stabilize because an equal amount of vacancies are gained and lost through the connecting grain boundaries. Grain dewetting contributes to pore coalescence, suggesting that pore kinetics and grain growth are coupled and should be analyzed in concert. The combined sintering and grain growth kinetics define six regimes of sintering behavior: (1) T, the transient regime; (2) E$_Υ$, the surface energy dominated, early sintering regime, where the grain growth exponent, p = 1, and the stress concentration factor, $f$ ~ $1/\hat{ρ}^{4.6}$; (3) E S , the stress dominated, early sintering regime, where p = 1 and $f$ ~ $1/\hat{ρ}^{4}$; (4) I$_Υ$, the surface energy dominated, intermediate sintering regime, where p = 2 and $f$ ~ $1/\hat{ρ}^{4.6}$; (5) I S , the stress dominated, intermediate sintering regime, where p = 2 and $f$ ~ $1/\hat{ρ}^{4}$; and (6) L, the late sintering regime, where p = 3 and $f$ ~ 1. At the macroscopic level, the rapid densification and suppression of grain growth observed in the electric field assisted sintering process is a consequence of the compounding effects of the underlying stress-, transport-, and interfacial-energy-induced energy minimization kinetics, as predicted by the multi-scale framework.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Electronic structure prediction of medium and high entropy alloys across composition space

We propose machine learning (ML) models to predict the electron density — the fundamental unknown of a material’s ground state — across the composition space of concentrated alloys. From this, other physical properties can be inferred, enabling accelerated exploration. A significant challenge is that the number of descriptors and sampled compositions required for accurate prediction grows rapidly with species. To address this, we employ Bayesian Active Learning (AL), which minimizes training data requirements by leveraging uncertainty quantification capabilities of Bayesian Neural Networks. Compared to the strategic tessellation of the composition space, Bayesian-AL reduces the number of training data points by a factor of 2.5 for ternary (SiGeSn) and 1.7 for quaternary (CrFeCoNi) systems. We also introduce easy-to-optimize, body-attached-frame descriptors, which respect physical symmetries while keeping descriptor-vector size nearly constant as alloy complexity increases. Our ML models demonstrate high accuracy and generalizability in predicting both electron density and energy across composition space.

materials science↗
DOE OSTI2025

Towards a Deeper Fundamental Understanding of (Al,Sc)N Ferroelectric Nitrides

Density functional theory (DFT) calculations, within the virtual crystal alloy approximation, are performed, along with the development of a Landau-type model employing a symmetry-allowed analytical expression of the internal energy and having parameters determined from first principles, to investigate properties and energetics of Al1-xScxN ferroelectric nitrides in their hexagonal forms. These DFT computations and this model predict the existence of two different types of minima, namely, the fourfold-coordinated wurtzite (WZ) polar structure and a five-fold coordinated paraelectric hexagonal phase (denoted as H5), for any Sc composition up to 40%. The H5 minimum progressively becomes the lowest-energy state within hexagonal symmetry as the Sc concentration increases from 0 to 0.4. Furthermore, the model points to several key findings. Examples include the crucial role of the coupling between polarization and strains to create the WZ minimum, in addition to polar and elastic energies, and that the origin of the H5 state overcoming the WZ phase as the global minimum within hexagonal symmetry when increasing the Sc composition mostly lies in the compositional dependency of only two parameters-one linked to the polarization and another one being purely elastic in nature. Other examples are that forcing Al1-xScxN systems to have no or a weak change in lattice parameters when heating them allows us to reproduce their finite-temperature polar properties well and that a value of the axial ratio close to that of the ideal WZ structure implies a large polarization at low temperatures but not necessarily at high temperatures because of the ordered-disordered character of the temperature-induced formation of the WZ state. Such findings should allow for a better fundamental understanding of (Al,Sc)N ferroelectric nitrides, which may be used to design efficient devices having, e.g., low operating voltages.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Computational Modeling of Atmospheric Processes at Texas Southern University

Texas Southern University (TSU) is strengthening its research program in atmospheric chemistry and physics with a climate science emphasis by leveraging partnerships with the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Facility, Brookhaven National Laboratory (BNL), and the Tracking Aerosol Convection Interactions ExpeRiment (TRACER). This RDPP-supported program focuses on secondary organic aerosols (SOAs) and reactive atmospheric species that influence cloud formation, precipitation processes, and radiative forcing. SOAs play a critical role in cloud microphysics and Earth’s energy balance, yet the chemical and physical mechanisms governing SOA–cloud interactions remain a significant source of uncertainty in predictive climate models. Through computational modeling, observational data analysis, and national laboratory collaboration, this program develops a skilled cohort of students trained in atmospheric science, environmental data analysis, and climate-relevant modeling. These research experiences build technical competencies that are transferable to careers in government laboratories, academia, and industry. By engaging students from historically underrepresented communities in high-impact climate research, TSU expands participation in the atmospheric sciences workforce while contributing meaningful scientific insights to DOE-supported ARM research activities. This partnership strengthens national capacity in climate science and supports the development of the next generation of atmospheric researchers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Novel Relativistic Electronic Structure Theories for Actinide-Containing Compounds

Actinides of importance to basic energy sciences contain electrons moving at speed comparable to the speed of light. Reliable computational simulation of these electrons and hence actinide chemistry requires accurate description of relativistic effects. The present project advances computational actinide chemistry with development of new methodologies, algorithms, and computer programs in relativistic quantum chemistry, as well as applications to actinide chemistry and spectroscopy. A new “electrons-only” exact two-component approach has been developed to provide efficient treatments of relativistic effects, while maintaining chemical accuracy. New computational algorithms developed here extend the applicability of relativistic electron-correlation methods to larger molecules. The method-development work in this project also features the first implementation of analytic gradient technique for relativistic electron-correlation methods, which provides significantly enhanced ability to compute properties for molecules containing actinides. The applicability and usefulness of these new methods and computer programs have been demonstrated in calculations of actinide-containing molecules to facilitate understanding of actinide chemistry and spectroscopy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Light Output Fitting Software

Light output response of scintillators is crucial to the utilization of organic scintillators as effective tools in radiation detection and measurement. While the response is a continuous distribution, the light output corresponding to the maximum energy deposition is crucial in effectively understanding and simulating a detector. There are a variety of fits derived in literature that will vary for every detector material. The Light Output Response Fitter, or LORF Program is a python script designed to easily and quickly compute and plot fits for a variety of scintillator light output models. It includes a stopping power library constructed from SRIM including Organic Glass, EJ309, EJ301, Stilbene, EJ276, and their deuterated counterparts by default, with the ability for the user to add custom stopping power libraries. The user is also capable of importing the python package and utilizing its in-built functions as appropriate. Uses for this capability include plotting and computing a model with known parameters.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Acceleration of Thermochemistry Solves in MOOSE and Pronghorn

This work focuses on the development and implementation of strategies to accelerate thermochemical calculations within MOOSE-based multiphysics simulations, particularly for applications in MSRs. We highlight the inherent complexity of nuclear materials, which require a multiscale approach to accurately model their behavior across various physical domains, including mechanical, chemical, and thermal phenomena. Thermochemical equilibrium calculations are crucial for predicting material properties and enhancing the fidelity of these simulations. The integration of Thermochimica, a Gibbs energy minimizer, into MOOSE allows for the direct minimization of Gibbs energy at every point on the mesh. However, the computational cost of such integration is significant. To address this, we explored acceleration strategies such as multi-threading support and the use of a thermodynamic ValueCache to reduce redundant calculations. Additionally, we investigated modifications to Thermochimica to enable phase constraints and improve its coupling with phase-field models, which are essential for simulating microstructural evolution and corrosion in MSR. These efforts aim to optimize the computational efficiency and accuracy of multiphysics simulations, thereby supporting the development of reliable and efficient nuclear materials for next-generation reactor technologies.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

A kinetic-based regularization method for data science applications

We propose a physics-based regularization technique for function learning, inspired by statistical mechanics. By drawing an analogy between optimizing the parameters of an interpolator and minimizing the energy of a system, we introduce corrections that impose constraints on the lower-order moments of the data distribution. This minimizes the discrepancy between the discrete and continuum representations of the data, in turn allowing to access more favorable energy landscapes, thus improving the accuracy of the interpolator. Our approach improves performance in both interpolation and regression tasks, even in high-dimensional spaces. Unlike traditional methods, it does not require empirical parameter tuning, making it particularly effective for handling noisy data. We also show that thanks to its local nature, the method offers computational and memory efficiency advantages over Radial Basis Function interpolators, especially for large datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Science & Technology Review June 2026: Predicting Tsunamis

At Lawrence Livermore National Laboratory, we focus on science and technology research to ensure our nation’s security. We also apply that expertise to solve other important national problems in energy, bioscience, and the environment. Science & Technology Review is published eight times a year to communicate, to a broad audience, the Laboratory’s scientific and technological accomplishments in fulfilling its primary missions. The publication’s goal is to help readers understand these accomplishments and appreciate their value to the individual citizen, the nation, and the world.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Inter-year Variability in EDS Standards

This report provides a deep dive into how the stability of energy dispersive spectroscopy (EDS) standards yielded insight into the shortfalls of using software to randomly select points for spectral acquisition when using heterogeneous standards. Some procedural recommendations are included which are meant to minimize the impact of anomalous reference standard spectra by detecting them before the standard is implemented into data processing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Science & Technology Review December 2025 - Optimizing Future Design

At Lawrence Livermore National Laboratory, we focus on science and technology research to ensure our nation’s security. We also apply that expertise to solve other important national problems in energy, bioscience, and the environment. Science & Technology Review is published eight times a year to communicate, to a broad audience, the Laboratory’s scientific and technological accomplishments in fulfilling its primary missions. The publication’s goal is to help readers understand these accomplishments and appreciate their value to the individual citizen, the nation, and the world.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The Dark Energy Survey supernova program: a reanalysis of cosmology results and evidence for evolving dark energy with an updated Type Ia supernova calibration

We present improved cosmological constraints from a re-analysis of the Dark Energy Survey (DES) 5-year sample of Type Ia supernovae (DES-SN5YR). This re-analysis includes an improved photometric cross-calibration, recent white dwarf observations to cross-calibrate between DES and low-redshift surveys, retraining the salt3 light-curve model and fixing a numerical approximation in the host-galaxy colour law. Our fully recalibrated sample, which we call DES-Dovekie, comprises ~1600 likely Type Ia SNe from DES and ~200 low-redshift SNe from other surveys. With DES-Dovekie, we obtain Ω m = 0.330 ± 0.015 in flat Lambda-cold dark matter (⁠ΛCDM) which changes Ω m by –0.022 compared to DES-SN5YR. Combining DES-Dovekie with cosmic microwave background data from Planck, Atacama Cosmology Telescope, and South Pole Telescope and the DESI DR2 measurements in a flat CDM cosmology, we find ω 0 = –0.803 ± 0.054 and ω a = –0.72 ± 0.21⁠. Our results hold a significance of 3.2σ, reduced from 4.2σ for DES-SN5YR, to reject the null hypothesis that the data are compatible with the cosmological constant. This significance is equivalent to a Bayesian model preference odds of approximately 5:1 in favour of the flat ω 0 ω a CDM model. Using generally accepted thresholds for model preference, our updated data exhibits only a weak preference for evolving dark energy.

dark energy↗
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

97 MATHEMATICS AND COMPUTING↗