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

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Random Phase Approximation Correlation Energy Using Real-Space Density Functional Perturbation Theory

We present a real-space method for computing the random phase approximation (RPA) correlation energy within Kohn–Sham density functional theory, leveraging the low-rank nature of the frequency-dependent density response operator. In particular, we employ a cubic-scaling formalism based on density functional perturbation theory that circumvents the calculation of the response function matrix, instead relying on the ability to compute its product with a vector through the solution of the associated Sternheimer linear systems. We develop a large-scale parallel implementation of this formalism using the subspace iteration method in conjunction with the spectral quadrature method while employing the Kronecker product-based method for the application of the Coulomb operator and the conjugate orthogonal conjugate gradient method for the solution of the linear systems. We demonstrate convergence with respect to key parameters and verify the method’s accuracy by comparing with plane-wave results. We show that the framework achieves good strong scaling to many thousands of processors, reducing the time to solution for a lithium hydride system with 128 electrons to around 150 s on 4608 processors.

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

An Integral-based Technique to Accelerate the Monte Carlo Radiative Transfer Computation for Supernovae

We present an integral-based technique (IBT) algorithm to accelerate supernova (SN) radiative transfer calculations. The algorithm utilizes “integral packets,” which are calculated by the path integral of the Monte Carlo (MC) energy packets, to synthesize the observed spectropolarimetric signal at a given viewing direction in a 3D time-dependent radiative transfer program. Compared to the event-based technique (EBT) proposed by M. Bulla et al., our algorithm significantly reduces the computation time and increases the MC signal-to-noise ratio (S/N). Using a 1D spherical symmetric Type Ia SN ejecta model DDC10 and its derived 3D model, the IBT algorithm has successfully passed the verification of spherical symmetry and cross comparison on a 3D SN model with the direct-counting technique and EBT. Notably, with our algorithm implemented in the 3D MC radiative transfer code SEDONA, the computation time is faster than EBT by a factor of 10−30, and the S/N is better by a factor of 1.5−3, with the same number of MC quanta.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Asymptotic errors in adiabatic evolution

The adiabatic theorem in quantum mechanics implies that if a system is in a discrete eigenstate of a Hamiltonian and the Hamiltonian evolves in time arbitrarily slowly, the system will remain in the corresponding eigenstate of the evolved Hamiltonian. Understanding corrections to the adiabatic result that arise when the evolution of the Hamiltonian is slow—but not arbitrarily slow—has become increasingly important, especially since adiabatic evolution has been proposed as a method of state preparation in quantum computing. Here, this paper identifies two regimes, an adiabatic regime in which corrections are generically small and can depend on details of the evolution throughout the path, and a hyperadiabatic regime in which the error is given by a form similar to an asymptotic expansion in the inverse of the evolution time with the coefficients depending principally on the behavior at the endpoints. However, the error in this hyperadiabatic regime is neither given by a true asymptotic series nor solely dependent on the endpoints: the coefficients combine the contributions from both endpoints, with relative phase factors that depend on the average spectral gaps along the trajectory, multiplied by the evolution time. The central result of this paper is to identify a quantity, referred to as the typical error, which is obtained by appropriately averaging the error over evolution times that are small compared to the evolution time itself. This typical error is characterized by an asymptotic series and depends solely on the endpoints of the evolution, remaining independent of the details of the intermediate evolution.

adiabatic approximation↗
DOE OSTI2024

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Spatiotemporal Downscaling Model for Solar Irradiance Forecast Using Nearest-Neighbor Random Forest and Gaussian Process

Accurate solar photovoltaic (PV) capacity estimation requires high-resolution, site-specific solar irradiance data to account for localized variability. However, global datasets, such as the National Solar Radiation Database (NSRDB), provide regional averages that fail to capture the fine-scale fluctuations critical for large-scale grid integration. This limitation is particularly relevant in the context of increasing distributed energy resources (DERs) penetration, such as rooftop PV. Additionally, it is critical to the implementation of the U.S. Federal Energy Regulatory Commission (FERC) Order 2222, which facilitates DER participation in U.S. bulk power markets. To address this challenge, this study evaluates Nearest-Neighbor Random Forest (NNRF) and Nearest-Neighbor Gaussian Process (NNGP) models for spatiotemporal downscaling of global solar irradiance data. By leveraging historical irradiance and meteorological data, these models incorporate spatial, temporal, and feature-based correlations to enhance local irradiance predictions. The NNRF model, a machine-learning approach, prioritizes computational efficiency and predictive accuracy, while the NNGP model offers a level of interpretability and prediction uncertainty by numerically quantifying correlations and dependencies in the data. Model validation was conducted using day-ahead predictions. The results showed that the average Goodness of Fit (GoF) of the NNRF model of 90.61% across all eight sites outperformed the GoF of the NNGP of 85.88%. Additionally, the computational speed of NNRF was 2.5 times faster than the NNGP. Finally, the NNGP displayed polynomial scaling while the NNRF scaled linearly with increasing number of nearest neighbors. Additional validation of the model on five sites in Puerto Rico further confirmed the superiority of the NNRF model over the NNGP model. These findings highlight the robustness and computational efficiency of NNRF for large-scale solar irradiance downscaling, making it a strong candidate for improving PV capacity estimation and real-time electricity market integration for DERs.

Asiedu, Shadrack (ORCID:0009000646004826)↗
DOE OSTI2025

On the functional dependence of transition-potential coupled cluster

Orbital relaxation of the core region is a primary source of error in the computation of core ionization and core excitation energies. Recently, Transition-Potential Coupled Cluster (TP-CC) methods have been used to explicitly treat orbital relaxation using non-variational molecular orbitals determined by reoccupation of orbitals optimized for a fractional core occupation. The amount of fractional occupation is governed by parameter λ, and recommended values for accurate TP-CCSD and XTP-CCSD computations of carbon, nitrogen, oxygen, and fluorine K edges were previously determined. Herein, we explore the performance of several density functionals for generating the fractionally occupied orbitals used in TP-CCSD. These functionals include HF, BP86, BH&HLYP, B3LYP, M06-2X, and ωB97m-V. The fractionally occupied orbitals computed across the various functionals were subsequently employed as the initial orbitals for our TP-CCSD calculations of organic K-edge x-ray absorption and photoelectron spectra. Regardless of the functional used to generate the fractionally occupied orbitals, the TP-CCSD calculations yield accurate and comparable core ionization energies, core excitation energies, and oscillator strengths.

Coupled-cluster methods↗
DOE OSTI2025

Software Tools Ecosystem Project (STEP) Midyear Report CY2025

This document provides a technical project report for the first six months of 2025 for the Software Tools Ecosystem Project (STEP). The mission of STEP is to enable critical software tools to proactively adapt to emerging platform technologies (such as new accelerators, storage devices, network technologies, and smart devices) and emerging application use cases (such as advanced machine learning and workflow frameworks) so that they continue to meet the needs of scientific computing and provide a strong foundation for future Advanced Scientific Computing Research activities. Our challenges include the wide breadth of our stakeholders and rapidly evolving platform technology dependencies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Strong CP and flavor in multi-Higgs theories

We introduce a class of multi-Higgs doublet extensions of the Standard Model that solve the strong problem with profound consequences for the flavor sector. The Yukawa matrices are constrained to have many zero entries by a “Higgs-flavor” symmetry, , that acts on Higgs and quark fields. The violation of both and occurs in the Higgs mass matrix so that, for certain choices of charges, the strong parameter is zero at tree level. Radiative corrections to are computed in this class of theories. They vanish in realistic two-Higgs doublet models with . We also construct realistic three-Higgs models with , where the one-loop results for are model-dependent. Requiring has important implications for the flavor problem by constraining the Yukawa coupling and Higgs mass matrices. Contributions to from higher-dimension operators are computed at one loop and can also be sufficiently small, although the hierarchy problem of this class of theories is worse than in the Standard Model.

Hall, Lawrence↗
DOE OSTI2024

Discontinuity in RG flows across dimensions: entanglement, anomaly coefficients and geometry

We study the entanglement entropy associated with a holographic RG flow from AdS 7 to AdS 4 × $\mathbb{H}$ 3 , where $\mathbb{H}$ 3 is a 3-dimensional hyperbolic manifold with curvature κ. The dual six-dimensional RG flow is disconnected from Lorentz-invariant flows. In this context we address various notions of central charges and identify a monotonic candidate c-function that captures IR aspects of the flow. The UV behavior of the holographic entanglement entropy and, in particular its universal term, display an interesting dependence on the curvature, κ. We then contrast our holographic results with existing field theory computations in six dimensions and find a series of new corrections in curvature to the universal term in the entanglement entropy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Electronic mobility, doping, and defects in epitaxial BaZrS 3 chalcogenide perovskite thin films

We present the electronic transport properties of BaZrS 3 thin films grown epitaxially by gas-source molecular beam epitaxy. We observe n-type behavior in all samples, with carrier concentration ranging from 4 × 10 18 to 4 × 10 20 cm −3 at room temperature (RT). We observe a champion RT Hall mobility of 11.1 cm 2 V −1 s −1 , which is competitive with established thin-film photovoltaic absorbers. Temperature-dependent Hall mobility data show that phonon scattering dominates at room temperature, in agreement with computational predictions. X-ray diffraction data illustrate a correlation between mobility and antiphase boundary concentration, illustrating how microstructure can affect transport. Despite the well-established environmental stability of chalcogenide perovskites, we observe significant changes to electronic properties as a function of storage time in ambient conditions. With the help of secondary ion mass spectrometry measurements, we propose and support a defect mechanism that explains this behavior: as-grown films have a high concentration of sulfur vacancies that are shallow donors (V ⋅ S or V ⋅⋅ S ), which are converted into neutral oxygen defects (O$^{×}_{S}$) upon air exposure. We discuss the relevance of this defect mechanism within the larger context of chalcogenide perovskite research, and we identify means to stabilize the electronic properties.

Van Sambeek, Jack [Massachusetts Institute of Tech↗
DOE OSTI2026

Transient hydroperoxyalkyl intermediates (•QOOH) in isopentane oxidation. II. Isomer-resolved unimolecular dynamics

Transient carbon-centered hydroperoxyalkyl intermediates (•QOOH) in isopentane oxidation are characterized by their time- and energy-resolved unimolecular dissociation dynamics to hydroxyl (OH) and cyclic ether products. Two distinct •QOOH isomers are examined with radical sites at a primary carbon of one of the methyl groups (β-Me) or a secondary carbon (β-Et) of the ethyl group. Energy-dependent unimolecular rates are obtained from the time-dependent appearance of OH products for the two isomers and compared with statistical microcanonical rates computed using RRKM theory, including heavy-atom tunneling, based on high-level theoretical calculations. A benchmark-corrected approach is utilized to compute high-accuracy stationary-point energies, most importantly, transition-state barriers, for the •QOOH_Me and •QOOH_Et isomers in isopentane oxidation, building on higher-level reference calculations for the oxidation of ethane (C 2 H 5 O 2 ) and propane (C 3 H 7 O 2 ), respectively. Here, the measured rates are compared with RRKM calculations incorporating the benchmark-corrected transition-state parameters, a vibrationally adiabatic multidimensional hindered-rotor treatment of key torsions, and quantum tunneling. Agreement between experiment and theory validates the statistical description and shows faster decay for •QOOH_Et due to its lower barrier. Both β-QOOH isomers decay almost exclusively to OH + cyclic ether products under the conditions studied.

Oxidation processes↗
DOE OSTI2026

A New 1D Model for Thermal Mixing and Stratification in Advanced Reactor Transients

Thermal mixing and stratification in large pools and enclosures play a critical role in the safety and performance of pool-type nuclear reactors, particularly during transient scenarios involving significant temperature differences between incoming and bulk coolant. Accurate modeling of these phenomena is essential for predicting system behavior and supporting passive safety features such as natural circulation. Here, this paper presents a new 1D model for thermal mixing and stratification, developed and implemented in the SAM code. The model represents a large pool as 1D coolant jet channels and zero-dimensional bulk pool volumes, enabling the simulation of a wide range of flow configurations, including hot and cold jet interactions, stratified layers, and the influence of complex geometries such as ceilings, free surfaces, and internal obstacles. Heat exchange between jet and pool regions is governed by closure relations calibrated against 3D computational fluid dynamics (CFD) simulations. The model improves upon earlier approaches by incorporating time-dependent jet characteristics and capturing the associated delay effects more accurately. Code-to-code comparisons and validation against experimental data from the Thermal Stratification Test Facility demonstrate the model’s accuracy and flexibility. This work offers two key contributions: (1) an efficient and robust method for simulating thermal mixing and stratification at the system level, eliminating the need for external coupling between system analysis codes and CFD, and (2) a significant enhancement of SAM’s capabilities to analyze thermal stratification phenomena in advanced reactor systems.

SAM↗
DOE OSTI2025

Explaining drivers of housing prices with nonlinear hedonic regressions

Housing markets play a critical role in shaping the spatial and demographic evolution of urban areas. Simulating housing price dynamics can enhance projections of future urban development outcomes. However, traditional hedonic regressions for housing prices, which neglect nonlinear interactions among explanatory variables, often exhibit limited predictive performance. While machine learning (ML) methods can provide a more flexible representation of the relationships between predictors, they are often regarded as “black boxes” due to their complexity and lack of transparency. Interpretable ML techniques provide a promising route by combining the flexibility of ML methods with approaches to analyze the relationships between inputs and outputs. In this study, we employ interpretable ML to analyze the patterns driving the housing market in Baltimore, Maryland, USA. We train an Artificial Neural Network (ANN) to predict Baltimore housing prices based on structural characteristics (e.g., home size, number of stories) and locational attributes (e.g., distance to the city center). We then conduct sensitivity and Partial Dependence Plot (PDP) analyses to interpret the fitted ANN model. We find that the ML model achieves higher predictive accuracy and explains 16 % more of housing price variance than a traditional linear regression model. The interpretable ML model also reveals more nuanced and realistic nonlinear relationships between housing sales price and predictors as well as interactive effects underlying Baltimore home price dynamics. For instance, while the linear model indicates a steady housing price increase over time, our interpretable ML model detects a post-2008 decline, with smaller properties experiencing the sharpest drop.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

HamPerf: A Hamiltonian-Oriented Approach to Quantum Benchmarking

Quantum computing technologies are undergoing rapid development. The different qubit modalities being considered for quantum computing each have their strengths and weaknesses, making it challenging to compare their performance relative to each other and the state-of-the-art in classical high-performance computing. To better understand the utility of a given quantum processor and to assess when and how it will be able to advance the frontiers of computational science, researchers need a robust approach to quantum benchmarking. A variety of approaches have been proposed, many of which characterize the presence of noise in current quantum devices. These efforts include component-level performance metrics, such as randomized benchmarking and gate set tomography; high-level application-dependent metrics; and devicelevel metrics, such as the Quantum Volume. However, it remains unclear how low-level metrics, such as fidelities and decoherence times, and global device metrics, such as Quantum Volume, relate to the computational utility and practical limitations of quantum processors to solve useful problems. In this paper, we describe our Hamiltonian-oriented approach to quantum benchmarking called HamPerf. Where previous application-dependent approaches specify a suite of benchmarking circuits inspired by applications, we place the problem Hamiltonian at the center. Our strategy allows us to probe the computational performance of a quantum processor on standardized and relevant problem sets, agnostic of the algorithms and hardware used to solve them; it also provides fundamental insights into how device characteristics correlate with computational utility.

Butko, Anastasiia↗
DOE OSTI2026

Electrode and Microstructure Dependence of Oxygen Diffusion in Ferroelectric Hafnium Zirconium Oxide Thin Films

Hafnia-based ferroelectrics hold promise to reduce energy demand for computing by enabling compute-in-memory and as non-volatile memories. The ferroelectric phase in this material system is, in part, stabilized by oxygen vacancies. While oxygen vacancies may be a necessity for phase stability, they limit device endurance through diffusion and accumulation into conducting channels. Herein, it is shown that oxygen diffusion is spatially variable within individual grains of ferroelectric hafnium zirconium oxide (HZO). Using 18 O tracers and finite difference modeling, it is shown that grain boundaries and regions near electrode interfaces allow for relatively rapid oxygen diffusion, with values as much as 10 4 larger than the grain cores. Further, the selection of electrode material affects the diffusion coefficients across all microstructural regions. HZO films in contact with TiN electrodes result in more oxygen-deficient HZO films and higher oxygen diffusion coefficients. Tungsten electrodes result in fewer vacancies and lower diffusion coefficients. Diffusion activation energy differences between the HZO with the two electrodes is reconciled by differing populations of charged and uncharged oxygen vacancies. This insight into the local vacancy populations and diffusion pathways provides a platform for designing hafnia-based films, deposition processes, and integration strategies to reduce vacancy gradients and improve performance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Phonon second harmonic generation in NaBr studied by inelastic neutron scattering and computer simulation

The phenomenon of second harmonic generation (SHG) was found for phonons in anharmonic NaBr by inelastic neutron scattering. The temperature dependence of this phonon SHG was measured from 300 K to 650 K. At 300 K the second harmonic (SH) is seen as a high-energy branch around 33 meV, nearly independent of $\overrightarrow{Q}$. The temperature effective potential (TDEP) method and classical molecular dynamics (MD) simulation with machine learning interatomic potential were able to reproduce the SH, and showed that SHG occurs with the flat transverse optical (TO) phonon branch. A classical model of a nonlinear medium explains the intensity and lifetime of the SH, compared to those of the TO modes. Also successful was a quantum model based on the Heisenberg-Langevin equation for interacting phonons coupled to a thermal bath, which also predicts a spectral distribution of the SH. In conclusion, the measured temperature dependence of the intensity of the second harmonic showed that it follows the Planck distribution of a one-phonon quasiparticle, and not two TO phonons.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Scalable learning of potentials to predict time-dependent Hartree–Fock dynamics

We propose a framework to learn the time-dependent Hartree–Fock (TDHF) inter-electronic potential of a molecule from its electron density dynamics. Although the entire TDHF Hamiltonian, including the inter-electronic potential, can be computed from first principles, we use this problem as a testbed to develop strategies that can be applied to learn a priori unknown terms that arise in other methods/approaches to quantum dynamics, e.g., emerging problems such as learning exchange–correlation potentials for time-dependent density functional theory. We develop, train, and test three models of the TDHF inter-electronic potential, each parameterized by a four-index tensor of size up to 60 × 60 × 60 × 60. Two of the models preserve Hermitian symmetry, while one model preserves an eight-fold permutation symmetry that implies Hermitian symmetry. Across seven different molecular systems, we find that accounting for the deeper eight-fold symmetry leads to the best-performing model across three metrics: training efficiency, test set predictive power, and direct comparison of true and learned inter-electronic potentials. All three models, when trained on ensembles of field-free trajectories, generate accurate electron dynamics predictions even in a field-on regime that lies outside the training set. To enable our models to scale to large molecular systems, we derive expressions for Jacobian-vector products that enable iterative, matrix-free training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images

Multispectral imaging (MSI) plays a critical role in material classification, environmental monitoring, and remote sensing. However, MSI sensors typically have wavelength-dependent resolution, which limits downstream analysis. MSI super-resolution (MSI-SR) methods address this limitation by reconstructing all bands at a common high spatial resolution. Existing methods can achieve high reconstruction quality but often rely on spatially-coupled optimization or large learning-based models, leading to significant computational cost and limiting their use in large-scale or time-critical settings. In this paper, we introduce ResSR, a computationally efficient, model-based MSI-SR method that achieves high-quality reconstruction without supervised training or spatially-coupled optimization. Notably, ResSR decouples spectral and spatial processing into two sequential steps. ResSR first computes a spectrally-informed high-resolution estimate of the MSI using singular value decomposition together with a spatially-decoupled approximate forward model. It then applies a residual correction step to restore low-frequency spatial consistency while preserving high-frequency detail recovered by the spectral reconstruction. ResSR achieves comparable or improved reconstruction quality relative to existing MSI-SR methods while being

Sullivan, Haley [ORNL] (ORCID:0000000274069217)↗