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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Amanzi–ATS: Modeling Environmental Systems across Scales [Brief]

Department of Energy national labs (Los Alamos, Oak Ridge, Lawrence Berkeley, and Pacific Northwest) designed Amanzi-ATS to model complex environmental systems across multiple scales. The open-source software includes the most complete suite of surface/subsurface processes, allowing users to select physical processes and their coupling interactions without rewriting software. Amanzi–ATS has been used to analyze pristine local watersheds, wildfire impact on watersheds, subsurface contaminant transport at legacy waste sites, the effect of a warming climate on the Arctic tundra, and groundwater in fractured porous media. Department of Energy national labs, U.S. Geological Survey, academia, and industry have applied the software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Differentially Private Map Matching (DPMM) v1.0

Human mobility trajectories provide valuable information for developing mobility applications, as they contain diverse and rich information about the users. User mobility data is valuable for various applications such as intelligent transportation systems (ITS), commercial business models, and disease-spread models. However, such spatio-temporal traces may pose a threat to user privacy. GPS trajectories in their raw form are not suitable for transportation studies, as they require matching locations with nearest road links — a process called map-matching. This software implements a differential privacy (DP)-based map-matching algorithm, called DPMM, that generates link-level location trajectories in a privacy-preserving manner to protect users' origin destinations (OD) and travel paths. OD privacy is achieved by injecting Planar Laplace noise to the user OD GPS points. Travel-path privacy is provided with randomized travel path construction using exponential DP mechanism. The injected noise level is selected adaptively, by considering the link density of the location and the functional category of the localized links. For path privacy, our mechanism samples waypoints and selects candidate paths between waypoints. DPMM provides privacy effectively with respect to link density instead of other trajectory samples in the database compared to other privacy mechanisms. Compared to the different baseline models our DP-based privacy model offers closer query responses to the raw data in terms of individual and aggregate trajectory-level statistics with an average at absolute deviation from the baseline for individual statistics on ϵ = 1.0. Beyond individual trajectory statistics, the DPMM outperforms the other benchmark DP-based mechanisms on different aggregate statistics with up to 8x improvement in utility.

Peisert, Sean [Lawrence Berkeley National Laborato↗
DOE OSTI2025

Differentially Private Adaptive Noise Injection (DP-ANI) v1.0

Location data is collected from users continuously to understand their mobility patterns. Releasing the user trajectories may compromise user privacy. Therefore, the general practice is to release aggregated location datasets. However, private information may still be inferred from an aggregated version of location trajectories. Differential privacy (DP) protects the query output against inference attacks regardless of background knowledge. This software implements a differential privacy-based privacy model that protects the user's origins and destinations from being inferred from aggregated mobility datasets. This is achieved by injecting Planar Laplace noise to the user origin and destination GPS points. The noisy GPS points are then transformed into a link representation using a link-matching algorithm. Finally, the link trajectories form an aggregated mobility network. The injected noise level is selected using the Sparse Vector Mechanism. This DP selection mechanism considers the link density of the location and the functional category of the localized links. Compared to the different baseline models, including a k-anonymity method, our differential privacy-based aggregation model offers query responses that are close to the raw data in terms of aggregate statistics at both the network and trajectory-levels with maximum 9% deviation from the baseline in terms of network length.

Peisert, Sean [Lawrence Berkeley National Laborato↗
DOE OSTI2025

GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

Large language models (LLMs) are increasingly adapted to downstream tasks via reinforcement learning (RL) methods like Group Relative Policy Optimization (GRPO), which often require thousands of rollouts to learn new tasks. We argue that the interpretable nature of language often provides a much richer learning medium for LLMs, compared to policy gradients derived from sparse, scalar rewards. To test this, we introduce GEPA (Genetic-Pareto), a prompt optimizer that thoroughly incorporates natural language reflection to learn high-level rules from trial and error. Given any AI system containing one or more LLM prompts, GEPA samples trajectories (e.g., reasoning, tool calls, and tool outputs) and reflects on them in natural language to diagnose problems, propose and test prompt updates, and combine complementary lessons from the Pareto frontier of its own attempts. As a result of GEPA's design, it can often turn even just a few rollouts into a large quality gain. Across six tasks, GEPA outperforms GRPO by 6% on average and by up to 20%, while using up to 35x fewer rollouts. GEPA also outperforms the leading prompt optimizer, MIPROv2, by over 10% (e.g., +12% accuracy on AIME-2025), and demonstrates promising results as an inference-time search strategy for code optimization. We release our code at https://github.com/gepa-ai/gepa.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Validation of the DESI-DR1 3x2-pt analysis: scale cut and shear ratio tests

Combined survey analyses of galaxy clustering and weak gravitational lensing (3x2-pt studies) will allow new and accurate tests of the standard cosmological model. However, careful validation is necessary to ensure that these cosmological constraints are not biased by uncertainties associated with the modelling of astrophysical or systematic effects. In this study we validate the combined 3x2-pt analysis of the Dark Energy Spectroscopic Instrument Data Release 1 (DESI-DR1) spectroscopic galaxy clustering and overlapping weak lensing datasets from the Kilo-Degree Survey (KiDS), the Dark Energy Survey (DES), and the Hyper-Suprime-Cam Survey (HSC). By propagating the modelling uncertainties associated with the non-linear matter power spectrum, non-linear galaxy bias and baryon feedback, we design scale cuts to ensure that measurements of the matter density and the amplitude of the matter power spectrum are biased by less than 30% of the statistical error. We also test the internal consistency of the data and weak lensing systematics by performing new measurements of the lensing shear ratio. We demonstrate that the DESI-DR1 shear ratios can be successfully fit by the same model used to describe cosmic shear correlations, and analyse the additional information that can be extracted about the source redshift distributions and intrinsic alignment parameters. This study serves as crucial preparation for the upcoming cosmological parameter analysis of these datasets.

Emas, N. [Swinburne U., Ctr. Astrophys. Supercompu↗
DOE OSTI2021

DuraMAT (Durable Module Materials Consortium) Annual Report FY 2020

The Durable Module Materials Consortium (DuraMAT) launched in November 2016 with five years of funding as part of the U.S. Department of Energy’s (DOE’s) Energy Materials Network. DuraMAT is a multilab consortium, led by the National Renewable Energy Laboratory (NREL) and Sandia National Laboratories (Sandia), with SLAC National Accelerator Laboratory (SLAC) and Lawrence Berkeley National Laboratory (LBNL) as core research labs. DuraMAT’s overarching goal is to discover, develop, de-risk, and enable the rapid commercialization of improved materials, designs, predictive tests, and models for PV modules that increase performance, extend lifetime, and enable new applications. We work in partnership with our 15-member Industry Advisory Board and the technical management team in DOE’s Solar Energy Technologies Office. This document describes DuraMAT activities in FY20.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

Standardizing UI/UX across accelerator labs

During February 26–28, 2025, the first-ever particle accelerator user interface/user experience (UI/UX) workshop was held at SLAC. Attendees had backgrounds ranging from software development to control systems management and human factors (HF) science. The workshop began with participants discussing the current state of UI/UX procedures and practices at their respective laboratories to share experiences and learn from one another. Additional discussions focused on how to effectively integrate UI/UX best practices into actionable goals for developers, managers, and operators when working on new or existing interfaces. The goal of the working group is to create a website that will guide developers, managers, scientists, and end users at accelerator laboratories in incorporating UI/UX best practices into software development. The working group continues to meet virtually toward this goal, and is planning a second workshop for next year.

Tran, Tiffany [SLAC]↗
DOE OSTI2024

Residual resistance ratio measurement system for Nb 3 Sn wires extracted from Rutherford cables

Residual resistance ratio (RRR) of superconducting strands is an important parameter for magnet electrical stability. RRR serves as a measure of the low-temperature electrical conductivity of the copper within a conductor that has a copper stabilization matrix. For Nb 3 Sn, due to the need of a reaction heat treatment, the technical requirements for high quality measurements of strands extracted from Rutherford cables are particularly demanding. Quality of wire, cabling deformation, heat treatment temperature, heat treatment atmosphere, sample handling, and measurement methods can all affect the RRR. Therefore, as an integral part of the electrical quality control (QC) of Nb 3 Sn Rutherford cables manufactured at the Lawrence Berkeley National Laboratory, it was prudent that we established a RRR measurement system that can isolate the assessment of cable-fabrication-related impacts from sample preparation and measurement factors. Here we describe a bespoke cryocooler-based measurement system, capable of measuring RRR of over 80 samples in a single cooldown. The samples are mounted on custom-designed printed circuit boards that accommodate the shape of strands extracted from a Rutherford cable without added deformation, which we will show is critical in ensuring that the measurements accurately represent the RRR values of the conductor within the cable. Using this sample mounting solution, we routinely measure the overall RRR of the strand as well as individual intra-strand sections corresponding to both cable edges and cable broad faces with high reproducibility. Such measurements provide valuable information on the variation of RRR along the length of the strands as well as across strand productions and cable runs over time.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Investigating high-energy proton-induced reactions on spherical nuclei: Implications for the preequilibrium exciton model

A number of accelerator-based isotope production facilities utilize $100-200$ MeV proton beams due to the high production rates enabled by high-intensity beam capabilities and the greater diversity of isotope production brought on by the long range of high-energy protons. However, nuclear reaction modeling at these energies can be challenging because of the interplay between different reaction modes and a lack of existing guiding cross section data. A Tri-lab collaboration has been formed between the Lawrence Berkeley, Los Alamos, and Brookhaven National Laboratories to address these complexities by characterizing charged-particle nuclear reactions relevant to the production of established and novel radioisotopes. In the inaugural collaboration experiments, stacked-targets of niobium foils were irradiated at the Brookhaven Linac Isotope Producer ($E_p=200$ MeV) and the Los Alamos Isotope Production Facility ($E_p=100$ MeV) to measure $^{93}$Nb(p,x) cross sections between $50-200$ MeV. The results were compared with literature data as well as the default calculations of the nuclear model codes TALYS, CoH, EMPIRE, and ALICE. The default code predictions largely failed to reproduce the measurements. Therefore, we developed a standardized procedure, which determines the reaction model parameters that best reproduce the most prominent reaction channels in a physically justifiable manner. Overall, the primary focus of the procedure was to determine the best parameterization for the pre-equilibrium two-component exciton model. This modeling study revealed a trend towards a relative decrease for internal transition rates at intermediate proton energies ($E_p=20-60$ MeV) in the current exciton model as compared to the default values. The results of this work are instrumental for the planning, execution, and analysis essential to isotope production.

43 PARTICLE ACCELERATORS↗
DOE OSTI2017

CZT DTRA final report

The objective of the project is to understand the physical origin of electronic noise injected by the electrical contacts in CZT and CdTe, and moreover to understand how it impacts the current- voltage (IV) relationships of these materials. This understanding is critical to enabling the next crucial enhancement in the performance of CZT radiation detectors, as there have recently been impressive advancements in the growth of CZT crystals, particularly at our commercial partner Redlen Technologies. Redlen scientists have successfully reduced the size of the transport-inhibiting tellurium precipitates to be <3 micrometers, such that, with sufficiently high fields, it is possible to achieve resolution of <1% at 662 keV using suitable electrode geometries. In contrast to the excellent progress in crystal growth, practitioners in the field of radiation detection have been fabricating rather routine contacts on CZT for nearly two decades; there is no basic understanding of the semiconductor physics of the contacts, and consequently no breakthrough progress in this area. Our objective is to resolve this inadequacy in CZT diode fabrication on the basis of a science-based study, such that CZT detectors can achieve their full promise in performance as superior contacts will enable use of higher fields with lower leakage current – thereby enhancing the resolution that is possible while eliminating the well-known “tailing” effect suffered by the photopeak. Our approach is to develop methods that reduce or eliminate leakage currents in CZT devices through “engineering” the surfaces with novel treatments and structures. This includes using high density plasma etching, doping via ion implantation and metal diffusion, rapid thermal annealing, amorphous semiconductor and dielectric films, and controlled oxide growth. Using these methods, sources of injected and generated noise at the surface can be eliminated via plasma etching and film deposition or oxide growth, while advanced junctions (both homo- and heterojunctions) can be created to eliminate current injection from the metal contacts. The work involves the fabrication and characterization of CZT having a variety of contact structures. Tools capable of evaporating and sputtering metals, together with the usual lithographic and chemical processing methods, are employed. The CZT material employed will be of the highest quality available (Redlen Technologies), and we are engaging Prof. Burger of Fisk University, who is internationally recognized as one of the most accomplished materials physicists in the field on radiation detectors. We have also begun collaborating with Prof. Nicholas Kioussis of Cal State Northridge, Srivananthan Labs (Bolingbrook, IL), and Mark Amman of Lawrence Berkeley National Laboratory, all funded separately by DTRA.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Determination of Molecular Structure and Dynamics of Molten Salts by Advanced Neutron and X-ray Scattering Measurements and Computer Modeling

The design and development of fully functional Molten-Salt Reactors (MSR) require detailed knowledge of the molten salt properties in order to understand and predict the salt’s behavior. Fundamental properties of interest include molecular structure, speciation, and dynamics (such as diffusion coefficients) of salt components and dissolved corrosion and fission products. Computer modeling is necessary to predict changes in physical and chemical properties due to irradiation, burning of dissolved fuel, and corrosion. The modeling requires experimental data, and advanced neutron and x-ray scattering and spectroscopy provide the most reliable and direct determination of the structure (Pair-Distribution Functions, PDF), and dynamics of ions in the melt. This project dealt with both fluoride and chloride salts. The PDFs have been measured by a combination of neutron and x-ray diffraction. We utilized the techniques of isotope substitutions, a very powerful tool available for neutron-scattering, to extract the details of the liquid structure. Although similar measurements have been done before, modern advanced neutron and x-ray-scattering techniques allow collecting the data at much higher resolution and in a wider range of temperatures. Importantly, we were among the first to study fluoride salts by neutron scattering. The importance of impurities and their effects on salt properties have become apparent recently and so new methods of salt purification were developed. We took advantage of these developments to produce reliable data, which have been used for computer simulations of both clean salts and those with added fission and corrosion products most relevant for MSRs. Ab initio molecular dynamics simulations have been performed to understand the multi-component liquid solution, in particular solubility of impurities and thermodynamic interactions in relation to the ionic-cluster structure of the fluid. We applied machine learning to regress from the simulation and experimental data in order to develop a fast-acting model that can handle molten salt with an arbitrary (≥ 10) number of chemical elements and be able to predict chemical potential as a function of composition and temperature. This project resulted in a number of experimental and computer-simulation publications, a patent application, and numerous conference presentations (American Physical Society, American Chemical Society, and The Electrochemical Society among others). Multiple students and postdocs participated and collaborated on aspects of this project. This project seeded new collaborations between MIT and other institutions, such as the University of Massachusetts Lowell, the University of Illinois Urbana-Champaign, the University of California Berkeley, and Oak Ridge and Los Alamos National Labs. As such, this project has had a broad and lasting impact beyond its original scientific scope.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Genesis Mission Data cards

As data-intensive research and artificial intelligence become central to DOE mission science, the need for machine-actionable dataset documentation has grown accordingly. However, many DOE-aligned communities, including the Office of Science, NNSA, and cross-laboratory collaborations, have developed independent metadata practices. This fragmentation creates friction for discovery, federation, and reuse across programs. To address these challenges, this talk introduces the Genesis Data Card: a shared metadata artifact developed in collaboration with a broad DOE community (Jefferson Lab and the National Lab of the Rockies, Oak Ridge, Sandia, Idaho, Berkeley, and Los Alamos). The Genesis Data Card aims to standardize dataset documentation across DOE-aligned initiatives while remaining extensible to discipline-specific needs. This talk will describe the data card template and the supporting code to validate completed data cards, using a companion LinkML schema. I'll walk through the design decisions behind the template, its alignment with existing standards, its treatment of sensitivity and governance metadata, and the phased roadmap toward lifecycle-integrated "xCards" that support autonomous discovery and reuse. The talk closes with current gaps, ongoing work, and how others can contribute datasets and feedback to the shared repository.

McSpadden, Helen [Thomas Jefferson National Accele↗
DOE OSTI2026

Meteor over New York City: Brines in a primitive CM asteroid

The CI (Ivuna-type) carbonaceous material returned from asteroids Ryugu and Bennu contain mobilized sodium from the evaporation or freezing of liquid water into brines, shedding light on the internal structure of ice-rich CI-type worlds and the formation of prebiotic organic compounds. The formation of brines has not been demonstrated in CM (Mighei-type) carbonaceous chondrites, which also supplied organic matter to the early Earth. Here, we announce the fall of a primitive meteorite from a daytime fireball over the New York metropolitan area in July 2024. It is a CM2 breccia that contains unique CM1 clasts rich in water and sodium. The meteorite contains abundant amino acids and other products of organic chemistry in brines that reveal subsurface processes on CM-type asteroid parent bodies.

Geosciences↗
DOE OSTI2025

HydroX, a light dark matter search with hydrogen-doped liquid xenon time projection chambers

Experimental efforts searching for dark matter particles over the last few decades have ruled out many candidates led by the new generation of tonne-scale liquid xenon. For light dark matter, hydrogen could be a better target than xenon as it would offer a better kinematic match to the low mass particles. This article describes the HydroX concept, an idea to expand the dark matter sensitivity reach of large liquid xenon detectors by adding hydrogen to the liquid xenon. We discuss the nature of signal generation in liquid xenon to argue that the signal produced at the interaction site by a dark matter–hydrogen interaction could be significantly enhanced over the same interaction on xenon, increasing the sensitivity to the lightest particles. We discuss the technical implications of adding hydrogen to a xenon detector, as well as some background considerations. Finally, we make projections as to the potential sensitivity of a HydroX implementation and discuss next steps.

Lippincott, W. H. [UC, Santa Barbara] (ORCID:00000↗
DOE OSTI2025

Systematic softening in universal machine learning interatomic potentials

Machine learning interatomic potentials (MLIPs) have introduced a new paradigm for atomic simulations. Recent advancements have led to universal MLIPs (uMLIPs) that are pre-trained on diverse datasets, providing opportunities for universal force fields and foundational machine learning models. However, their performance in extrapolating to out-of-distribution complex atomic environments remains unclear. In this study, we highlight a consistent potential energy surface (PES) softening effect in three uMLIPs: M3GNet, CHGNet, and MACE-MP-0, which is characterized by energy and force underprediction in atomic-modeling benchmarks including surfaces, defects, solid-solution energetics, ion migration barriers, phonon vibration modes, and general high-energy states. The PES softening behavior originates primarily from the systematically underpredicted PES curvature, which derives from the biased sampling of near-equilibrium atomic arrangements in uMLIP pre-training datasets. Our findings suggest that a considerable fraction of uMLIP errors are highly systematic, and can therefore be efficiently corrected. We argue for the importance of a comprehensive materials dataset with improved PES sampling for next-generation foundational MLIPs.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Roll-to-Roll Advanced Materials Manufacturing Lab Consortium

Oak Ridge National Laboratory (ORNL), Argonne National Laboratory (ANL), Lawrence Berkeley National Laboratory (LBNL), and the National Renewable Energy Laboratory (NREL) in collaboration with Eastman Kodak Business Park (Kodak) and Citrine Informatics (Citrine) formed a consortium to broadly disseminate materials, process science, and advanced technologies to industry in the area of R2R manufacturing.

36 MATERIALS SCIENCE↗