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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Linear shaped-charge jet optimization using machine learning methods

Linear shaped charges are used to focus energy into rapidly creating a deep linear incision. The general design of a shaped charge involves detonating a confined mass of high explosive (HE) with a metal-lined concave cavity on one side to produce a high velocity jet for the purpose of striking and penetrating a given material target. This jetting effect occurs due to the interaction of the detonation wave with the cavity geometry, which produces an unstable fluid phenomenon known as the Richtmyer–Meshkov instability and results in the rapid growth of a long narrow jet. We apply machine learning and optimization methods to hydrodynamics simulations of linear shaped charges to improve the simulated jet characteristics. The designs that we propose and investigate in this work generally involve modifying the behavior of the detonation waves prior to interaction with the liner material. These designs include the placement of multiple detonators and the use of metal inclusions within the HE. In conclusion, we are able to produce a linear shaped-charge design with a higher penetration depth than the baseline case that we consider and accomplish this using the same amount of or less HE.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Scattering-based structural inversion of soft materials via Kolmogorov–Arnold networks

Small-angle scattering techniques are indispensable tools for probing the structure of soft materials. However, traditional analytical models often face limitations in structural inversion for complex systems, primarily due to the absence of closed-form expressions of scattering functions. To address these challenges, we present a machine learning framework based on the Kolmogorov–Arnold Network (KAN) for directly extracting real-space structural information from scattering spectra in reciprocal space. This model-independent, data-driven approach provides a versatile solution for analyzing intricate configurations in soft matter. By applying the KAN to lyotropic lamellar phases and colloidal suspensions—two representative soft matter systems—we demonstrate its ability to accurately and efficiently resolve structural collectivity and complexity. Here, our findings highlight the transformative potential of machine learning in enhancing the quantitative analysis of soft materials, paving the way for robust structural inversion across diverse systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Ultrafast two-photon fluorescence imaging of cerebral blood circulation in the mouse brain in vivo

Characterizing blood flow dynamics in vivo is critical to understanding the function of the vascular network under physiological and pathological conditions. Existing methods for hemodynamic imaging have insufficient spatial and temporal resolution to monitor blood flow at the cellular level in large blood vessels. By using an ultrafast line-scanning module based on free-space angular chirped enhanced delay, we achieved two-photon fluorescence imaging of cortical blood flow at 1,000 two-dimensional (2D) frames and 1,000,000 one-dimensional line scans per second in the awake mouse. This orders-of-magnitude increase in temporal resolution allowed us to measure cerebral blood flow at up to 49 mm/s and observe pulsatile blood flow at harmonics of heart rate. Directly visualizing red blood cell (RBC) flow through vessels down to >800 µm in depth, we characterized cortical layer–dependent flow velocity distributions of capillaries, obtained radial velocity profiles and kilohertz 2D velocity mapping of multifile blood flow, and performed RBC flux measurements from penetrating blood vessels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Multi-fidelity learning for interatomic potentials: low-level forces and high-level energies are all you need

The promise of machine learning interatomic potentials (MLIPs) has led to an abundance of public quantum mechanical (QM) training datasets. The quality of an MLIP is directly limited by the accuracy of the energies and atomic forces in the training dataset. Unfortunately, most of these datasets are computed with relatively low-accuracy QM methods, e.g. density functional theory with a moderate basis set. Due to the increased computational cost of more accurate QM methods, e.g. coupled-cluster theory with a complete basis set (CBS) extrapolation, most high-accuracy datasets are much smaller and often do not contain atomic forces. The lack of high-accuracy atomic forces is quite troubling, as training with force data greatly improves the stability and quality of the MLIP compared to training to energy alone. Because most datasets are computed with a unique level of theory, traditional single-fidelity (SF) learning is not capable of leveraging the vast amounts of published QM data. In this study, we apply multi-fidelity learning (MFL) to train an MLIP to multiple QM datasets of different levels of accuracy, i.e. levels of fidelity. Specifically, we perform three test cases to demonstrate that MFL with both low-level forces and high-level energies yields an extremely accurate MLIP—far more accurate than a SF MLIP trained solely to high-level energies and almost as accurate as a SF MLIP trained directly to high-level energies and forces. Therefore, MFL greatly alleviates the need for generating large and expensive datasets containing high-accuracy atomic forces and allows for more effective training to existing high-accuracy energy-only datasets. Indeed, low-accuracy atomic forces and high-accuracy energies are all that are needed to achieve a high-accuracy MLIP with MFL.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Compensation of Anisotropy in Spin Hall Devices for Neuromorphic Applications

Spintronic nano-oscillators and diodes with reduced nonlinearity benefit from low phase noise and improved device properties. Moreover, they could offer key advantages for realizing neuromorphic applications, such as spike-based neurons and frequency multiplexing in neural networks. Here, in this work, we experimentally demonstrate the reduction in nonlinearity of a spin Hall nano-oscillator (SHNO) by compensation of its effective magnetic anisotropy. The study involves optimization of Co/Ni multilayer growth to achieve the compensation, followed by spin-diode measurements on patterned microstrips to quantify their anisotropy. The relationship between the second- (H k 2 = 0.47 mT) and first-order ($H^{eff}_{k_1}$ = -0.8 mT) anisotropy fields reveals the existence of an easy cone, thereby validating the presence of compensation. Furthermore, we demonstrate a compensated spin diode that has a fixed frequency when the input power is varied. We then study the current-induced auto-oscillation properties of SHNOs on compensated films by patterning nanoconstrictions of 200 and 100 nm wide. The invariance of the resonance frequency and linewidth of the compensated SHNO with applied dc current indicates the absence of nonlinearity. This independence is maintained irrespective of the applied external fields and their orientations. The compensated SHNO obtained has a linewidth of 1.1 MHz and a peak output power of up to 1 pW/MHz.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Flow-Driven Branching in a Frangible Porous Medium

Channel formation and branching is widely seen in physical systems where movement of fluid through a porous structure causes the spatiotemporal evolution of the medium. We provide a simple theoretical framework that embodies this feedback mechanism in a multiphase model for flow through a frangible porous medium with a dynamic permeability. Numerical simulations of the model show the emergence of branched networks whose topology is determined by the geometry of external flow forcing. Furthermore, this allows us to delineate the conditions under which splitting and/or coalescing branched network formation is favored, with potential implications for both understanding and controlling branching in soft frangible media.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Nuclear Responses with Neural-Network Quantum States

We introduce a variational Monte Carlo framework that combines neural-network quantum states with the Lorentz integral transform technique to compute the dynamical properties of self-bound quantum many-body systems in continuous Hilbert spaces. While broadly applicable to various quantum systems, including atoms and molecules, in this initial application we focus on the photoabsorption cross section of light nuclei, where benchmarks against numerically exact techniques are available. Our accurate theoretical predictions are complemented by robust uncertainty quantification, enabling meaningful comparisons with experiments. Here, we demonstrate that a relatively simple nuclear Hamiltonian—based on a leading-order pionless EFT expansion and known to accurately reproduce ground-state energies of nuclei with 𝐴 ≤ 40—also provides a reliable description of the photoabsorption cross section.

Ab initio calculations↗
DOE OSTI2022

Use of Event-Time Embeddings via RNN to Discern Novel Event Sequences in EHRs

In highly configurable health information technology (HIT) systems, such as VistA of the Veterans Health Administration, the variations in how the system is used among different healthcare facilities and how the data are recorded can be significant. Despite the successful standardization of care efforts, some of these variations can be indicative of HIT hazards and demand further investigation. In this work, we implemented a recurrent neural network (RNN) architecture to learn clinical provider order sequences and their temporal dynamics while predicting the orders' terminal state. We demonstrate model performance and provide a use case for the model discerning novel event sequences. This model is proposed to find novel event sequences in an operational environment.

Ozmen, Ozgur↗
DOE OSTI2023

Clustering Acoustic Background Noise in the Stratosphere Using Machine Learning

Infrasound, characterized by low-frequency sound inaudible to humans (<20 Hz), emanates from natural and anthropogenic sources. Its efficacy for monitoring phenomena necessitates robust sensing networks. Traditional ground-based infrasound sensors have limitations due to atmospheric dynamics and noise interference. Balloon-bore sensors have emerged as an alternative, offering reduced noise and improved capabilities. This study bridges clustering algorithms with balloon borne infrasound data, a domain yet to be explored. Employing K-Means, DBSCAN, and GMM algorithms on normalized and reshaped data and only normalized data from a New Zealand-based NASA balloon flight, insights into background noise at stratospheric altitudes were revealed. Despite challenges arising from distinguishing signals amid unique background noise, this research provides vital reference material for noise analysis and calibration. Beyond infrasound event capture, the dataset enriches comprehension of background noise characteristics in the southern hemisphere.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Zero Trust Cybersecurity: Concepts and Models for Application

Zero Trust is a cybersecurity paradigm centered on the idea that a network breach is inevitable and so no user or asset should be implicitly trusted. Entities on the network are continuously monitored and access-granting decisions are based on dynamic risk assessment using multiple inputs. To limit the damage from an attack, privileges and lateral access are constrained by default. This report provides an overview of current models and constructs employed in building out these concepts into a zero trust architecture.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Surrogate Modeling of Landau Damping with Deep Operator Networks

Kinetic simulations excel at capturing microscale plasma physics phenomena with high accuracy, but their computational demands make them impractical for modeling large-scale space and astrophysical systems. In this context, we build a surrogate model, using Deep Operator Networks (DeepONets), based upon the Vlasov–Poisson simulation data to model the dynamical evolution of plasmas, focusing on the Landau damping process—a fundamental kinetic phenomenon in space and astrophysical plasmas. The trained DeepONets are able to capture the evolution of electric field energy in both linear and nonlinear regimes under various conditions. Extensive validation highlights DeepONets’ robust performance in reproducing complex plasma behaviors with high accuracy, paving the way for large-scale modeling of space and astrophysical plasmas.

plasma astrophysics↗
DOE OSTI2025

Nuclear responses with neural-network quantum states

We introduce a variational Monte Carlo framework that combines neural-network quantum states with the Lorentz integral transform technique to compute the dynamical properties of self-bound quantum many-body systems in continuous Hilbert spaces. While broadly applicable to various quantum systems, including atoms and molecules, in this initial application we focus on the photoabsorption cross section of light nuclei, where benchmarks against numerically exact techniques are available. Our accurate theoretical predictions are complemented by robust uncertainty quantification, enabling meaningful comparisons with experiments. We demonstrate that a simple nuclear Hamiltonian, based on a leading-order pionless effective field theory expansion and known to accurately reproduce the ground-state energies of nuclei with $A\leq 20$ nucleons also provides a reliable description of the photoabsorption cross section.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems

We develop data-driven methods for incorporating physical information for priors to learn parsimonious representations of nonlinear systems arising from parameterized PDEs and mechanics. Our approach is based on Variational Autoencoders (VAEs) for learning nonlinear state space models from observations. We develop ways to incorporate geometric and topological priors through general manifold latent space representations. We investigate the performance of our methods for learning low dimensional representations for the nonlinear Burgers equation and constrained mechanical systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Direct Study of Changes in Catalyst Structure-Kinetic Properties During Redox Transitions

The Temporal Analysis of Products (TAP) pulse response methodology is a transient technique that provides the time resolution needed to deconvolve reaction steps from the complex networks typical in industrial catalytic processes. Traditionally, TAP measurements observe gas phase dynamics at the reactor exit but lack direct measurements of changes in the catalyst itself. Recently, a new operando technique was developed that couples gas phase transients to dynamic changes in metal centers with the precise TAP methodology for nanomole titration. Using an industrial CrOx/Al2O3 catalyst used for propane dehydrogenation, we demonstrate the capabilities of this unique device to reveal key catalytic processes: 1) total propane oxidation not associated with chromia centers, 2) reduction of Cr6+ to Cr3+ correlated with selective product formation, and 3) subsequent carbon accumulation. By utilizing incremental pulsing in a diffusion-only transport regime, the spectrokinetic device allows us to resolve detailed changes in catalyst structure, composition, and kinetic function that are otherwise indistinguishable in conventional operando devices. The unification of the TAP methodology with time-resolved spectroscopic measurements offers new and unique insights into the complex kinetic phenomena regulated by solid catalyst surfaces.

03 - NATURAL GAS↗
DOE OSTI2025

Machine Learning for Well Log Analysis in Uranium Mining

This project explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate well log analysis for uranium mining. Geophysical log data—spontaneous potential, resistivity, and gamma ray—were used to classify lithology, correlate well logs and identify roll front zonation patterns, which are critical for locating uranium ore bodies. Supervised ML algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Random Forest were trained to classify lithology with high accuracy. Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks were also used for role front zone identification. Moreover, a Fast Dynamic Time Warping (FastDTW) algorithm was employed for well log correlation. Additionally, sample lag was addressed using dynamic programming. Results demonstrate the potential of AI and ML to streamline well log analysis and enhance uranium exploration workflows.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Dynamic conformational switching underlies TFIIH function in transcription and DNA repair and impacts genetic diseases

Transcription factor IIH (TFIIH) is a protein assembly essential for transcription initiation and nucleotide excision repair (NER). Yet, understanding of the conformational switching underpinning these diverse TFIIH functions remains fragmentary. TFIIH mechanisms critically depend on two translocase subunits, XPB and XPD. To unravel their functions and regulation, we build cryo-EM based TFIIH models in transcription- and NER-competent states. Using simulations and graph-theoretical analysis methods, we reveal TFIIH’s global motions, define TFIIH partitioning into dynamic communities and show how TFIIH reshapes itself and self-regulates depending on functional context. Our study uncovers an internal regulatory mechanism that switches XPB and XPD activities making them mutually exclusive between NER and transcription initiation. By sequentially coordinating the XPB and XPD DNA-unwinding activities, the switch ensures precise DNA incision in NER. Mapping TFIIH disease mutations onto network models reveals clustering into distinct mechanistic classes, affecting translocase functions, protein interactions and interface dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A density-functional theory study of the Al/AlOx/Al tunnel junction

The aluminum oxide tunnel junction is a key component of the majority of superconducting quantum devices. For high-quality, reproducible, and scalably manufacturable qubits, the ability to fabricate Josephson junctions (JJs) with a targeted critical current and high uniformity is essential. In this study, we use first-principles modeling to assess fundamental aspects of the atomic structure of both amorphous and crystalline aluminum oxide tunnel junctions and relate the structure to predicted performance metrics. We use modified ab initio molecular dynamics to develop realistic models of the tunnel junction, from which interface roughness and local thickness fluctuations are analyzed in an unbiased manner by training a neural network to identify the boundary between metal and oxide. We show that the effective thickness of the insulating part of the junction can be different from the apparent physical thickness. We calculate the rate of Cooper pair tunneling for the atomically resolved electrostatic potential using direct numerical solution in 3D, which shows a channeling effect that impacts the junction critical current. The predicted critical current is a useful JJ design parameter that can be accessed from the ab initio calculations without fitting parameters. To assess the limits of uniformity and fabrication choices (e.g., oxidation vs epitaxy), we compare the amorphous junctions to crystalline models, which show order of magnitude more efficient tunneling compared to the amorphous case, underlining the connection between atomistic structure and Cooper pair tunneling efficiency. Further, this work provides a foundation for ab initio materials design and evaluation to help accelerate future development of improved tunnel junctions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Microgrid Modeling for Stability Analysis

Here this document is a summary of a report prepared by the IEEE PES Task Force (TF) on Microgrid (MG) Dynamic Modeling, IEEE Power and Energy Society, Tech. Rep. PES-TR106, 2023. In this paper, the major issues and challenges in microgrid modeling for stability analysis are discussed, and a review of state-of-the-art modeling approaches and trends is presented. In the context of the IEEE 1547 standard, the document covers issues associated with component models for MG dynamic studies and simulations, including generator and grid modeling, full and average converter models, unbalanced and balanced system conditions, dynamic and static loads, protection requirements, and detailed and simplified controls considering communications delays, packet losses, and security issues. Considering the future integration of grids and MGs to form broad integrated networks, a discussion is presented of the use of phasor vis-à-vis electromagnetic transient simulation tools for MG dynamic stability studies, as well as modeling scale-up issues and MG equivalent models. Specifically white-, grey-, and black-box models, are presented. This TF paper and companion report constitute a modeling guide for R&D groups working on developments and standards of MGs with a focus on stability issues.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗