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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Spin–Phonon Coupling in Ferromagnetic Monolayer Chromium Tribromide

Novel 2D magnets exhibit intrinsic electrically tunable magnetism down to the monolayer limit, which has significant value for nonvolatile memory and emerging computing device applications. In these compounds, spin–phonon coupling (SPC) typically plays a crucial role in magnetic fluctuations, magnon dissipation, and ultimately establishing long-range ferromagnetic order. However, a systematic understanding of SPC in 2D magnets that combines theory and experiment is still lacking. Here in this work, monolayer chromium tribromide is studied to investigate SPC in 2D magnets via Raman spectroscopy and first principle calculations. The experimental Curie temperature and phonon shifts are found to be in good agreement with the numerical simulations. Specifically, it is demonstrated how magnetic exchange interactions affect phonon vibrations, which helps establish design fundamentals for 2D magnetic materials and other related devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Sulfurization Engineering of One–Step Low–Temperature MoS 2 and WS 2 Thin Films for Memristor Device Applications

2D materials have been of considerable interest as new materials for device applications. Non-volatile resistive switching applications of MoS 2 and WS 2 have been previously demonstrated; however, these applications are dramatically limited by high temperatures and extended times needed for the large-area synthesis of 2D materials on crystalline substrates. The experimental results demonstrate a one-step sulfurization method to synthesize MoS 2 and WS 2 at 550 °C in 15 min on sapphire wafers. Furthermore, a large area transfer of the synthesized thin films to SiO 2 /Si substrates is achieved. Following this, MoS 2 and WS 2 memristors are fabricated that exhibit stable non-volatile switching and a satisfactory large on/off current ratio (10 3 –10 5 ) with good uniformity. Tuning the sulfurization parameters (temperature and metal precursor thickness) is found to be a straightforward and effective strategy to improve the performance of the memristors. Furthermore, the demonstration of large-scale MoS 2 and WS 2 memristors with a one-step low-temperature sulfurization method with simple strategy to tuning can lead to potential applications such as flexible memory and neuromorphic computing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Engineering of Grain Boundaries in CeO 2 Enabling Tailorable Resistive Switching Properties

Abstract Defect engineering in valence change memories aimed at tuning the concentration and transport of oxygen vacancies are studied extensively, however mostly focusing on contribution from individual extended defects such as single dislocations and grain boundaries. In this work, the impact of engineering large numbers of grain boundaries on resistive switching mechanisms and performances is investigated. Three different grain morphologies, that is, “random network,” “columnar scaffold,” and “island‐like,” are realized in CeO 2 thin films. The devices with the three grain morphologies demonstrate vastly different resistive switching behaviors. The best overall resistive switching performance is shown in the devices with “columnar scaffold” morphology, where the vertical grain boundaries extending through the film facilitate the generation of oxygen vacancies as well as their migration under external bias. The observation of both interfacial and filamentary switching modes only in the devices with a “columnar scaffold” morphology further confirms the contribution from grain boundaries. In contrast, the “random network” or “island‐like” structures result in excessive or insufficient oxygen vacancy concentration migration paths. The research provides design guidelines for grain boundary engineering of oxide‐based resistive switching materials to tune the resistive switching performances for memory and neuromorphic computing applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Earth mover’s distance as a measure of CP violation

We introduce a new unbinned two sample test statistic sensitive to CP violation utilizing the optimal transport plan associated with the Wasserstein (earth mover’s) distance. The efficacy of the test statistic is shown via two examples of CP asymmetric distributions with varying sample sizes: the Dalitz distributions of B 0 → K + π – π 0 and of D 0 → π + π – π 0 decays. The windowed version of the Wasserstein distance test statistic is shown to have comparable sensitivity to CP violation as the commonly used energy test statistic, but also retains information about the localized distributions of CP asymmetry over the Dalitz plot. For large statistic datasets we introduce two modified Wasserstein distance based test statistics — the binned and the sliced Wasserstein distance statistics, which show comparable sensitivity to CP violation, but improved computing time and memory scalings. Finally, general extensions and applications of the introduced statistics are discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects

Simulation-based approaches to microstructure generation can suffer from a variety of limitations, such as high memory usage, long computational times, and difficulties in generating complex geometries. Generative machine learning models present a way around these issues, but they have previously been limited by the fixed size of their generation area. Here, we present a new microstructure generation methodology leveraging advances in inpainting using denoising diffusion models to overcome this generation area limitation. We show that microstructures generated with the presented methodology are statistically similar to grain structures generated with a kinetic Monte Carlo simulator, SPPARKS.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Implementation of Triply Periodic Minimal Surfaces (TPMS) as surface objects in OpenMC

Triply Periodic Minimal Surfaces (TPMS) represent a promising geometry for future fuel designs due to their significant surface-to-volume ratio, which facilitates efficient cooling of nuclear fuel, a crucial factor for safety and efficiency. Demonstrating the remarkable capabilities of TPMS fuel requires initial modeling and simulation. This paper presents an implementation of TPMS in the Monte Carlo code OpenMC, enabling reactor physics modeling of TPMS. Here, the primary advantages over traditional methods using CAD files include reduced memory requirements for computations and high-fidelity implementation. This implementation has been tested against CAD files loaded in Serpent2, yielding promising results with low biases in the $k_{\textrm{eff}}$, comparable to biases in the material balance sheet. The implementation presented in this work will be used in future reactor physics computations related to new reactor designs involving TPMS-based fuels.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Physics-informed latent neural operator for real-time predictions of time-dependent parametric PDEs

Deep operator network (DeepONet) has shown significant promise as surrogate models for systems governed by partial differential equations (PDEs), enabling accurate mappings between infinite-dimensional function spaces. However, when applied to systems with high-dimensional input-output mappings arising from large numbers of spatial and temporal collocation points, these models often require heavily overparameterized networks, leading to long training times. Latent DeepONet addresses some of these challenges by introducing a two-step approach: first learning a reduced latent space using a separate model, followed by operator learning within this latent space. While efficient, this method is inherently data-driven and lacks mechanisms for incorporating physical laws, limiting its robustness and generalizability in data-scarce settings. Here, in this work, we propose PI-Latent-NO, a physics-informed latent neural operator framework that integrates governing physics directly into the learning process. Our architecture features two coupled DeepONets trained end-to-end: a Latent-DeepONet that learns a low-dimensional representation of the solution, and a Reconstruction-DeepONet that maps this latent representation back to the physical space. By embedding PDE constraints into the training via automatic differentiation, our method eliminates the need for labeled training data and ensures physics-consistent predictions. The proposed framework is both memory and compute-efficient, exhibiting near-constant scaling with problem size and demonstrating significant speedups over traditional physics-informed operator models. We validate our approach on a range of parametric PDEs, showcasing its accuracy, scalability, and suitability for real-time prediction in complex physical systems.

Latent representations↗
DOE OSTI2025

An investigation on machine learning predictive accuracy improvement and uncertainty reduction using VAE-based data augmentation

The confluence of ultrafast computers with large memory, rapid progress in Machine Learning (ML) algorithms, and the availability of large datasets place multiple engineering fields at the threshold of dramatic progress. However, a unique challenge in nuclear engineering is data scarcity because experimentation on nuclear systems is usually more expensive and time-consuming than most other disciplines. One potential way to resolve the data scarcity issue is deep generative learning, which uses certain ML models to learn the underlying distribution of existing data and generate synthetic samples that resemble the real data. In this way, one can significantly expand the dataset to train more accurate predictive ML models. In this study, our objective is to evaluate the effectiveness of data augmentation using variational autoencoder (VAE)-based deep generative models. We investigated whether the data augmentation leads to improved accuracy in the predictions of a deep neural network (DNN) model trained using the augmented data. Additionally, the DNN prediction uncertainties are quantified using Bayesian Neural Networks (BNN) and conformal prediction (CP) to assess the impact on predictive uncertainty reduction. To test the proposed methodology, we used TRACE simulations of steady-state void fraction data based on the NUPEC Boiling Water Reactor Full-size Fine-mesh Bundle Test (BFBT) benchmark. Here, we found that augmenting the training dataset using VAEs has improved the DNN model’s predictive accuracy, improved the prediction confidence intervals, and reduced the prediction uncertainties.

Bayesian neural network↗
DOE OSTI2025

Imaging Domain Walls in van der Waals Ferroelectrics Using Tip-Enhanced Second Harmonic Generation

van der Waals ferroelectrics have captured significant interest for applications in novel memory and neuromorphic computing devices. Within these materials, interfaces between regions of different ferroelectric polarizations (i.e., domain walls) exhibit distinctive behaviors. Characterization of these behaviors remains a challenge, as domain wall thickness resides at the nanoscale. Using tip-enhanced second harmonic generation (TESHG), we demonstrate nanoscale imaging of domain walls in 2D ferroelectric α-In 2 Se 3 . With a narrow-band near-IR laser, we take advantage of high-wavelength, off-resonant signal enhancement to generate robust and reproducible TESHG. In conclusion, a 16 nm spatial resolution is achieved, and we identify spectral features suggesting that nonlocal effects from domain walls persist nanometers into adjacent domains.

Mantilla, Alexander B. C. [Washington State Univ.,↗
DOE OSTI2023

Nonvolatile Memristive Effect in Few-Layer CrI 3 Driven by Electrostatic Gating

The potential of memristive devices for applications in nonvolatile memory and neuromorphic computing has sparked considerable interest, particularly in exploring memristive effects in two-dimensional (2D) magnetic materials. However, the progress in developing nonvolatile, magnetic field-free memristive devices using 2D magnets has been limited. In this work, we report an electrostatic-gating-induced nonvolatile memristive effect in CrI 3 -based tunnel junctions. The few-layer CrI 3 -based tunnel junction manifests notable hysteresis in its tunneling resistance as a function of gate voltage. We further engineered a nonvolatile memristor using the CrI 3 tunneling junction with low writing power and at zero magnetic field. We show that the hysteretic transport observed is not a result of trivial effects or inherent magnetic properties of CrI 3 . We propose a potential association between the memristive effect and the newly predicted ferroelectricity in CrI 3 via gating-induced Jahn–Teller distortion. Furthermore, our work illuminates the potential of 2D magnets in developing next-generation advanced computing technologies.

2D magnet↗
DOE OSTI2025

Probing the Effects of the First Atomic Layer on the Dynamic Behavior of Sub-2 nm MgO/Al 2 O 3 Memristors

As electronic devices continue to scale down from the current sub-5 nm range, atomic-scale control of defects becomes increasingly crucial to suppressing their impact on the physical properties of the devices. Memristors present an excellent example as a nonlinear and dynamic device with high speed and endurance required for electronic applications ranging from neuromorphic computing to nonvolatile memories. Herein we investigate the impact of atomic defects in sub-2 nm thick MgO/Al 2 O 3 atomic layer stack (ALS) memristors that use an M1 (switching layer)/M2 (oxygen vacancy reservoir layer) bilayer structure grown using in vacuo atomic layer deposition (iALD). Intriguingly, we revealed a direct correlation of the atomic defects in the M2 layer with the memristor dynamic behavior using a combined analysis of in situ scanning tunneling spectroscopy (iSTS) on the M2 layer and ex situ characterization on the memristors. Specifically, incomplete coverage of the 1st ALD atomic layer of M2 on the electrode yields defects at the M2/electrode interface. Despite the monotonic increase of ALD coverage, by almost three-fold from ~30% to >90%, at completion of the M2 layer of ~ 0.7 nm in thickness, the impact of the defects on the M2/electrode interface has been found detrimental to both memristor switching speed and endurance. Guided by atomistic simulation, we addressed the issue of interface defects via tuning of the Al surface hydroxylation to increase the first atomic layer ALD coverage to ~75%, leading to improved memristor switching speed and endurance by several orders of magnitude. In conclusion, these findings shed light on the correlation between the atomic defects and the dynamic behavior of sub-2 nm memristors and the importance of minimizing the atomic defects in memristors for future electronic applications.

Atomic Layer Deposition↗
DOE OSTI2023

Nanoscale Surface Structure of Nanometer-Thick Ferroelectric BaTiO 3 Films Revealed by Synchrotron X-ray Scanning Tunneling Microscopy: Implications for Catalytic Adsorption Reactions

Ferroelectric nanomaterials are of interest in catalysis, non-volatile memory, and neuromorphic computing among other applications because of their switchable structure that can alter electronic and interface properties of a single material. The investigation of the role of polarization on the surface structure and chemistry of ferroelectric nanomaterials is a longstanding challenge, as it ideally requires a combination of both nanoscale imaging and chemical spectroscopy. In this work we study a model ferroelectric BaTiO 3 thin film by synchrotron X-ray scanning tunneling microscopy (SX-STM), a unique method which integrates nanoscale surface imaging and chemically sensitive spectroscopy. We find that polarization switching from downward to upward in (001)single crystalline BaTiO 3 thin films increases the intensity of X-ray absorption across Ba M, Ti L and O K edges. Chemical mapping of nanometer sized domains further demonstrates the modulation of surface structures upon polarization switching, as well as confirming the trends observed in single point experiments across the surface. Here, we complement these measurements with ab-initio computational absorption spectroscopy to elucidate the effect of polarization switching on the core-hole excitations using the Bethe-Salpeter equation approach. Our experimental and theoretical results thus confirm a stronger binding strength for the upward polarized surface with molecular O 2 as a model reactant, offering mechanistic evidence that supports previous reports. This work advances the understanding of the surface chemistry and electronic structure of ferroelectrics, which can ultimately aid strategies to design interfaces with tailored properties.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Curvature Induced Modifications of Chirality and Magnetic Configuration in Perpendicular Films

Designing curvature in three-dimensional (3D) magnetic nanostructures enables controlled manipulation of local energy landscapes, allowing for the modification of noncollinear spin textures relevant for next-generation spintronic devices. In this study, we experimentally investigate 3D magnetization textures in a Co/Pd multilayer film, exhibiting strong perpendicular magnetic anisotropy (PMA), deposited onto curved Cu nanowire meshes with diameters as small as 50 nm and lengths of several microns. Utilizing magnetic soft X-ray nanotomography, we achieve reconstructions of 3D magnetic domain patterns at approximately 30 nm spatial resolution. This approach provides detailed information on both the orientation and magnitude of magnetization within the film. Our results reveal that interfacial anisotropy in the Co/Pd multilayers drives the magnetization toward the local surface normal. In contrast to typical labyrinth domains observed in planar films, the presence of curved nanowires significantly alters the domain structure, with domains preferentially aligning along the nanowire axis in close proximity, while adopting random orientations farther away. We report direct experimental observation of a curvature-induced Dzyaloshinskii-Moriya interaction (DMI), which is quantified to be approximately one-third of the intrinsic DMI in Co/Pd stacks. The curvature induced DMI enhances stability of Néel-type domain walls. These experimental observations are further supported by micromagnetic simulations. Altogether, our findings demonstrate that introducing curvature into magnetic nanostructures provides a powerful strategy for tailoring complex magnetic behaviors, paving the way for the design of advanced 3D racetrack memory and neuromorphic computing devices.

Raftrey, David↗
DOE OSTI2021

Exciton dissociation in quantum dots connected with photochromic molecule bridges

Here, we report modulation of exciton dissociation dynamics in quantum dots (QD) connected with photochromic molecules. Our results show that switching the configuration of photochromic molecules changes the inter-QD potential barrier height which has a major impact on the charge tunnelling and exciton dissociation. The switching of the dominant exciton decay pathway between the radiative recombination and exciton dissociation results in switchable photoluminescence intensity from QDs. Implications of our findings for optical memory and optical computing applications are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Evaluating Performance Portability with the CMS Heterogeneous Pixel Reconstruction code

In the past years the landscape of tools for expressing parallel algorithms in a portable way across various compute accelerators has continued to evolve significantly. There are many technologies on the market that provide portability between CPU, GPUs from several vendors, and in some cases even FPGAs. These technologies include C++ libraries such as Alpaka and Kokkos, compiler directives such as OpenMP, the SYCL open specification that can be implemented as a library or in a compiler, and standard C++ where the compiler is solely responsible for the offloading. Given this developing landscape, users have to choose the technology that best fits their applications and constraints. For example, in the CMS experiment the experience so far in heterogeneous reconstruction algorithms suggests that the full application contains a large number of relatively short computational kernels and memory transfer operations. In this work we use a stand-alone version of the CMS heterogeneous pixel reconstruction code as a realistic use case of HEP reconstruction software that is capable of leveraging GPUs effectively. We summarize the experience of porting this code base from CUDA to Alpaka, Kokkos, SYCL, std::par, and OpenMP offloading. We compare the event processing throughput achieved by each version on NVIDIA and AMD GPUs as well as on a CPU, and compare those to what a native version of the code achieves on each platform.

Andriotis, Nikolaos↗
DOE OSTI2025

Conservative projection-based data-driven model order reduction of a fluid-kinetic spectral solver

Kinetic simulations are computationally intensive due to six-dimensional phase space discretization. Many kinetic spectral solvers use the asymmetrically weighted Hermite expansion due to its conservation and fluid-kinetic coupling properties, i.e., the lower-order Hermite moments capture and describe the macroscopic fluid dynamics, and higher-order Hermite moments describe the microscopic kinetic dynamics. We leverage this structure by developing a parametric data-driven reduced-order model based on the proper orthogonal decomposition, which projects the higher-order kinetic moments while retaining the fluid moments intact. We demonstrate analytically and numerically that the method ensures local and global mass, momentum, and energy conservation. The numerical results show that the proposed method effectively replicates the high-dimensional spectral simulations at a fraction of the computational cost and memory, as validated on the weak Landau damping and two-stream instability benchmark problems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Simple algorithm for polarized parton evolution

We present an algorithm to include the correlation between the production and decay planes of gluons in a parton-shower simulation. The technique is based on identifying the charge currents responsible for the creation and annihilation of the vector field. It is applicable in both the hard-collinear and the soft wide-angle region. As a function of the number of particles, the algorithm scales linear in computing time and memory. We demonstrate agreement with fixed-order perturbative calculations in the relevant kinematical limits, and present a new observable that can be used to probe correlations beyond current-current interactions.

Höche, Stefan [Fermi National Accelerator Laborato↗
DOE OSTI2020

Experimental demonstration and analysis of random field effects in ferromagnet/antiferromagnet bilayers

More than 30 years ago, Malozemoff [Phys. Rev. B 35, 3679 (1987)] hypothesized that exchange interaction at the interface between a ferromagnet (F) and an antiferromagnet (AF) can act as an effective random field, which can profoundly affect the magnetic properties of the system. However, until now this hypothesis has not been directly experimentally tested. We utilize magnetoelectronic measurements to analyze the effective exchange fields at permalloy/CoO interface. Our results cannot be explained in terms of quasiuniform effective exchange fields but are in agreement with the random-field hypothesis of Malozemoff. Finally, the presented approach opens a new route for the quantitative analysis of effective exchange fields and anisotropies in magnetic heterostructures for memory, sensing and computing applications.

36 MATERIALS SCIENCE↗