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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

This package includes geospatial data layers developed to investigate how environmental gradients—specifically topography and near-surface soil properties—drive the spatial arrangement of dominant plant communities in mountainous watersheds. The geospatial products, which support the analysis of these ecological relationships, are derived from airborne hyperspectral and LiDAR datasets acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP), in conjunction with an extensive ground field campaign conducted in summer 2018. This work is part of the DOE Watershed Function Science Focus Area (SFA) and features geospatial datasets developed based on observations and ground data collected at East River, Colorado, in collaboration with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey in June 2018. Classification Map: - Classification Map (PNG, GeoTIFF): Derived from hyperspectral and LiDAR airborne data using a machine learning approach. - Class Code Mapper (CSV): Associates pixel values with corresponding vegetation/non-vegetation classes. - Classification Reference Data (CSV): Reference data used in the machine learning procedure. LiDAR-Derived Products: - Topographical Metrics (GeoTIFFs): Elevation, slope, curvature, TWI, TPI, solar insolation, and canopy height model (CHM), smoothed with a 5x5 pixel window. Vegetation Indices: - GeoTIFFs of NDVI, NDNI, NDWI: Vegetation indices derived from hyperspectral data. Urban Masks: - Urban Mask (GeoTIFF): Applied to the mapping to convert bare soil classes to urban classes. Software Compatibility: GeoTIFFs: Can be visualized with GIS software or libraries that support GeoTIFF images. CSV Files: Can be opened with any software that handles comma-separated values. The FLMD file provides details and links to the source datasets used to derive the products. The manuscript (in the Method session) provides details on how each product was derived. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Update on 2026-03-25: Since the original dataset publication date of 02/28/2020, this package has a new classification map derived by an improved methodology. This update also includes additional ground data that improved the representation of some of the communities. See the methods for further details on what has changed between versions.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2018

Betatron x-ray radiation in the self-modulated laser wakefield acceleration regime: prospects for a novel probe at large scale laser facilities

This paper presents an experimental and theoretical study of betatron x-ray radiation from laser wakefield acceleration in the self-modulated regime. Our experiments use picosecond duration laser pulses up to 150 J, for plasmas with electronic densities on the order of 10 19 cm -3 . In the self-modulated regime, electrons accelerated in the wake of the laser pulse are subject to both the longitudinal plasma and transverse laser electrical fields. As a result, they undergo oscillations and radiate a synchrotron-like spectrum. In our experimental configuration, electrons accelerated up to about 300 MeV, as well as betatron x-ray spectra with energies of 10 s of keV and photon fluxes between 10 8 –10 10 photons/eV/Sr are reported here. Our experiments open the prospect of using betatron x-ray radiation for applications, and the source could be competitive with current x-ray backlighting methods on multi-kilojoule laser systems used for high energy density or fusion sciences.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Optimizing Batch Crystallization with Model-based Design of Experiments

Adaptive and self-optimizing intelligent systems such as digital twins are increasingly important in science and engineering. Digital twins utilize mathematical models to provide added precision to decision-making. However, physics-informed models are challenging to build, calibrate, and validate with existing data science methods. Model-based design of experiments (MBDoE) is a popular framework for optimizing data collection to maximize parameter precision in mathematical models and digital twins. In this work, we apply MBDoE, facilitated by the open-source package Pyomo.DoE, to train and validate mathematical models for batch crystallization. We quantitatively examined the estimability of the model parameters for experiments with different cooling rates. This analysis provides a quantitative explanation for the heuristic of using multiple experiments at different cooling rates.

Lynch, Hailey↗
DOE OSTI2021

Advanced magnetic X-ray spectro-microscopies to characterize mesoscopic magnetic materials

Here, this article provides a brief overview of advanced magnetic X-ray spectro-microscopies that are widely used in characterizing mesoscopic magnetic materials. Common to those techniques are various X-ray magnetic dichroism effects that are used as magnetic contrast mechanism. A particular interest is to use those techniques to image the underlying microscopic spin structures in magnetic materials with high spatial resolution and to ultimately resolve their full 3D mesoscale characteristics with elemental sensitivity, as well as their ultrafast dynamics upon excitations with field and current pulses. Recent research examples using various magnetic X-ray spectro-microscopies are presented to showcase their specific features with a focus on imaging novel topological spin textures, such as vortices, skyrmions and Hopfions, which are considered as potential building blocks towards low power, high-speed and high-density magnetic devices that could transform information technologies and potentially be used in biomedical applications. Future developments with magnetic X-ray spectro-microscopies harness the full coherence of next generation X-ray sources and could open the path towards single shot imaging with spatial and temporal resolutions down to fundamental magnetic length and time scales.

36 MATERIALS SCIENCE↗
DOE OSTI2019

RetSynth: determining all optimal and sub-optimal synthetic pathways that facilitate synthesis of target compounds in chassis organisms

The efficient biological production of industrially and economically important compounds is a challenging problem. Brute-force determination of the optimal pathways to efficient production of a target chemical in a chassis organism is computationally intractable. Many current methods provide a single solution to this problem, but fail to provide all optimal pathways, optional sub-optimal solutions or hybrid biological/non-biological solutions. Here we present RetSynth, software with a novel algorithm for determining all optimal biological pathways given a starting biological chassis and target chemical. By dynamically selecting constraints, the number of potential pathways scales by the number of fully independent pathways and not by the number of overall reactions or size of the metabolic network. This feature allows all optimal pathways to be determined for a large number of chemicals and for a large corpus of potential chassis organisms. Additionally, this software contains other features including the ability to collect data from metabolic repositories, perform flux balance analysis, and to view optimal pathways identified by our algorithm using a built-in visualization module. This software also identifies sub-optimal pathways and allows incorporation of non-biological chemical reactions, which may be performed after metabolic production of precursor molecules. The novel algorithm designed for RetSynth streamlines an arduous and complex process in metabolic engineering. Our stand-alone software allows the identification of candidate optimal and additional sub-optimal pathways, and provides the user with necessary ranking criteria such as target yield to decide which route to select for target production. Furthermore, the ability to incorporate non-biological reactions into the final steps allows determination of pathways to production for targets that cannot be solely produced biologically. With this comprehensive suite of features RetSynth exceeds any open-source software or webservice currently available for identifying optimal pathways for target production.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13 C Metabolic Flux Analysis

Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13 C Metabolic Flux Analysis ( 13 C MFA) and Two-Scale 13 C Metabolic Flux Analysis (2S- 13 C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13 C MFA or 2S- 13 C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore.

13C metabolic flux analysis↗
DOE OSTI2019

iMicrobe: Tools and data-driven discovery platform for the microbiome sciences

Background: Scientists have amassed a wealth of microbiome datasets, making it possible to study microbes in biotic and abiotic systems on a population or planetary scale; however, this potential has not been fully realized given that the tools, datasets, and computation are available in diverse repositories and locations. To address this challenge, we developed iMicrobe.us, a community-driven microbiome data marketplace and tool exchange for users to integrate their own data and tools with those from the broader community. Findings: The iMicrobe platform brings together analysis tools and microbiome datasets by leveraging National Science Foundation–supported cyberinfrastructure and computing resources from CyVerse, Agave, and XSEDE. The primary purpose of iMicrobe is to provide users with a freely available, web-based platform to (1) maintain and share project data, metadata, and analysis products, (2) search for related public datasets, and (3) use and publish bioinformatics tools that run on highly scalable computing resources. Analysis tools are implemented in containers that encapsulate complex software dependencies and run on freely available XSEDE resources via the Agave API, which can retrieve datasets from the CyVerse Data Store or any web-accessible location (e.g., FTP, HTTP). Conclusions: iMicrobe promotes data integration, sharing, and community-driven tool development by making open source data and tools accessible to the research community in a web-based platform.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

DeePMD-kit v2: A software package for deep potential models

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features, such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, DP-range correction, DP long range, graphics processing unit support for customized operators, model compression, non-von Neumann molecular dynamics, and improved usability, including documentation, compiled binary packages, graphical user interfaces, and application programming interfaces. This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, this article presents a comprehensive procedure for conducting molecular dynamics as a representative application, benchmarks the accuracy and efficiency of different models, and discusses ongoing developments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

IBM Open Science Price - SWAP Gate Challenge

On November 30, 2020, IBM announced an open science price competition aimed at improving the fidelity of a SWAP gate on their 7 qubit Quantum system named ”Casablanca”. The details of the SWAP gate problem was outlined in a Jupyter notebook, which utilizes the IBM developed open source package Qiskit. The participants were allowed to work in teams with up to five people and restricted to only use open source software in solving the problem. Their results were due on April 16, 2021. Our approach to solving the SWAP gate challenge is based on the quantum optimal control techniques implemented in our open source packages Juqbox.jl and Quandary. The results from the optimal control approach is only as good as the accuracy in the description of the quantum system dynamics, characterized by a Hamiltonian model that was provided by IBM. Based on the calibrated control pulses IBM provide for the standard gate set, we developed a reverse engineering approach to calibrate our computational model. Techniques were developed to translate between Qiskit’s pulse representation and the B-spline formulation used in Juqbox and Quandary. The fidelity of the optimized pulse sequences were estimated using Qiskit’s randomized bench marking techniques.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

IsoForma: An R Package for Quantifying and Visualizing Positional Isomers in Top-Down LC-MS/MS Data

Proteoforms, the different forms of a protein with sequence variations including post-translational modifications (PTMs), execute vital functions in biological systems such as cell signaling and epigenetic regulation. Precisely defining the stoichiometry of PTMs has been challenging because, in the widely used bottom-up proteomics methods, the detection occurs at the peptide level and thus the link between peptides and their specific modification site is lost, resulting in proteoform ambiguity. Advances in top-down mass spectrometry (MS) technology have permitted the direct characterization of intact proteoforms and their exact number of modification sites, allowing for the relative quantification of positional isomers (PI). Proteins with positional isomers refers to proteoforms with identical total mass and set of modifications but varying PTM site combinations. The relative abundance of PI can be estimated by matching proteoform-specific fragment ions to top-down tandem MS (MS2) data to localize and quantify modifications. However, current approaches heavily rely on manual annotation. Here, we present IsoForma, an open-source R package for relative quantification of PI within a single tool. We benchmarked IsoForma’s performance against two existing workflows and highlight the similarity of the results and improvements in speed. Overall, IsoForma provides a streamlined process, reduces the time of conducting isoform-based analyses, and offers an essential framework for developing customized proteoform analysis workflows. Finally, the software is open source and available at https://github.com/EMSL-Computing/isoforma-lib.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A universal language for finding mass spectrometry data patterns

Despite being information rich, the vast majority of untargeted mass spectrometry data are underutilized; most analytes are not used for downstream interpretation or reanalysis after publication. The inability to dive into these rich raw mass spectrometry datasets is due to the limited flexibility and scalability of existing software tools. Here, in this study, we introduce a new language, the Mass Spectrometry Query Language (MassQL), and an accompanying software ecosystem that addresses these issues by enabling the community to directly query mass spectrometry data with an expressive set of user-defined mass spectrometry patterns. Illustrated by real-world examples, MassQL provides a data-driven definition of chemical diversity by enabling the reanalysis of all public untargeted metabolomics data, empowering scientists across many disciplines to make new discoveries. MassQL has been widely implemented in multiple open-source and commercial mass spectrometry analysis tools, which enhances the ability, interoperability and reproducibility of mining of mass spectrometry data for the research community.

Damiani, Tito [Czech Academy of Sciences (CAS), Pr↗
DOE OSTI2025

MLSPICE: Machine Learning based SPICE Modeling Platform for Power Magnetics

Electrical power converters are critical to a wide range of applications ranging from renewable integration to transportation electrification, and can be a key factor determining the size, weight, and efficiency of energy conversion systems. Magnetic components are typically the largest and least efficient components in power electronics. While there have been major strides in the modeling and analysis of power semiconductor devices and circuit simulations, the necessary advances in the design of power magnetics have lagged. In this project, we have transformed the modeling and design of power magnetics with machine learning enabled methods and catalyze simultaneous disruptive improvements for ML-based power electronics design tools. A fully automated open-source machine learning based magnetics modeling platform – the MagNet project - with innovations in full stack have been developed to greatly accelerate the design process and provide new insights to magnetic material and geometry design. The ARPA-E funded MagNet platform contains three major building blocks: 1) a ML-Integrated Data Acquisition System (MIDAS): a highly automated data acquisition testbed which is capable of measuring a large number of magnetic cores with a wide range of electrical circuit excitations; 2) a ML-integrated Core Loss Model (MICLM): a machine-learning trained modeling method for modeling the core loss and saturation effects of magnetic materials for arbitrary excitation waveforms; 3) ML-guided Magnetics SPICE Simulation Tool (PMSPICE): a fully integrated CAD tool which can simulate the magnetics in SPICE. It can help the designers to quickly model the linear and non-linear characteristics of magnetic components and evaluate their behavior in SPICE simulations. The developed MagNet system has fully demonstrated the proposed performance target and has been open sourced to the entire power electronics community to advance the modeling and design of power magnetics from many different angles.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A Software Defined Storage Approach to Exascale Storage Services (Final Technical Report)

The Mochi project began in 2015 in recognition of a set of changes that were set to alter the landscape of storage and I/O for DOE science. On the one hand, an influx of new classes of applications including an initial set of data intensive and machine learning applications were operating with new types of data and in new ways, as compared to traditional computational science simulation codes. Simultaneously, new memory, storage, and networking technologies were driving a need to adapt software designs to a rapidly changing design space. The goal of the three-year Mochi project was to develop and demonstrate an approach for composing storage services which provides new levels of functionality, performance, and reliability for science applications at extreme scale. The team, including a mix of Laboratory, university, and industry collaborators, focused on systems and applications of interest to DOE Office of Science, tracking developments at NERSC and the LCFs as well as application trends within ECP and SciDAC. Technical work focused on three themes: composition (understand the building blocks needed to provide storage services that meet DOE mission needs and how these components are flexibly composed), application interfacing (what service compositions are most effective for common DOE application use cases), and consistency and synchronization (taking advantage of knowledge about, and from, the system and scientific applications to provide better service than would otherwise be possible). The project was highly successful, producing 13 publications and tens of thousands of lines of open source software that is now being used by multiple ECP teams, HPC software vendors, and international collaborators. The uptake of Mochi technologies speaks to both the value of the service composition model and the quality of architecture and software developed by the team. Through continued funding under ASCR and ECP, the Mochi team and its technologies are on track to further contribute to DOE science successes into the exascale timeframe.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

CACTUS: Chemistry Agent Connecting Tool Usage to Science

Large language models (LLMs) have shown remarkable potential in various domains but often lack the ability to access and reason over domain-specific knowledge and tools. In this article, we introduce Chemistry Agent Connecting Tool-Usage to Science (CACTUS), an LLM-based agent that integrates existing cheminformatics tools to enable accurate and advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama3-8b, and Mistral-7b, on a benchmark of thousands of chemistry questions. Our results demonstrate that CACTUS significantly outperforms baseline LLMs, with the Gemma-7b, Mistral-7b, and Llama3-8b models achieving the highest accuracy regardless of the prompting strategy used. Moreover, we explore the impact of domain-specific prompting and hardware configurations on model performance, highlighting the importance of prompt engineering and the potential for deploying smaller models on consumer-grade hardware without a significant loss in accuracy. By combining the cognitive capabilities of open-source LLMs with widely used domain-specific tools provided by RDKit, CACTUS can assist researchers in tasks such as molecular property prediction, similarity searching, and drug-likeness assessment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

AI for Earthquake Physics

The core LANL program sponsored by Office of Science, Basic Energy Science, Chemical Sciences, Geosciences, and Biosciences (DOE-BES-CSGB) and led by PI Johnson aims to research earthquake faults to advance fault physics and earthquake hazards. All work completed is required to be made publicly available through publications and open-source codes supporting the published results. All routines are/will-be written in open source python and applied to publicly available data sets. These routines will format data from input into models, develop and test modeling frameworks for the problems addressed, and produce figures applicable to peer-reviewed manuscripts. All work is reviewed for Los Alamos Unlimited Release before submitting to a journal. This summary encompasses recently completed work and work to be complete for the duration of the program.

Johnson, Christopher↗
DOE OSTI2026

FY25 Electric Grid Security Annual Report

Sandia’s Electric Grid Security program advances a national vision of energy dominance and accessibility, while applying our national security -emphasis on ensuring of a secure, resilient, and affordable electric system for all users. Our achievements reflect a strategic approach combining technology development; modeling, simulation, and data analytics; and partnered demonstrations and outreach to further the adoption of advanced grid and storage technologies. Our FY25 efforts leverage the strengths of our partnerships—spanning Sandia’s core science and technology competencies as well as external technology leaders—to develop the solutions today which enable the grid of tomorrow. Key accomplishments in this report that support our strategy span our technical program areas and include: • New open-source analytical tools for systems -level planning and optimization, including significant advances to the QuESt analytical environment; • Further advancement of artificial intelligence and machine learning to enhanced grid operations and planning as we rise to the challenge of new large loads; • Development of solid-state power conversion technologies and a new medium-voltage research lab; • New technologies to assess wildfire vulnerabilities and mitigate potential impacts; • Advanced applications of new cybersecurity technologies with industry partners; • Contributions to understanding the impacts of electromagnetic pulses and geomagnetic disturbances on grid components; and • Digital twin development for hybrid microgrids with multiple generators, storage, and loads. This report indicates key areas of research and engagement and summarizes the impact of Sandia’s contributions through notable accomplishments, journal publications, patents, and technical conferences and presentations. It is provided with the hope that readers discover ways we can further team to create our modern grid and apply the outcomes of our efforts. The bulk of work described herein is funded by several offices within the U.S. Department of Energy (USDOE), including the Office of Electricity (OE); Cybersecurity, Energy Security, and Emergency Response (CESER); former offices such as the Office of Energy Efficiency and Renewable Energy (EERE), the Grid Deployment Office (GDO), the Office of Clean Energy Demonstrations (OCED), and other key programs at USDOE. As we continue to state in these annual reports, the contributors to our successes are too numerous to name here, though our team wishes to express our deep gratitude to the numerous program and project sponsors at the US Department of Energy, who often function equally as technical collaborators; our many partners in industry, academia, utilities, and other national labs; and fellow researchers and business partners at Sandia whose leadership and creativity have enabled the accomplishments described herein.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

FY26 Progress Report on the Operation of the Activated Materials Laboratory at the Advanced Photon Source as a Nuclear Science User Facilities Partner Facility

The Activated Materials Laboratory (AML), located in the Long Beamline Building of the Advanced Photon Source (APS) at Argonne National Laboratory (ANL), provides a centralized radiological capability for preparing, handling, and supporting synchrotron experiments on activated materials. As a Nuclear Science User Facilities (NSUF) partner facility, the AML enables the receipt of radioactive shipments, open-form sample handling, encapsulation, transport of specimens to and from APS beamlines, and experimentation with dedicated equipment. The partnership includes the APS 1-ID and 20-ID beamlines, which provide high-energy x-ray scattering, tomography, and diffraction microscopy techniques for ex-situ, in-situ, and grainresolved three-dimensional (3D) characterization. All samples supported through the AML and partner beamlines must meet radiological limits of less than 100 mrem/h at 30 cm. This FY26 progress report summarizes the first full year of AML operations as an NSUF partner facility and highlights progress in both user support and capability development. By the end of FY26, the AML had received a total of 10 NSUF-awarded projects, including 2 Consolidated Innovative Nuclear Research (CINR), 7 Rapid Turnaround Experiment (RTE), and 1 Super RTE projects. Beamtime was fully delivered for 3 RTE projects and partially delivered for 1 CINR project, demonstrating successful workflows for receipt, encapsulation, beamline transfer, and radiological experiment execution. These efforts demonstrated safe radiological experiments at APS beamlines for samples with dose rates above the historical 5 mrem/h threshold and now up to 100 mrem/h at 30 cm, marking an important milestone for neutron-irradiated materials research. During FY26, the AML also expanded its experimental capabilities. A Psylotech xTS load frame with in-grip rotation was deployed for room-temperature mechanical testing with threedimensional x-ray characterization during interrupted loading. A Linkam TS1500V vacuum heater was commissioned for thermally driven studies, and temperature calibration experiments were performed to establish specimen-relevant thermal profiles. In parallel, a customized split-tube furnace for high-temperature mechanical testing entered commissioning, and initial work began on robotic sample handling to reduce worker dose and improve operational efficiency. Data workflow and beamline operations continued to mature. Together, these developments demonstrate that the AML is becoming a unique national resource for safe, efficient, and scientifically advanced characterization of activated materials at the APS.

Zhang, Xuan↗