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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Electrical Gating of the Charge-Density-Wave Phases in Two-Dimensional h -BN/1T-TaS 2 Devices

Here we report on the electrical gating of the chargedensity- wave phases and current in h-BN-capped three-terminal 1T-TaS 2 heterostructure devices. It is demonstrated that the application of a gate bias can shift the source–drain current– voltage hysteresis associated with the transition between the nearly commensurate and incommensurate charge-density-wave phases. The evolution of the hysteresis and the presence of abrupt spikes in the current while sweeping the gate voltage suggest that the effect is electrical rather than self-heating. We attribute the gating to an electric-field effect on the commensurate charge-density-wave domains in the atomic planes near the gate dielectric. The transition between the nearly commensurate and incommensurate charge-density-wave phases can be induced by both the source– drain current and the electrostatic gate. Since the charge-densitywave phases are persistent in 1T-TaS 2 at room temperature, one can envision memory applications of such devices when scaled down to the dimensions of individual commensurate domains and few-atomic plane thicknesses.

2D van der Waals materials↗
DOE OSTI2026

Relaxor Ferroelectric-Like Spatiotemporal Memory in Field-Driven Lipid Bilayers

Lipid membranes are often regarded as passive barriers, yet their nonlinear dielectric response remains poorly understood. Using all-atom molecular dynamics, we show that fully hydrated dipalmitoylphosphatidylcholine bilayers exhibit relaxor ferroelectric-like behavior under time-dependent electric fields. Unlike crystalline relaxors, which are bipolar and display little remanent polarization, lipid bilayers exhibit a unipolar polarization response: even an alternating current field produces persistent, asymmetric polarization. Furthermore, the underlying free-energy landscape contains two distinct minima, a nonpolarized state and a unipolarly polarized state, between which stochastic thermally activated transitions occur. Directionally resolved Van Hove analysis reveals pronounced anisotropy arising from out-of-plane electric dipole alignment, interleaflet coupling, and lateral polarization domains. Each field cycle nucleates polarization at distinct sites and monitors their relaxation, marking a crossover from thermal fluctuations to field-sustained polarization. Remarkably, these polarized domains persist after field removal, generating long-lived, spatially coherent dipolar patterns that encode nanoscale polarization memory. Potassium chloride amplifies these effects via dielectric screening and a modified hydration structure, enhancing electric dipole flexibility and cooperativity. Together, these results establish protein-free bilayers as nonlinear, history-dependent dielectrics capable of sustaining field-tunable electromechanical coupling, providing an emergent physical foundation for nanoscale information storage and memory phenomena reminiscent of short- and long-term plasticity in soft neuromorphic systems.

Insulators↗
DOE OSTI2021

Relocation and persistence of named data elements in coordination namespace

An approach is disclosed that relocates a named data element. A request to move a name corresponding to the named data element is received from a first storage area in a Coordination Namespace to a second storage area in the Coordination Namespace. The first storage area has a first level of persistence, and the second storage area has a second level of persistence. The named data element exists in a Coordination Namespace that is allocated in a memory distributed amongst a plurality of nodes that include the local node and one or more remote nodes. The approach then creates a copy of the named data element in the second storage area.

Nair, Ravi↗
DOE OSTI2020

Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model

Inherent variability in photovoltaic (PV) and associated impacts on power systems is a challenging problem for both the PV owners and the grid operators. Existing statistical and machine learning algorithms typically work well for weather conditions similar to historical data. Furthermore, uncertain weather conditions pose a great challenge to the estimation accuracy of the estimation models. With the enhanced integration of intelligent electronic devices and the realization of associated automation in the power grid, renewable energy data is becoming more accessible, which can be utilized by deep learning models and improve the PV power generation estimation accuracy. In this paper, a hybrid deep learning model driven by external weather data is proposed to do day-ahead PV output forecasting at 15-minute-interval. The proposed model is motivated by the recent advancement of Long-Short-Term-Memory (LSTM) networks and AutoEncoder (AE), which estimates uncertainties in sequence while making the prediction for complex weather conditions. Meanwhile, the persistence model (PM) is used to predict continuous sunny weather conditions. The forecasting result is validated with data from multiple locations

42 ENGINEERING↗
DOE OSTI2025

Interlayer Exciton Polarons in Mesoscopic V 2 O 5 for Broadband Optoelectronic Synapses

Persistent photoconductivity and optoelectronic synaptic behavior are demonstrated in solution-processed mesoscopic α-phase vanadium pentoxide (V 2 O 5 ) thin films. First-principles simulations coupled with the two-site Holstein polaron hopping model show that vacancies at the terminal oxygen position lead to long recombination times because photoexcited electrons and holes reside on different layers separated by the van der Waals gap, forming a weakly coupled interlayer exciton polaron. Mid-gap polaronic states also significantly broaden the photoresponse of the films to span across visible and infrared wavelengths. By controlling the amplitude/intensity, duration, and/or number of optical pulses, tunable optoelectronic memory functions, such as short-term and long-term plasticity, are experimentally established in V 2 O 5 -based optoelectronic synapses. Furthermore, device fabrication was extended to mechanically flexible ultrathin glass substrates. Flexible optoelectronic synapses maintained high performance after 150 bending cycles.

Phan, Thanh Luan [National Renewable Energy Labora↗
DOE OSTI2021

Stacking-engineered ferroelectricity in bilayer boron nitride

Stacking a ferroelectric Properties of layered van der Waals structures can depend sensitively on the stacking arrangement of constituent layers. This phenomenon has been exploited to engineer superconducting, correlated insulator, and magnetic states. Two groups now show that ferroelectricity can also be engineered through stacking: Parallel-stacked bilayers of hexagonal boron nitride exhibit ferroelectric switching even though the bulk material is not ferroelectric (see the Perspective by Tsymbal). To explore these phenomena, Yasuda et al. used transport measurements, whereas Vizner Stern et al. focused on atomic force microscopy. Science , abd3230 and abe8177, this issue p. 1458 and p. 1462 ; see also abi7296, p. 1389

Science & Technology - Other Topics↗
DOE OSTI2025

SlimIO: Lightweight I/O Path Design for Write Isolation in FDP-backed In-Memory Databases

In-Memory Databases (IMDBs) are widely used with HPC applications to manage transient data, often using snapshot-based persistence for backups. Redis, a representative IMDB, employs both snapshot and Write-Ahead Log (WAL) mechanisms, storing data on persistent devices via the traditional kernel I/O path. This method incurs syscall overhead, I/O contention between processes, and SSD garbage collection (GC) delays. To address these issues, we propose SlimIO, which adopts I/O passthru to minimize syscall overhead and inter-process I/O interference. Additionally, it leverages Flexible Data Placement (FDP) SSDs as backup storage to avoid performance degradation from SSD GC. Experimental results show that SlimIO reduces snapshot time by up to 25%, increases query throughput by up to 30% during non-snapshot periods, and lowers 99.9%-ile latency by up to 50%. Furthermore, it achieves a write amplification factor (WAF) of 1.00, indicating no redundant internal writes, thus extending SSD lifespan.

Lee, Sangyun [Sogang University]↗
DOE OSTI2026

Phase Synergy Enables Low‐Power Ferroelectric Switching in HfO 2 Epitaxial Films

HfO 2 -based ferroelectric materials have emerged as leading candidates for next-generation non-volatile memory technologies, owing to their nanoscale robust ferroelectricity and complementary metal–oxide–semiconductor (CMOS) compatibility. However, challenges and debates persist in advancing and comprehensively understanding their ferroelectric behavior. In particular, conventional approaches typically regard non-ferroelectric phases as detrimental and primarily focus on suppressing their formation, yet overlooking their potentially synergistic contributions—particularly those of the tetragonal (T) phase. Here, we unambiguously clarify the beneficial role of the T-phase and introduce a phase-boundary engineering strategy that deliberately harnesses it to enhance ferroelectricity in HfO 2 films. By stabilizing optimal coherent boundaries between ferroelectric orthorhombic (O) and T phases in epitaxial La-doped HfO 2 films, we achieve significant improvements in ferroelectric properties—doubling the remanent polarization (P r ∼ 30 µC/cm 2 ) and substantially reducing the coercive field (E c ∼ 3 MV/cm) by 30% compared to low-La doped samples without such boundaries. Atomic-scale electron microscopy reveals the structural nature of the atomically sharp, coherent O–T boundaries. Combined with deep-learning enhanced molecular dynamics simulations, our results unravel that these boundaries facilitate intermediate polarization states that lower the switching energy barrier. Consequently, phase coexistence shifts from an inherent drawback to a tunable design element, offering a broadly applicable route to ultra-low-power HfO 2 -based nanoelectronics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

The impact of metastability on the high-pressure behavior of cerium

The structures adopted by solids under pressure are often assumed to reflect thermodynamic equilibrium, yet in many materials phase selection is strongly influenced by kinetic pathways and microstructural inheritance. Elemental cerium (Ce) exemplifies this challenge, with decades of conflicting reports describing different high-pressure crystal structures emerging under nominally identical conditions. Here we use neutron diffraction from large ( ~ 60 mm 3 ) sample volumes to follow the structural evolution in ultra-high-purity Ce during controlled pressure-temperature cycling between 85 and 295 K and up to 8 GPa. We find that the crystal structure formed at high pressure depends on the compression pathway: slow compression ( ~ 0.25 GPa hr −1 ) at room temperature favors an orthorhombic phase (α'), whereas slow ( ~ 0.25 GPa hr −1 ) and also moderately faster ( ~ 0.5 GPa hr −1 ) compression at low temperature stabilizes a pure monoclinic phase (α"). The low-temperature phase persists metastably over a wide temperature range but transforms irreversibly upon heating above ~ 280 K, or modest pressure cycling. Remarkably, the lower-pressure γ phase remains trapped far beyond the expected stability field, persisting to the highest pressures studied. These observations show that phase selection in Ce is governed by kinetics and microstructural memory rather than equilibrium thermodynamics or sample morphology alone, establishing path dependence as a defining feature of its high-pressure behavior.

Ridley, Christopher J. [Oak Ridge National Laborat↗
DOE OSTI2024

Diabatic Eddy Forcing Increases Persistence and Opposes Propagation of the Southern Annular Mode in MERRA-2

Abstract As a dominant mode of jet variability on subseasonal time scales, the Southern Annular Mode (SAM) provides a window into how the atmosphere can produce internal oscillations on longer-than-synoptic time scales. While SAM’s existence can be explained by dry, purely barotropic theories, the time scale for its persistence and propagation is set by a lagged interaction between barotropic and baroclinic mechanisms, making the exact physical mechanisms challenging to identify and to simulate, even in latest generation models. By partitioning the eddy momentum flux convergence in MERRA-2 using an eddy–mean flow interaction framework, we demonstrate that diabatic processes (condensation and radiative heating) are the main contributors to SAM’s persistence in its stationary regime, as well as the key for preventing propagation in this regime. In SAM’s propagating regime, baroclinic and diabatic feedbacks also dominate the eddy–jet feedback. However, propagation is initiated by barotropic shifts in upper-level wave breaking and then sustained by a baroclinic response, leading to a roughly 60-day oscillation period. This barotropic propagation mechanism has been identified in dry, idealized models, but here we show evidence of this mechanism for the first time in reanalysis. The diabatic feedbacks on SAM are consistent with modulation of the storm-track latitude by SAM, altering the emission temperature and cloud cover over individual waves. Therefore, future attempts to improve the SAM time scale in models should focus on the storm-track location, as well as the roles of the cloud and moisture parameterizations. Significance Statement As they circumnavigate the planet, the tropospheric jet streams slowly drift north and south over about 30 days, longer than the normal limit of weather prediction. Understanding the source of this “memory” could improve our knowledge of how the atmosphere organizes itself and our ability to make long-term forecasts. Current theories have identified several possible internal atmospheric interactions responsible for this memory. Yet most of the theories for understanding the jets’ behavior assume that this behavior is only weakly influenced by atmospheric water vapor. We show that this assumption is not enough to understand jet persistence. Instead, clouds and precipitation are more important contributors in reanalysis data than internal “dry” mechanisms to this memory of the Southern Hemisphere jet.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Multiscale analysis of large twist ferroelectricity and swirling dislocations in bilayer hexagonal boron nitride

With its atomically thin structure and intrinsic ferroelectric properties, heterodeformed bilayer hexagonal boron nitride (hBN) has gained prominence in next-generation non-volatile memory applications. However, studies to date have focused almost exclusively on small-twist bilayer hBN, leaving the question of whether ferroelectricity can persist under small heterostrain and large heterodeformation entirely unexplored. In this work, we establish the crystallographic origin of ferroelectricity in bilayer hBN configurations heterodeformed relative to high-symmetry configurations such as AA-stacking and 21.786789° twisted configurations (Σ7), using Smith normal form bicrystallography. We then demonstrate out-of-plane ferroelectricity in bilayer hBN across configurations vicinal to both the AA and Σ7 stackings. Atomistic simulations reveal that AA-vicinal systems support ferroelectricity under both small twist and small strain, with polarization switching in the latter governed by the deformation of swirling dislocations rather than the straight interface dislocations seen in the former. For Σ7-vicinal systems, where existing interatomic potentials underperform particularly under extreme out-of-plane compression, we develop a density-functional-theory-informed continuum framework—the bicrystallography-informed frame-invariant multiscale (BFIM) model, which captures out-of-plane ferroelectricity in heterodeformed configurations vicinal to Σ7 stacking. Interface dislocations in these large heterodeformed bilayer configurations exhibit markedly smaller Burgers vectors compared to interface dislocations in small-twist and small-strain bilayer hBN. The BFIM model reproduces experimental results and provides a powerful, computationally efficient framework for predicting ferroelectricity in large-unit-cell heterostructures where atomistic simulations are prohibitively expensive.

Ahmed, Md Tusher [Univ. of Illinois at Urbana-Cham↗
DOE OSTI2024

Identification of Distorted Gamma-Ray Signature Patterns Using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network

The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. Here, this paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241 Am, 60 Co, 137 Cs, 192 Ir, 239 Pu, and 235 U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

High-Resolution Full-Field Structural Microscopy of the Voltage-Induced Filament Formation in VO 2 -Based Neuromorphic Devices

In order to make neuromorphic functions in memristive devices more efficient, information about the structural properties of filament formation at the micro- and mesoscopic scales is necessary. Despite extensive research on VO 2 , a key material due to its filament formation, local operando structural measurements remain challenging and often involve destructive specimen preparation and long rastering times, greatly limiting the scope of experimental studies. Utilizing dark-field X-ray microscopy (DFXM), a fullfield imaging modality, structural signatures of the filament formation process operando are revealed in VO 2 devices. DFXM experiments illustrate that rutile filaments contain isolated monoclinic clusters, indicating structural nonuniformity interior to the filament. The formation of the rutile phase beneath device electrodes was shown to precede filament development, followed by the formation of filament paths guided by nucleation sites within the device. Finally, a medium-term (<30 min) memory mechanism is observed in VO 2 , mediated by sites within the device gap that tend to switch at significantly lower voltages after electrical cycling, a tendency that persists through a brief thermal reset. High spatial resolution, large field-of-view, structure selectivity, and fast signal acquisition of DFXM provided insight into structural features of the filamentary channel and surrounding regions during voltage cycling.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

The Persistent Challenge of Data Locality in the Post-Exascale Era

The era of exascale computing, exemplified by systems like Frontier achieving exaflop-level performance, marks a milestone. However, the quest for sheer compute power leads to strong imbalance in system design. Hence, scaling advancements in memory, network bandwidth, and storage are also necessary and pose challenges, with a crucial need to address data locality issues. This article underscores the fundamental importance of data locality as a key abstraction for optimizing application performance. Despite notable software solutions, the growing complexity of parallelism and memory hierarchy demands performance-portable data locality solutions across diverse computing platforms. Additionally, the article revisits data locality aspects, covering hardware considerations, application perspectives, software stack abstractions, and tool support. It concludes with insights into data locality challenges and opportunities, emphasizing the ongoing significance of collaborative research for progress in this critical issue.

Unat, Didem [Koc University, Istanbul (Turkey)] (O↗
DOE OSTI2022

Experiments on the single-mode Richtmyer–Meshkov instability with reshock at high energy densities

Here, the hydrodynamic instability growth of a reshocked single-mode interface between high energy density fluids is studied. A laser-driven shock wave is used to drive an initially solid, sinusoidal interface between a dense plastic (1.43 g/cc) and a light foam (≈ 0.110 g/cc). After the interface has grown to a nonlinear state where the amplitude is of order of the wavelength, it is reshocked. The reshock compresses the nonlinear perturbation, which then grows at about twice the rate. While the pre-reshock growth rate is sensitive to the initial amplitude and wavelength of the perturbation, the post-reshock growth rate is comparatively insensitive to the initial condition. Qualitatively, we observe that the perturbations are less coherent after reshock, consistent with the idea that having a reshock accelerates the transition to turbulence. We find that some memory of the initial condition remains, even after reshock at late time: it appears if the initial perturbations have large enough wavelengths, and the flow structure of size comparable to the initial wavelength persists through reshock. Our results agree with design simulations and are consistent with the phenomenology of reshock studies in conventional gaseous shock tubes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Method and apparatus for using compression to improve performance of low voltage caches

A method of operating a cache in a computing device includes, in response to receiving a memory access request at the cache, determining compressibility of data specified by the request, selecting in the cache a destination portion for storing the data based on the compressibility of the data and a persistent fault history of the destination portion, and storing a compressed copy of the data in a non-faulted subportion of the destination portion, wherein the persistent fault history indicates that the non-faulted subportion excludes any persistent faults.

Kalamatianos, John↗
DOE OSTI2024

Comparison of Machine Learning-Based Predictive Models of the Nutrient Loads Delivered from the Mississippi/Atchafalaya River Basin to the Gulf of Mexico

Predicting nutrient loads is essential to understanding and managing one of the environmental issues faced by the northern Gulf of Mexico hypoxic zone, which poses a severe threat to the Gulf’s healthy ecosystem and economy. The development of hypoxia in the Gulf of Mexico is strongly associated with the eutrophication process initiated by excessive nutrient loads. Due to the complexities in the excessive nutrient loads to the Gulf of Mexico, it is challenging to understand and predict the underlying temporal variation of nutrient loads. The study was aimed at identifying an optimal predictive machine learning model to capture and predict nonlinear behavior of the nutrient loads delivered from the Mississippi/Atchafalaya River Basin (MARB) to the Gulf of Mexico. For this purpose, monthly nutrient loads (N and P) in tons were collected from US Geological Survey (USGS) monitoring station 07373420 from 1980 to 2020. Machine learning models—including autoregressive integrated moving average (ARIMA), gaussian process regression (GPR), single-layer multilayer perceptron (MLP), and a long short-term memory (LSTM) with the single hidden layer—were developed to predict the monthly nutrient loads, and model performances were evaluated by standard assessment metrics—Root Mean Square Error (RMSE) and Correlation Coefficient (R). The residuals of predictive models were examined by the Durbin–Watson statistic. The results showed that MLP and LSTM persistently achieved better accuracy in predicting monthly TN and TP loads compared to GPR and ARIMA. In addition, GPR models achieved slightly better test RMSE score than ARIMA models while their correlation coefficients are much lower than ARIMA models. Moreover, MLP performed slightly better than LSTM in predicting monthly TP loads while LSTM slightly outperformed for TN loads. Furthermore, it was found that the optimizer and number of inputs didn’t show effects on the LSTM performance while they exhibited impacts on MLP outcomes. This study explores the capability of machine learning models to accurately predict nonlinearly fluctuating nutrient loads delivered to the Gulf of Mexico. Further efforts focus on improving the accuracy of forecasting using hybrid models which combine several machine learning models with superior predictive performance for nutrient fluxes throughout the MARB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

ExTreeM: Scalable Augmented Merge Tree Computation via Extremum Graphs

Over the last decade merge trees have been proven to support a plethora of visualization and analysis tasks since they effectively abstract complex datasets. Here, this paper describes the ExTreeM-Algorithm: A scalable algorithm for the computation of merge trees via extremum graphs. The core idea of ExTreeM is to first derive the extremum graph G of an input scalar field f defined on a cell complex K, and subsequently compute the unaugmented merge tree of f on G instead of K; which are equivalent. Any merge tree algorithm can be carried out significantly faster on G, since K in general contains substantially more cells than G. To further speed up computation, ExTreeM includes a tailored procedure to derive merge trees of extremum graphs. The computation of the fully augmented merge tree, i.e., a merge tree domain segmentation of K, can then be performed in an optional post-processing step. All steps of ExTreeM consist of procedures with high parallel efficiency, and we provide a formal proof of its correctness. Our experiments, performed on publicly available datasets, report a speedup of up to one order of magnitude over the state-of-the-art algorithms included in the TTK and VTK-m software libraries, while also requiring significantly less memory and exhibiting excellent scaling behavior.

97 MATHEMATICS AND COMPUTING↗