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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Gutzwiller hybrid quantum-classical computing approach for correlated materials

Rapid progress in noisy intermediate-scale quantum (NISQ) computing technology has led to the development of novel resource-efficient hybrid quantum-classical algorithms, such as the variational quantum eigensolver (VQE), that can address open challenges in quantum chemistry, physics, and material science. Proof-of-principle quantum chemistry simulations for small molecules have been demonstrated on NISQ devices. While several approaches have been theoretically proposed for correlated materials, NISQ simulations of interacting periodic models on current quantum devices have not yet been demonstrated. Here, we develop a hybrid quantum-classical simulation framework for correlated electron systems based on the Gutzwiller variational embedding approach. We implement this framework on Rigetti quantum processing units (QPUs) and apply it to the periodic Anderson model, which describes a correlated heavy electron band hybridizing with noninteracting conduction electrons. Our simulation results quantitatively reproduce the known ground state quantum phase diagram including metallic, Kondo and Mott insulating phases. This is the first fully self-consistent hybrid quantum-classical simulation of an infinite correlated lattice model executed on QPUs, demonstrating that the Gutzwiller hybrid quantum-classical embedding framework is a powerful approach to simulate correlated materials on NISQ hardware. This benchmark study also puts forth a concrete pathway towards practical quantum advantage on NISQ devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

BACKFLIP Project: Determination of BACKsheet Material Properties: A Comparison of Market-Benchmark Technologies to Novel Non-Fluoro-Based Co-Extruded Backsheet Materials and Their Correlation and Impact on PV Module Degradation Rates

Quantifying degradation rate of backsheets will help procuring materials for 30+ years. Today most backsheets on the market have a laminated PET core; polyolefin materials may provide better properties for backsheets (barrier to water, mechanical properties through UV and hydrolytic environments, etc.). End-of-life regulations may require fluorine-free backsheet products in future global markets. Parametrized equation predicting the service life of backsheets, and validated by both lab and outdoor extensive testing. Technical and commercial confidence to the entire value chain: from backsheet and module manufacturers to the downstream community (IPP, banks, developers) that materials of higher quality can help reduce finance costs, therefore driving down LCOE. A fast track PV module market adaptation for novel high-quality materials, such as co-extruded polyolefin-core backsheets, by obtaining unbiased durability data.

14 SOLAR ENERGY↗
DOE OSTI2025

Machine Learning for Predicting Multipactor Susceptibility in Planar RF Structures

Multipactor discharge is a persistent challenge in high-power microwave (HPM) and accelerator systems, where secondary electron avalanches can cause heating, vacuum degradation, and failure. This work presents the first supervised machine learning (ML) framework for multipactor prediction, trained on high-fidelity 3D Particle-in-Cell (PIC) simulation data in planar geometries. The model maps operational, geometric, and material-dependent secondary electron yield (SEY) parameters to the time-averaged electron growth rate, enabling rapid reconstruction of susceptibility charts. Among the models evaluated, tree-based ensemble methods such as Random Forest and Extra Trees demonstrate superior generalization to unseen materials compared to neural networks such as multilayer perceptron (MLP). Performance metrics, including Intersection over Union (IoU), Structural Similarity Index Measure (SSIM), and Pearson correlation, show close agreement with simulation benchmarks. Principal Component Analysis attributes generalization limits to material feature-space disjointedness.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Corrosion Control in Carbon Fiber Reinforced Plastic (CFRP) Composite-Aluminum Closure Panel Hem Joints

This project targeted the implementation of CFRP/Al closure panels as a drop-in replacement for all-aluminum closures used in automobile production today. The project focused on the development of corrosion fundamentals involving CFRP materials and the use of those materials in mixed-material joints. Numerous studies were completed to benchmark current materials and test mixed metal joints. Good correlation was found between outdoor exposures and laboratory cyclic corrosion testing. The insights from the corrosion testing fundamentals and benchmarking phases informed the development of low-cure adhesive and electrocoats and conductive primers for the CFRP. Adhesive and electrocoat materials were successfully developed meeting low-cure targets for bake (150C/10min). These materials were scaled-up and used to produce 5 prototype liftgates to further evaluate CFRP/Al joints in a full-scale part. The lower cure materials enabled mitigation of CTE mismatch and reduced the required bake temperature, resulting in reduced part distortion. Corrosion testing of the liftgates revealed unexpected increases in corrosion relative to the standard bake materials. While rigorous evaluation of the parts was not possible due to numerous complexities associated with prototype testing, the results provided significant insights into the material behavior of CFRP/Al joints. Key learnings from the project included a better understanding of the system complexity (interplay of substrate/primer conductivity, production of full-scale prototypes), advances in coating/adhesive design for low-cure applications involving CFRP, and advancement of corrosion test protocols for CFRP.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A benchmark of Gutzwiller conjugate gradient minimization method in ground state energy calculations of dimers

Herein we present numerical results of ground-state energies of 9 molecules in the well-established G2 molecule set given by the Gutzwiller conjugate gradient minimization (GCGM) method. The method, beyond the commonly used Gutzwiller approximation, was recently developed based on Gutzwiller variational wave functions. We find that compared to benchmark data given by full configuration interaction, GCGM total energies are reasonably well reproduced with the minimum basis set. To include the dynamical correlation beyond the minimal basis calculations, we adopt the local density approximation for the dynamical correlation energy $E_c$. By comparing the results with benchmark data given by experiments and large-basis configuration interaction, the GCGM total energies with $E_c$ are in general better reproduced, but discrepancies are still observed for some dimers.

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

Thermal modifications of mesons and energy-energy correlators from real-time simulations of a 𝑈⁡(1) lattice gauge theory

We investigate thermal properties of a 𝑈⁡(1) lattice gauge theory in 1 + 1 dimensions through real-time simulations. We extract the spectral functions directly coupling to the pseudoscalar and scalar mesons, demonstrating the thermal modifications of these states with increasing temperatures. Introducing the notion of energy-flow operators, we quantify the temporal buildup of correlations in the energy flows across the lattice. We demonstrate that energy-energy correlators fail to factorize to products of energy flows, both in the vacuum and at nonzero temperature, indicating the presence of nontrivial correlations in the quantum states. Our results constitute a first real-time ab initio study of bound-state thermal broadening and finite temperature energy-flow correlations in a gauge theory, providing a benchmark for future studies of hadronic matter under extreme conditions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Recognizing Unrecognized Sources of Uncertainty (USU) in Nuclear Data

Historically, time-of-flight (TOF) nuclear cross section measurements on different nuclides are assumed to be uncorrelated if they were recorded in different facilities with identical methods, identical facilities with different methods, and even identical facilities with identical methods. Ideally, measurements of different nuclides would truly be uncorrelated thus providing independent assessments of some cross section. In reality, correlations exist between measurements but are simply assumed to be unimportant. To eliminate these qualitative assumptions, in this paper we make a counter-intuitive suggestion to perform an intentionally correlated measurement of energy-differential fission and capture reactions for nuclides in a single criticality safety benchmark during a single experimental campaign. While this would introduce undesirable correlations, it would fully quantify correlations between datasets, rather than assume that the correlations do not exist.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy

We present OrbNet Denali, a machine learning model for an electronic structure that is designed as a drop-in replacement for groundstate density functional theory (DFT) energy calculations. The model is a message-passing graph neural network that uses symmetryadapted atomic orbital features from a low-cost quantum calculation to predict the energy of a molecule. OrbNet Denali is trained on a vast dataset of 2.3 × 10 6 DFT calculations on molecules and geometries. This dataset covers the most common elements in biochemistry and organic chemistry (H, Li, B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, and I) and charged molecules. OrbNet Denali is demonstrated on several well-established benchmark datasets, and we find that it provides accuracy that is on par with modern DFT methods while offering a speedup of up to three orders of magnitude. For the GMTKN55 benchmark set, OrbNet Denali achieves WTMAD-1 and WTMAD-2 scores of 7.19 and 9.84, on par with modern DFT functionals. For several GMTKN55 subsets, which contain chemical problems that are not present in the training set, OrbNet Denali produces a mean absolute error comparable to those of DFT methods. For the Hutchison conformer benchmark set, OrbNet Denali has a median correlation coefficient of R 2 = 0.90 compared to the reference DLPNO-CCSD(T) calculation and R 2 = 0.97 compared to the method used to generate the training data (ωB97X-D3/def2- TZVP), exceeding the performance of any other method with a similar cost. Similarly, the model reaches chemical accuracy for noncovalent interactions in the S66x10 dataset. For torsional profiles, OrbNet Denali reproduces the torsion profiles of ωB97X-D3/def2-TZVP with an average mean absolute error of 0.12 kcal/mol for the potential energy surfaces of the diverse fragments in the TorsionNet500 dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Toward an accurate equation of state and B1-B2 phase boundary for magnesium oxide up to terapascal pressures and electron-volt temperatures

By applying auxiliary-field quantum Monte Carlo, we calculate the equation of state (EOS) and B1-B2 phase transition of magnesium oxide (MgO) up to 1 TPa. Here, the results agree with available experimental data at low pressures and are used to benchmark the performance of various exchange-correlation functionals in density functional theory calculations. We determine PBEsol is an optimal choice for the exchange-correlation functional and perform extensive phonon and quantum molecular-dynamics calculations to obtain the thermal EOS. Our results provide a preliminary reference for the EOS and B1-B2 phase boundary of MgO from zero up to 10,500 K.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

(U) Correlated Sampling Using Batch Statistics to Reduce the Uncertainty of Combinations of KSEN Outputs with MCNP6

The relative sensitivity of k eff to the densities of nuclides in a material are combined to compute relative sensitivities to other inputs. When computed in a single Monte Carlo run, the nuclide density sensitivities are correlated, and the statistical uncertainties propagated to other inputs will be incorrect unless those correlations are accounted for. Equations are presented to apply correlated sampling using batch statistics for the sum of an arbitrary number of random tallies and the difference of two random tallies when each is multiplied by a different constant. When correlated sampling is used on a recent benchmark evaluation, the correct statistical uncertainties for certain combinations of sensitivities are dramatically smaller than the incorrect uncertainties. A user-controlled, regular output of MCNP6’s KSEN sensitivities that allows batch statistics to be applied to combinations would be extremely valuable.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Correlated Dirac–Coulomb–Breit multiconfigurational self-consistent-field methods

The fully correlated frequency-independent Dirac–Coulomb–Breit Hamiltonian provides the most accurate description of electron–electron interaction before going to a genuine relativistic quantum electrodynamics theory of many-electron systems. In this work, we introduce a correlated Dirac–Coulomb–Breit multiconfigurational self-consistent-field method within the frameworks of complete active space and density matrix renormalization group. In this approach, the Dirac–Coulomb–Breit Hamiltonian is included variationally in both the mean-field and correlated electron treatment. Here, we also analyze the importance of the Breit operator in electron correlation and the rotation between the positive- and negative-orbital space in the no-virtual-pair approximation. Atomic fine-structure splittings and lanthanide contraction in diatomic fluorides are used as benchmark studies to understand the contribution from the Breit correlation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Benchmark of interfacial area concentration approaches for the two-fluid model in gas-dispersed condensing flow

Accurate predictions of interfacial area concentration (IAC) are required for closure of the condensation phase change term. The interfacial area transport equation (IATE) can dynamically predict interfacial area concentration and is increasingly being used in the benchmarking of phase change flows over static based correlations which require a flow regime map. For higher flow regimes, the two-group two-fluid IATE model can separately model the transport of spherical bubbles and cap/slug bubbles. Recently, a modified formulation of the one-group and two-group two-fluid model with interfacial area transport equation was proposed for gas-dispersed condensing flows. Three datasets have been used for validation consisting of condensing two-phase flow data in the unheated section of a vertical annulus with conditions spanning a range of pressures, inlet subcoolings and mass fluxes for benchmarking the various IAC approaches. For bubbly flows, the coupled void transport - IATE simulation results in good agreement in the predictions of the void fraction and the interfacial area concentration under nearly all sixty conditions. Considering the proposed two-group two-fluid IATE model formulation, the group-2 Nusselt number correlation is investigated to account for the appropriate heat transfer length scale for group-2 bubbles. Validation of the proposed two-group two-fluid model against the new dataset shows good agreement with experimental data with significant improvement over the one-group IATE model for conditions with substantial group-2 void fraction throughout the simulation domain.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

A quantum eigenvalue solver based on tensor networks

Electronic ground states are of central importance in chemical simulations, but have remained beyond the reach of efficient classical algorithms except in cases of weak electron correlation or one-dimensional spatial geometry. We introduce a hybrid quantum-classical eigenvalue solver that constructs a wavefunction ansatz from a linear combination of matrix product states in rotated orbital bases, enabling the characterization of strongly correlated ground states with arbitrary spatial geometry. The energy is converged via a gradient-free generalized sweep algorithm based on quantum subspace diagonalization, with a potentially exponential speedup in the off-diagonal matrix element contractions upon translation into compact quantum circuits of linear depth in the number of qubits. Chemical accuracy is attained in numerical experiments for both a stretched water molecule and an octahedral arrangement of hydrogen atoms, achieving substantially better correlation energies compared to a unitary coupled-cluster benchmark, with orders of magnitude reductions in quantum resource estimates and a surprisingly high tolerance to shot noise. This proof-of-concept study suggests a promising new avenue for scaling up simulations of strongly correlated chemical systems on near-term quantum hardware.

chemistry↗
DOE OSTI2025

Machine learning-accelerated path integral molecular dynamics simulations of reactive organic electrolytes

Hydrogen bonded electrolytes that exhibit accelerated proton transport via sequential reactive hops have drawn interest for their promise in clean energy applications. Molecular dynamics simulations of these electrolytes offer the opportunity to uncover microscopic mechanistic details that could be used to design and tune the properties of candidate electrolyte technologies. However, accurately modeling the proton transfer reactions and transport properties that give rise to high charge conductivites in these electrolytes proves computationally challenging because of the need to perform lengthy condensed phase simulations, treating both the electronic and nuclear degrees of freedom quantum mechanically. In this paper, we demonstrate that such a modeling task can be efficiently achieved with the use of density functional theory (DFT)-trained machine learning potentials (MLP) to accelerate path integral molecular dynamics (PIMD) simulations. We highlight the practical utility of this approach by using it to benchmark how closely PIMD simulations employing different DFT exchange–correlation functionals reproduce the composition-dependent densities, diffusion coefficients, and electrical conductivities of mixtures consisting of imidazole and levulinic acid. Even with the speedup afforded by our MLPs, PIMD simulations remain quite expensive. Furthermore, in order to render PIMD more computationally tractable, we introduce and benchmark the accuracy of a ring polymer contraction approach that leverages a computationally efficient short-range MLP to accelerate our PIMD simulations by an additional factor of four.

Chemical bonding↗
DOE OSTI2024

Pebble Tanker Model for Nuclear Criticality Safety Needs

This report documents a study performed to investigate the requirements for criticality safety benchmark experiments for high-assay, low-enriched uranium (HALEU) fuel in transportation applications. In this work, an exploratory application model, the “Pebble Tanker,” was developed to represent TRISO fuel in a transportation scenario for an analysis of the validation basis in industrial quantities. An aspect of the criticality validation process involves assessing the “similarity” between application and experimental benchmark systems through an integral index parameter evaluation. Here, this includes propagating nuclear data uncertainties and calculating a correlation coefficient (hereinafter referred to as “c k ”) to evaluate the similarity of benchmark experiments compared with the application Pebble Tanker model. Finding sufficient critical benchmark experiments allows for the evaluation of bias and bias uncertainty, thus determining the upper subcritical limit (USL) of the transportation package. A target k eff of ~0.94 was used in this work to establish appropriate modeling conditions, reflecting a reasonable estimate for a USL. Two container models were investigated: one with the Hermes-type pebble and one with the Pebble Bed Modular Reactor (PBMR)–type pebble. The models were simplified, considering only fuel, containment structure, and either water or air. This allows a focus on the underlying physics of applications involving TRISO fuel pebbles using the Pebble Tanker model. A crucial consideration is the transport package's ability to safely hold pebbles while flooded, maintaining subcritical conditions. Tools available in the SCALE 6.3.1 suite—the CSAS6-Shift, TSUNAMI-3D-Shift, and TSUNAMI-IP sequences—were employed for neutronics and sensitivity and uncertainty (S/U) analysis of the Pebble Tanker. Findings demonstrated sufficient available critical experiment benchmarks to perform a validation of the Pebble Tanker in the most reactive state, i.e., when the Tanker is flooded.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. Furthermore, we observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Comparative Study on Representativeness and Stochastic Efficacy of Miniature Tensile Specimen Testing

Here, in this article, a miniature dog bone tensile coupon design was tested against the existing ASTM standard specimen design. Specimens were prepared from commercially sourced austenitic stainless steel 304 alloy, and a defect-ridden additively manufactured 304L alloy was studied. By utilizing a tensile specimen design that is 1/230th volume of the smallest ASTM E8-04(2016), Standard Test Methods for Tension Testing of Metallic Materials, dog bone specimen, coupled to a digital image correlation (DIC) setup, case studies were performed to compare tensile property measurements and strain field evolution. Whereas yield strength measurements were observed to be similar, post-yield, the ultimate strength measurements and ductility measurements from the miniature specimens were observed to be higher than the ASTM specimen design. Although the strength measurements were comparable, the strain evolution was found to differ in the miniature specimens. Studies to assess effects of varying thickness and defect population were also pursued on the miniature tensile specimen. From the DIC strain field estimations, the peak local strain values at ultimate tensile strength were observed to be increasing with reducing specimen thickness. Testing of defect ridden stainless steel revealed the sensitivity to failure through strain localization and the influence of defect size was captured in the strength measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Thermodynamics of Metal Carbonates and Bicarbonates and Their Hydrates for Mg, Ca, Fe, and Cd Relevant to Mineral Energetics

The heats of formation of carbonate, bicarbonate and bicarbonate/hydroxide metal complexes and hydrated versions these complexes of Mg 2+ , Ca 2+ , Fe 2+ , and Cd 2+ are predicted from atomization energies using correlated molecular orbital theory at the CCSD(T) level extrapolated to the complete basis set limit following the Feller-Peterson-Dixon (FPD) approach. Using the calculated gas phase values and the available experimental solid state values, the cohesive energies of selected minerals were calculated. The gas phase decomposition energies into MO, CO 2 and H 2 O follow the order Mg ~ Ca > Cd ~ Fe, and correlate with the hardness of the metal +2 ions. Gas phase hydration energies show that the order is Mg > Fe > Ca ~ Cd. There are a number of bulk hydrated Mg and Ca complexes that occur as minerals but there are few if any for Fe and Cd, suggesting that a number of factors are important in determining the stability of the bulk mineral hydrates. The FPD heats of formation were used to benchmark a range of density functional theory exchange-correlation functionals, including those commonly used in solid state mineral calculations. None of the functionals provided chemical accuracy agreement (± 1 kcal/mol) with the FPD results The best functionals are ωB97X and ωB97X-D with mean average unsigned errors of 10 kcal/mol.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗