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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Stimulated geologic hydrogen: from mechanistic control to engineered rock transformation

Geologic hydrogen (GeoH 2 ) generated from subsurface iron-rich rock–water reaction (i.e., serpentinization) is emerging as a promising candidate for the next primary energy source. Yet, accelerating GeoH 2 production from geological to human timescales via enhanced serpentinization remains a formidable scientific and technical challenge. Here, in this Review, we decipher the mechanistic control and explore strategies for accelerating in situ, engineered iron-rich rock transformation into carbon-free GeoH 2 by orders of magnitude. Serpentinization rate is hindered by low porosity and permeability of source rocks, suboptimal temperatures, unfavorable water chemistry, inefficient Fe 2+ -to-Fe 3+ conversion, thermodynamic constraints, and low reactive surface area. While closed-system experiments provide valuable mechanistic insights, open-system conditions with fluid circulation are more crucial for economically viable GeoH 2 production. We assess stimulation techniques from enhanced hydrocarbon and geothermal recovery as tools to be adopted or adapted for increasing reactive surface areas for stimulated GeoH2 production. We estimate that 7.40 × 10 5 to 1.73 × 10 6 million metric tons (Mt) of hydrogen could be engineered over 20 to 50 years from about 10% iron-rich rocks within 10 km depth of continental crust. Enabling GeoH 2 as a viable energy source requires not only advancing scientific frontiers but also forming a global GeoH 2 research network and innovation ecosystem to address the critical scientific, technical, societal, economic, and policy challenges.

08 HYDROGEN↗
DOE OSTI2024

Pressure-induced transformation of Nb 2 O 5 under shock compression from first principles

Ab initio molecular dynamics (AIMD) simulations were carried out to investigate the equation of state of Nb 2 O 5 and its pressure-density relationship under shock conditions. Here, the focus of this study is on the monoclinic B-Nb 2 O 5 (C2/c) polymorph. Enthalpy calculations from AIMD trajectories at 300 K show that the pressure-induced transformation between the thermody-namically most stable crystalline monoclinic parent phase H-Nb 2 O 5 (P2/m) and B-Nb 2 O 5 occurs at ~1.9 GPa. This H→B transition is energetically more favorable than the H→L(Pmm2) pressure-induced transition recently observed at ~5.9-9.0 GPa. The predicted shock properties of Nb 2 O 5 polymorphs are also compared to their Nb and NbO 2 counterparts to assess the impact of niobium oxidation on shock response.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Accurate and efficient open-source implementation of domain-based local pair natural orbital (DLPNO) coupled-cluster theory using a t1-transformed Hamiltonian

We present an efficient, open-source formulation for coupled-cluster theory through perturbative triples with domain-based local pair natural orbitals [DLPNO-CCSD(T)]. Similar to the implementation of the DLPNO-CCSD(T) method found in the ORCA package, the most expensive integral generation and contraction steps associated with the CCSD(T) method are linear-scaling. In this work, we show that the t1-transformed Hamiltonian allows for a less complex algorithm when evaluating the local CCSD(T) energy without compromising efficiency or accuracy. Our algorithm yields sub-kJ mol−1 deviations for relative energies when compared with canonical CCSD(T), with typical errors being on the order of 0.1 kcal mol−1, using our TightPNO parameters. We extensively tested and optimized our algorithm and parameters for non-covalent interactions, which have been the most difficult interaction to model for orbital (PNO)-based methods historically. To highlight the capabilities of our code, we tested it on large water clusters, as well as insulin (787 atoms).

Chemistry↗
DOE OSTI2024

The self-annealing of irradiation induced defects in magnetite Fe 3 O 4 : Revealing reversible irradiation-induced disorder transformation through in situ TEM

This work reports heavy ion-irradiation effects in polycrystalline Fe 3 O 4 . For this matter, Fe/Fe 3 O 4 heterostructures were irradiated in situ in a transmission electron microscopy with 1 MeV Kr ions at 50 K. Evidence of cubic to monoclinic transformation (a.k.a Verwey transition) was recorded in some magnetite grains upon cooling the sample (around 90 K); however, most of the oxide grains retain their cubic spinel structure. Irradiation effects were analyzed in the cubic phase up to a maximum dose of 38 dpa without the sign of amorphization. The extinction of first-order reflections was recorded at doses below 1 dpa, indicating the formation of a new (metastable) phase with half of the lattice parameters compared to the unirradiated Fe 3 O 4 crystal. The formation of the new crystalline phase, which also presents a high resistance to amorphization, is related to the disordering of the cation lattice and the high mobility of the cation interstitials. The metastable phase readily recovers around 225 K during the natural warming of the sample from 50 K to room temperature.

Atom irradiation effects↗
DOE OSTI2026

Neural operator transformers capture bifurcating drift-wave turbulence in fusion plasma simulations

Self-consistent modeling of turbulence-driven transport is critical for optimizing confinement in magnetically confined fusion plasmas, such as tokamaks and stellarators. In particular, capturing the long-term co-evolution of turbulence, flow, and background plasma profiles remains computationally challenging. Direct numerical simulation of these multiscale, highly nonlinear processes is often demanding and impractical for real-time control or design optimization. To address this bottleneck, we investigate transformer-based neural operator partial differential equation surrogates for emulating the dynamics of drift-wave turbulence bifurcation mediated by zonal flows, using the modified Hasegawa–Wakatani (MHW) model as a prototypical system. We find that the finetuned neural operator model has excellent performance in capturing the multi-spatiotemporal-scales of MHW turbulence bifurcation and is robust to testing on rare and out-of-distribution dynamics. Specifically, we demonstrate that a single unified model accurately predicts both quasi-steady-state turbulence and a wide range of dynamical transition processes, such as nonlinear saturation, spontaneous suppression of turbulence, and the emergence of macroscopic zonal flows, over time horizons vastly exceeding the local turbulence correlation time. This computationally efficient approach establishes a strong foundation for fast, AI-based modeling of complex, multiscale phenomena in magnetized fusion plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

DESI DR1 Ly α 1D power spectrum: the Fast Fourier Transform estimator measurement

Here, we present the one-dimensional Lyman-α forest power spectrum measurement derived from the data release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI). The measurement of the Lyman-α forest power spectrum along the line of sight from high-redshift quasar spectra provides information on the shape of the linear matter power spectrum, neutrino masses, and the properties of dark matter. In this work, we use a Fast Fourier Transform (FFT)-based estimator, which is validated on synthetic data in a companion paper. Compared to the FFT measurement performed on the DESI early data release, we improve the noise characterization with a cross-exposure estimator and test the robustness of our measurement using various data splits. We also refine the estimation of the uncertainties and now present an estimator for the covariance matrix of the measurement. Furthermore, we compare our results to previous high-resolution and eBOSS measurements. In another companion paper, we present the same DR1 measurement using the Quadratic Maximum Likelihood Estimator (QMLE). These two measurements are consistent with each other and constitute the most precise one-dimensional power spectrum measurement to date, while being in good agreement with results from the DESI early data release.

Lyman alpha forest↗
DOE OSTI2025

CryoTEN: efficiently enhancing cryo-EM density maps using transformers

Abstract Motivation Cryogenic electron microscopy (cryo-EM) is a core experimental technique used to determine the structure of macromolecules such as proteins. However, the effectiveness of cryo-EM is often hindered by the noise and missing density values in cryo-EM density maps caused by experimental conditions such as low contrast and conformational heterogeneity. Although various global and local map-sharpening techniques are widely employed to improve cryo-EM density maps, it is still challenging to efficiently improve their quality for building better protein structures from them. Results In this study, we introduce CryoTEN—a 3D UNETR++ style transformer to improve cryo-EM maps effectively. CryoTEN is trained using a diverse set of 1295 cryo-EM maps as inputs and their corresponding simulated maps generated from known protein structures as targets. An independent test set containing 150 maps is used to evaluate CryoTEN, and the results demonstrate that it can robustly enhance the quality of cryo-EM density maps. In addition, automatic de novo protein structure modeling shows that protein structures built from the density maps processed by CryoTEN have substantially better quality than those built from the original maps. Compared to the existing state-of-the-art deep learning methods for enhancing cryo-EM density maps, CryoTEN ranks second in improving the quality of density maps, while running >10 times faster and requiring much less GPU memory than them. Availability and implementation The source code and data are freely available at https://github.com/jianlin-cheng/cryoten.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Co-designing Spectral Transformation Oracles with Hybrid Oscillator-Qubit Quantum Processors: From Algorithms to Compilation

We co-design a family of quantum eigenvalue transformation oracles that can be efficiently implemented on hybrid discrete- or continuous-variable (qubit or qumode) hardware. To illustrate the oracle’s representation-theoretic power and near-term experimental accessibility, we encode a Gaussian imaginary time-evolution spectral filter. As a result, we define a continuous linear combination of unitaries block encoding. Due to the ancillary qumode’s infinite-dimensional nature, continuous-variable qumodes constitute a powerful compilation tool for encoding continuous spectral functions without discretization errors while minimizing resource requirements. We then focus on the ubiquitous task of preparing eigenstates in quantum spin models. For completeness, we provide an end-to-end compilation which expresses high-level oracles in terms of an experimentally realizable instruction set architecture in both 1D and 2D. Finally, we examine the leading-order effects of physical errors and highlight open research directions. Our algorithms scale linearly with the spatial extent of the target system and are applicable to both near-term and large-scale quantum processors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Competing ionization and dissociation: Extension of the energy-dependent frame transformation to the gerade symmetry of H 2

This article solves two major tasks that frequently arise in the theory of electron collisions with a target molecular cation. First, it extends the energy-dependent frame transformation (EDFT) treatment, which is needed to map fixed-nuclei electron-molecule scattering matrices into an energy-dependent laboratory-frame scattering matrix with vibrational channel indices. The EDFT mapping can now be carried out even when the target molecule possesses multiple low-energy potential curves, significantly transcending previous applications. Second, it implements a method to extract the rest of the full laboratory-frame scattering matrix, i.e., the columns and rows describing input and/or output dissociation channels. The treatment is benchmarked in this article against the essentially exact solution of a refined two-dimensional model of the singlet gerade Σ symmetry of H 2 . Our tests demonstrate that the theory accurately maps fixed-nuclei scattering information, of the type provided by existing electron-molecule computer codes, into a laboratory-frame scattering matrix that includes both ionization and dissociation. Furthermore, this treatment can provide a general framework applicable to a broad class of electron collision processes involving diatomic target ions, suitable for an accurate description of challenging processes such as dissociative recombination.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

200-fold increase in dynamic strength of platinum at 430 GPa without a phase transformation

Here, the high-pressure strength of solid platinum is studied at the National Ignition Facility via Rayleigh-Taylor ripple growth under ramp compression. Laser-driven experiments reached pressures up to 430 GPa, collecting velocimetry and radiography data. Despite no phase transformation, the observed ripple growth aligns with hydrodynamic simulations using an amplified Steinberg-Guinan model, resulting in a 200-fold increase in strength. Molecular dynamics simulations of platinum ramp compression yield similar von Mises stress at comparable high pressure and high strain rate conditions. These findings represent the highest-pressure strength measurements ever achieved for platinum and offer critical insight into its significant strengthening behavior under high pressure and strain rate conditions.

Righi, Gaia [Lawrence Livermore National Laborator↗
DOE OSTI2024

Precise cosmological constraints from BOSS galaxy clustering with a simulation-based emulator of the wavelet scattering transform

For this study, we perform a reanalysis of the BOSS CMASS DR12 galaxy dataset using a simulation-based emulator for the wavelet scattering transform (WST) coefficients. Moving beyond our previous works, which laid the foundation for the first galaxy clustering application of this estimator, we construct a neural net-based emulator for the cosmological dependence of the WST coefficients and the 2-point correlation function multipoles, trained from the state-of-the-art suite of abacussummit simulations combined with a flexible halo occupation distribution (HOD) galaxy model. In order to confirm the accuracy of our pipeline, we subject it to a series of thorough internal and external mock parameter recovery tests, before applying it to reanalyze the CMASS observations in the redshift range 0.46 < z < 0.57. We find that a joint WST+2-point correlation function likelihood analysis allows us to obtain marginalized 1⁢σ errors on the Λ⁢ CDM parameters that are tighter by a factor of 2.5–6, compared to the 2-point correlation function, and by a factor of 1.4–2.5 compared to the WST-only results. This corresponds to a competitive 0.9%, 2.3% and 1% level of determination for parameters ω c , ⁢σ 8 &n s , respectively, and also to a 0.7% and 2.5% constraint on derived parameters h and ƒ⁡(z)⁢⁢σ 8 ⁡(z), in agreement with the Planck 2018 results. Our results reaffirm the constraining power of the WST and highlight the exciting prospect of employing higher-order statistics in order to fully exploit the power of upcoming stage-IV spectroscopic observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Improved limits on n → n ' transformation from the Spallation Neutron Source

Conversions between neutrons n and dark matter candidate sterile neutrons n' have been proposed as a mechanism for baryon number $\mathscr{B}$ violation. In the case that there is a small mass difference Δ⁢m between the n and the n' states, oscillations can be induced by compensating for Δ⁢m with a magnetic field. A search for such neutron oscillations was performed at the Spallation Neutron Source by looking for anomalous neutron transmission through a strongly absorbing cadmium wafer inside of a 6.6 T magnet. The approach described here saw no regenerated neutrons above background, which provides an improved limit for neutron–sterile neutron transformations for a range of Δ⁢m between 0.1 and 1000 neV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗
DOE OSTI2024

Power System Resilience Considering Hurricane Storms and Generator Step-up Transformers

Power system resilience describes the system’s ability to withstand and recover quickly from unexpected power outages due to extreme events. As society’s electrification continues at a rapid rate, it is imperative electric infrastructure is designed and operated with extra security. This work studies the impact of severe hurricane storms on the power system located along the United States’ Eastern Seaboard. Specific focus is designated to steady state system operation and loss of large generation step up power transformers. A series of phasor domain power system simulations are conducted to determine the critical point at which the system loses steady state operability. Additionally, active power load shedding and generator dispatching are studied as mitigation and recovery strategies. Results indicate generator dispatching as an effective proactive mitigation strategy and load shedding as an effective recovery strategy.

Liu, Yilu↗
DOE OSTI2025

Intercell Power Transformer for Power Balancing of Polyphase Wireless Power Transfer Systems

Polyphase wireless power transfer (PWPT) systems experience receiver side imbalances in misaligned conditions. This paper proposes a method for power balancing in PWPT systems, specifically addressing the challenges caused by receivers coil unequal coupling. An intercell power transformer (IPT) is introduced at the receiver side to mitigate the effects of coil mismatch. Simulations of the PWPT system incorporating IPT demonstrate its effectiveness in enhancing power distribution. For voltage-fed rectifiers (VFR), the IPT increases the conduction angle, resulting in higher output power. In current- fed rectifiers (CFR), the IPT promotes uniform power sharing among rectifiers, thereby reducing electrical stress on semiconductor devices and passive components. Simulation results indicate that, under coil misalignment, the IPT improves output power by approximately 49% for VFRs and 44% for CFRs compared to conventional PWPT systems without IPT.

Wojda, Rafal [ORNL] (ORCID:0000000286609268)↗
DOE OSTI2025

Advanced Photovoltaic Module Characterization: Using Image Transformers for Current–Voltage Curve Prediction From Electroluminescence Images

Individual photovoltaic (PV) module health monitoring can be a daunting task for operation and maintenance of solar farms. Modules can be inspected through luminescence, thermal imaging, and current–voltage (I–V) curve analyzes for identification of damage and power loss. I–V curves provide easily interpretable data to determine module health as they directly provide electrical performance metrics. However, in order to obtain these curves, modules must be disconnected from the array and either removed to a solar simulator or characterized in situ with corrections for module temperature, the incident solar spectrum, and intensity. Luminescence or thermal images of a module are relatively easy to acquire in situ. Electroluminescence (EL) images highlight physical defects in the modules but do not provide easily interpretable features to correlate with electrical performance. This work presents a SWin transformer network to predict I–V curves for PV modules from their corresponding EL images. The predicted I–V curves allow the accurate prediction of the maximum power point (MPP), short-circuit current I sc , and open-circuit voltage V oc with a mean error less of than 1%. Comparing single diode model (SDM) parameters extracted from the predicted curves to those extracted from the true curves, the series resistance R s demonstrates a mean error of 5.19%, and the photocurrent I a mean error of 0.197%. The shunt resistance R sh and dark current Io parameters are predicted with larger errors because of their sensitivity to small changes in the I–V curve.

Byford, Brandon K. [New Mexico State Univ., Las Cr↗
DOE OSTI2025

Toward Mixed Analog-Digital Quantum Signal Processing: Quantum AD/DA Conversion and the Fourier Transform

Signal processing stands as a pillar of classical computation and modern information technology, applicable to both analog and digital signals. Recently, advancements in quantum information science have suggested that quantum signal processing (QSP) can enable more powerful signal processing capabilities. However, the developments in QSP have primarily leveraged digital quantum resources, such as discrete-variable (DV) systems like qubits, rather than analog quantum resources, such as continuous-variable (CV) systems like quantum oscillators. Consequently, there remains a gap in understanding how signal processing can be performed on hybrid CV-DV quantum computers. Here we address this gap by developing a new paradigm of mixed analog-digital QSP. We demonstrate the utility of this paradigm by showcasing how it naturally enables analog-digital conversion of quantum signals—specifically, the transfer of states between DV and CV quantum systems. We then show that such quantum analog-digital conversion enables new implementations of quantum algorithms on CV-DV hardware. This is exemplified by realizing the quantum Fourier transform of a state encoded on qubits via the free-evolution of a quantum oscillator, albeit with a runtime exponential in the number of qubits due to information theoretic arguments. Collectively, this work marks a significant step forward in hybrid CV-DV quantum computation, providing a foundation for scalable analog-digital signal processing on quantum processors.

42 ENGINEERING↗
DOE OSTI2024

Study of electron-induced chemical transformations in polymers

In extreme ultraviolet (EUV) photoresist exposure, the primary and secondary electrons drive chemistry rather than the EUV photons themselves. These electrons have a wide range of energies below approximately 80 eV, which are capable of complex network of reactions during exposure. To better understand the ability of electrons of different energies within the EUV primary and secondary electron range, we want to characterize and compare the chemistry induced in pure polymer films by direct exposure to electrons. Thin films of poly(tert-butyl methacrylate), poly(methyl methacrylate), and poly(4-hydroxystyrene) were exposed to a 20 to 80 eV electron beam. Outgassing during exposure was characterized in-situ using a quadrupole residual gas analyzer. The thickness changes were measured using ellipsometry and chemical bond structure data were collected using Fourier-transform infrared spectroscopy (FTIR) after exposure to compare different exposure conditions. Poly(4-hydroxystyrene) demonstrated stability during electron exposures. Exposures of the other two materials led to outgassing of protecting groups, the intensity of which decayed in time. Outgassing, FTIR, and thickness loss data exhibited approximately linear relationships to each other.

Mueller, Maximillian↗