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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Irradiation-Induced Grain Growth of gamma-Fe2O3 and Fe3O4 at Cryogenic Temperatures

This work reports the first observations of grain growth under irradiation of Fe oxides, specifically maghemite gamma-Fe2O3 and magnetite Fe3O4. The Fe oxide thin films were grown by Pulsed Laser Deposition and irradiated in-situ in a Transmission Electron Microscope at - 223°C using 1 MeV Kr2+ ions up to 8.75 x 10^19 ions/m^2 to study grain growth kinetics under irradiation. Grain growth at such low temperatures appears to follow kinetics that can be accounted for by the thermal spike model developed for metals in the literature. The corresponding activation energies were calculated and compared with other oxides. The results demonstrate that gamma-Fe2O3, despite its larger initial grain size, exhibits surprisingly fast grain growth in comparison with Fe3O4, likely due to the presence of iron vacancies in the crystal structure that facilitate faster atomic diffusion under irradiation.

Kretov, Dmitrii↗
DOE OSTI2022

Exploring Structure-Sensitive Relations for Small Species Adsorption Using Machine Learning

Accurate prediction of adsorption energies on heterogeneous catalyst surfaces is crucial to predicting reactivity and screening materials. Adsorption linear scaling relations have been developed extensively but often lack accuracy and apply to one adsorbate and a single binding site type at a time. These facts undermine their ability to predict structure sensitivity and optimal catalyst structure. Using machine learning on nearly 300 density functional theory calculations, we demonstrate that generalized coordination number scaling relations hold well for oxygen- and high-valency carbon-binding species but fail for others. Here we reveal that the valency and the electronic coupling of a species with the surface, along with the site type and its coordination environment, are critical for small species adsorption. The model simultaneously predicts the adsorption energy and preferred site and significantly outperforms linear scalings in accuracy. It can expose the structure sensitivity of chemical reactions and enable enhanced catalyst activity via engineering particle shape and facet defects. The generality of our methodology is validated by training the model with transition metal data and transferring it to predict adsorption energies on single-atom alloys.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Machine Learning Accelerated First-Principles Study of the Hydrodeoxygenation of Propanoic Acid

The complex reaction network of catalytic biomass conversions often involves hundreds of surface intermediates and thousands of reaction steps, greatly hindering the rational design of metal catalysts for these conversions. Here, we present a framework of machine learning (ML)-accelerated first-principles studies for the hydrodeoxygenation (HDO) of propanoic acid over transition metal surfaces. The microkinetic model (MKM) is initially parametrized by ML-predicted energies and iteratively improved by identifying the rate-determining species and steps (RDS), computing their energies by density functional theory (DFT), and reparameterizing the MKM until all the RDS are computed by DFT. The Gaussian process (GP) model performs significantly better than the linear ridge regression model for predicting both the adsorption free energies and transition state free energies. Parameterized with energies from the GP model, only 5–20% of the full reaction network has to be computed by DFT for the MKM to possess DFT-level accuracy for the TOF and dominant reaction pathway. While the linear ridge regression model performs worse than the GP model, its performance is greatly improved when only transition states are predicted by the regression model and adsorption energies are computed by DFT. Overall, we find that a high accuracy in adsorption free energies is more important for a reliable MKM than a high accuracy in TS free energies. Lastly, based on the GP model with GOH and GCHCHCO as catalyst descriptors, we build two-dimensional volcano plots in activity and selectivity that can help design promising alloy catalysts for HDO reactions of organic acids.

adsorption↗
DOE OSTI2023

Revealing quasi-excitations in the low-density homogeneous electron gas with model exchange–correlation kernels

Time-dependent density functional theory within the linear response regime provides a solid mathematical framework to capture excitations. The accuracy of the theory, however, largely depends on the approximations for the exchange–correlation (xc) kernels. Away from the long-wavelength (or q = 0 short wave-vector) and zero-frequency (ω = 0) limit, the correlation contribution to the kernel becomes more relevant and dominant over exchange. The dielectric function, in principle, can encompass xc effects relevant to describe low-density physics. Furthermore, besides collective plasmon excitations, the dielectric function can reveal collective electron–hole excitations, often dubbed “ghost excitons.” Besides collective excitons, the physics of the low-density regime is rich, as exemplified by a static charge-density wave that was recently found for r s > 69, and was shown to be associated with softening of the plasmon mode. These excitations are seen to be present in much higher density 2D homogeneous electron gases of r s ≳ 4. Here, in this work, we perform a thorough analysis with xc model kernels for excitations of various nature. The uniform electron gas, as a useful model of real metallic systems, is used as a platform for our analysis. We highlight the relevance of exact constraints as we display and explain screening and excitations in the low-density region.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Out of time order correlation of the Hubbard model with random local disorder

The out-of-time-order correlator (OTOC) serves as a powerful tool for investigating quantum information spreading and chaos in complex systems. We present a method employing non-equilibrium dynamical mean-field theory and coherent potential approximation combined with diagrammatic perturbation on the Schwinger–Keldysh contour to calculate the OTOC for correlated fermionic systems subjected to both random disorder and electron interaction. Furthermore, our key finding is that random disorder enhances the OTOC decay in the Hubbard model for the metallic phase in the weakly interacting limit. However, the current limitation of our perturbative solver restricts the applicability to weak interaction regimes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Obtaining bulk-like correlated oxide surfaces with protective caps

Functional oxides exhibit a diverse range of correlated electron phenomena, some of which are highly attractive for novel electronic, magnetic, and optical devices. Despite decades of advancement of our fundamental understanding of these materials, they consistently fall short of realizing their promise in functional devices. We identify a significant bottleneck toward device realization to be surface overoxidation. Protective caps can effectively prevent overoxidation, but their interfaces with functional oxides are not well understood. These interfaces are critical for effectively using functional oxides in field-effect devices, where “the interface is the device.” This work addresses the chemistry and physics of the interface between protective caps and the correlated metal SrVO3, a model functional oxide. Our comparison of five different cap materials reveals effective protection and similar SrVO3 surface chemistry in all cases. Systematic comparisons of surface and bulk-sensitive photoelectron spectra reveal that negligible interface redox takes place, elucidating the cap-SrVO3 interface chemistry. This work demonstrates a robust and simple solution to the surface overoxidation problem in vanadates, paving the way toward effectively using these materials in field-effect devices. Our conclusions are general and can be applied to numerous other systems, thus moving oxide electronics closer to the realization of functional devices.

Cohen, Amit (ORCID:0009000276477510)↗
DOE OSTI2023

Machine learning the electronic structure of matter across temperatures

Here, we introduce machine learning (ML) models that predict the electronic structure of materials across a wide temperature range. Our models employ neural networks and are trained on density functional theory (DFT) data. Unlike most other ML models that use DFT data, our models directly predict the local density of states (LDOS) of the electronic structure. This provides several advantages, including access to multiple observables such as the electronic density and electronic total free energy. Moreover, our models account for both the electronic and ionic temperatures independently, making them ideal for applications like laser heating of matter. We validate the efficacy of our LDOS-based models on a metallic test system. They accurately capture energetic effects induced by variations in ionic and electronic temperatures over a broad temperature range, even when trained on a subset of these temperatures. These findings open up exciting opportunities for investigating the electronic structure of materials under both ambient and extreme conditions.

Fiedler, Lenz↗
DOE OSTI2022

Impact of nuclear data covariance libraries on uncertainty quantification of sodium cooled fast reactor simulation

Uncertainty quantification in core modelling to assess reactor performance is a regular practice to identify design safety limits. Best-estimate plus uncertainty (BEPU) methods are commonly used to propagate uncertainties to core responses from input parameters, characterized by variance-covariance libraries. This study evaluates the impact of different covariance libraries on the uncertainty associated with core safety parameters and their propagation through reactor transient modelling for a metallic core (ABR-1000). The COMAC-1.0, COMMARA-2.0, and COMMARA-2.1 covariance libraries are used to propagate nuclear data uncertainties to neutron feedback coefficients using the Generalized Perturbation Theory, then through transient safety analyses to establish the confidence interval for safety performance of the reactor using stochastic sampling. The considered transients include Unprotected Transient Over Power and Unprotected Loss Of Flow. Most influential nuclide and reaction pairs in different variance-covariance libraries are identified and their impact on the uncertainty of safety parameters are evaluated. Significant differences were observed between uncertainties from COMAC compared to COMMARA libraries. Uncertainties from COMMARA libraries were consistently larger than COMAC for all cases. For structure, coolant density feedback coefficients and control rod worth, total nuclear data uncertainties were approximately 50% larger for COMMARA than COMAC. Differences were also observed in identification of top contributors of uncertainty in terms of reaction-cross sections. For instance, largest contribution to total uncertainty of radial expansion coefficient from COMMARA-2.0 and 2.1 originates from Na{sup 23} P1 Elastic cross section (0.81%) but from Pu{sup 239} Fission (0.53%) with COM C-1.0. Such differences are evaluated in this research for all quantities of interest. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Metallic Fuel Benchmark Simulations Based on the X430 Experiments

To gauge the accuracy of current BISON metallic fuel performance models and identify models that require further development, Oak Ridge National Laboratory has developed benchmark problems based on two pins of the X430 experiment. These benchmarks recreate the experimental conditions of the X430 series of experiments with as much accuracy as possible given the available data. The benchmarks were implemented in the BISON nuclear fuel performance code and the simulation results were compared with the experimental results to quantify the simulation accuracy. Findings in this work were consistent with previous benchmark problems. The temperature profiles, burnup, and fission gas behavior were all as accurate as can reasonably be expected. The cladding radial growth was accurate enough to not affect the results of other measurements. The fuel axial growth was accurate for U-Zr fuel but not for U-Pu-Zr fuel, which appears to be caused by a lack of U-Pu-Zr data. Swelling correlations have not been developed for U-Pu-Zr fuel because there were not enough data on this fuel type, so BISON used the U-Zr correlations. Finally, recommendations were made for near-term BISON development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Towards Ab-Initio Simulations of Crystalline Defects at the Exascale Using Spectral Quadrature Density Functional Theory

Defects in crystalline solids play a crucial role in determining properties of materials at the nano, meso- and macroscales, such as the coalescence of vacancies at the nanoscale to form voids and prismatic dislocation loops or diffusion and segregation of solutes to nucleate precipitates, phase transitions in magnetic materials via disorder and doping. First principles Density Functional Theory (DFT) simulations can provide a detailed understanding of these phenomena. However, the number of atoms needed to correctly simulate these systems is often beyond the reach of many widely used DFT codes. The aim of this article is to discuss recent advances in first principles modeling of crystal defects using the spectral quadrature method. The spectral quadrature method is linear scaling with respect to the number of atoms, permits spatial coarse-graining, and is capable of simulating non-periodic systems embedded in a bulk environment, which allows the application of appropriate boundary conditions for simulations of crystalline defects. In this article, we discuss the state-of-the-art in ab-initio modeling of large metallic systems of the order of several thousand atoms that are suitable for utilizing exascale computing resourses.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The Hubble PanCET Program: A Metal-rich Atmosphere for the Inflated Hot Jupiter HAT-P-41b

We present a comprehensive analysis of the 0.3–5 μm transit spectrum for the inflated hot Jupiter HAT-P-41b. The planet was observed in transit with Hubble STIS and WFC3 as part of the Hubble Panchromatic Comparative Exoplanet Treasury (PanCET) program, and we combine those data with warm Spitzer transit observations. We extract transit depths from each of the data sets, presenting the STIS transit spectrum (0.29–0.93 μm) for the first time. We retrieve the transit spectrum both with a free-chemistry retrieval suite (AURA) and a complementary chemical equilibrium retrieval suite (PLATON) to constrain the atmospheric properties at the day–night terminator. Both methods provide an excellent fit to the observed spectrum. Both AURA and PLATON retrieve a metal-rich atmosphere for almost all model assumptions (most likely O/H ratio of log{sub 10}Z/Z{sub ⊙}=1.46{sub −0.68}{sup +0.53} and log{sub 10}Z/Z{sub ⊙}=2.33{sub −0.25}{sup +0.23}, respectively); this is driven by a 4.9σ detection of H{sub 2}O as well as evidence of gas absorption in the optical (>2.7σ detection) due to Na, AlO, and/or VO/TiO, though no individual species is strongly detected. Both retrievals determine the transit spectrum to be consistent with a clear atmosphere, with no evidence of haze or high-altitude clouds. Interior modeling constraints on the maximum atmospheric metallicity (log{sub 10}Z/Z{sub ⊙}<1.7) favor the AURA results. The inferred elemental oxygen abundance suggests that HAT-P-41b has one of the most metal-rich atmospheres of any hot Jupiters known to date. Overall, the inferred high metallicity and high inflation make HAT-P-41b an interesting test case for planet formation theories.

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

Confined and deconfined chaos in classical spin systems

Weakly perturbed integrable many-body systems are typically chaotic, and thermal at late times. However, there are distinct relationships between the timescales for thermalization and chaos. The typical relationship is confined chaos: when trajectories are still confined to regions in phase space with constant conserved quantities (actions), the conjugate angle variables are already unstable. Chaotic instabilities thus far precede thermalization. In a different relationship, which we term deconfined chaos, chaotic instabilities and thermalization occur on the same timescale. We investigate these two qualitatively distinct scenarios through numerical and analytical studies of two perturbed integrable classical spin models: the Ishimori spin chain (confined chaos), and the central spin model with XX interactions (deconfined chaos). We analytically establish (super)-integrability in the latter model in a microcanonical shell. Deconfined chaos emerges through the separation of phase space into large quasi-integrable regions and a thin chaotic manifold. The latter leads to chaos and thermalization on the fastest possible timescale, which is proportional to the inverse perturbation strength. This behavior is reminiscent of the quantum SYK models and strange metals.

Chaotic Dynamics (nlin.CD)↗
DOE OSTI2022

MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks

Abstract We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal–organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Commutative Algebra Modeling in Materials Science – A Case Study on Metal–Organic Frameworks (MOFs)

Metal-organic frameworks (MOFs) are a class of important crystalline and highly porous materials whose hierarchical geometry and chemistry hinder interpretable predictions in materials properties. Commutative algebra is a branch of abstract algebra that has been rarely applied in data and material sciences. We introduce the first ever commutative algebra modeling and prediction in materials science. Specifically, category-specific commutative algebra (CSCA) is proposed as a new framework for MOF representation and learning. It integrates element-based categorization with multiscale algebraic invariants to encode both local coordination motifs and global network organization of MOFs. These algebraically consistent, chemically aware representations enable compact, interpretable, and data efficient modeling of MOF properties such as Henry’s constants and uptake capacities for common gases. Compared to traditional geometric and graph-based approaches, CSCA achieves comparable or superior predictive accuracy while substantially improving interpretability and stability across data sets. By aligning commutative algebra with the chemical hierarchy, the CSCA establishes a rigorous and generalizable paradigm for understanding structure and property relationships in porous materials and provides a nonlinear algebra-based framework for data-driven material discovery.

Khaemba, Caleb S.↗
DOE OSTI2024

Optimization and Multimachine Learning Algorithms to Predict Nanometal Surface Area Transfer Parameters for Gold and Silver Nanoparticles

Interactions between gold metallic nanoparticles and molecular dyes have been well described by the nanometal surface energy transfer (NSET) mechanism. However, the expansion and testing of this model for nanoparticles of different metal composition is needed to develop a greater variety of nanosensors for medical and commercial applications. In this study, the NSET formula was slightly modified in the size-dependent dampening constant and skin depth terms to allow for modeling of different metals as well as testing the quenching effects created by variously sized gold, silver, copper, and platinum nanoparticles. Overall, the metal nanoparticles followed more closely the NSET prediction than for Förster resonance energy transfer, though scattering effects began to occur at 20 nm in the nanoparticle diameter. To further improve the NSET theoretical equation, an attempt was made to set a best-fit line of the NSET theoretical equation curve onto the Au and Ag data points. An exhaustive grid search optimizer was applied in the ranges for two variables, 0.1≤C≤2.0 and 0≤α≤4, representing the metal dampening constant and the orientation of donor to the metal surface, respectively. Three different grid searches, starting from coarse (entire range) to finer (narrower range), resulted in more than one million total calculations with values C=2.0 and α=0.0736. The results improved the calculation, but further analysis needed to be conducted in order to find any additional missing physics. With that motivation, two artificial intelligence/machine learning (AI/ML) algorithms, multilayer perception and least absolute shrinkage and selection operator regression, gave a correlation coefficient, R2, greater than 0.97, indicating that the small dataset was not overfitting and was method-independent. This analysis indicates that an investigation is warranted to focus on deeper physics informed machine learning for the NSET equations.

Demers, Steven M. E. (ORCID:0000000192213246)↗
DOE OSTI2023

Application of an Empirical Density Law via Python for Aqueous Plutonium Chloride Systems in MCNP6

Current aqueous plutonium processing models for criticality safety often contain significant bias due to material modeling assumptions. These solutions include plutonium chloride solutions, which are modeled as fictitious plutonium metal-water mixtures because little is known about the actual density of the solution. Furthermore, there is no current predictive capability for modeling plutonium metal-water mixtures that is approved for use at Los Alamos National Laboratory (LANL). Recent density measurements for aqueous plutonium chloride systems (PuCl 3 -HCl-H 2 O) now allow for the development and application of a more realistic density law. This work develops a Python-based density law for this ternary solution using an empirical method. This code can be used in conjunction with an MCNP6 input to determine the density and composition of a solution based upon user inputs of plutonium concentration, hydrochloric acid concentration, and temperature. The tool allows users to input plutonium and acid content of a solution in terms of molality, molarity, or concentration, and predicts density within the current data range within 1.4% of experimental data. Current preliminary MCNP6 calculations utilizing this tool have demonstrated a minimum decrease in system reactivity of 5% in comparison to the current modeling conventions. Thus, this tool enables more accurate criticality safety operational limits by better crediting chorine content while still maintaining necessary conservatism.

Density Law↗
DOE OSTI2020

Visualizing strange metallic correlations in the two-dimensional Fermi-Hubbard model with artificial intelligence

Strongly correlated phases of matter are often described in terms of straightforward electronic patterns. This has so far been the basis for studying the Fermi-Hubbard model realized with ultracold atoms. Here, we show that artificial intelligence (AI) can provide an unbiased alternative to this paradigm for phases with subtle, or even unknown, patterns. Long- and short-range spin correlations spontaneously emerge in filters of a convolutional neural network trained on snapshots of single atomic species. In the less well-understood strange metallic phase of the model, we find that a more complex network trained on snapshots of local moments produces an effective order parameter for the non-Fermi-liquid behavior. We report our technique can be employed to characterize correlations unique to other phases with no obvious order parameters or signatures in projective measurements, and has implications for science discovery through AI beyond strongly correlated systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Dynamic modelling and control strategy of a temperature-driven metal hydride cooling system for buildings

A temperature-driven coupled metal hydride (MH) based thermal energy storage (TES) system can allow to shave and shift the peak energy demand in buildings. The high energy density and long-term (seasonal) energy storage capability are its major advantages over other energy storage methods. The dynamic nature of the MH operation, however, requires controlled hydrogen transfer between the coupled MHs at a rate needed to meet the building's transient load. While temperature-driven MH systems are studied in the literature, their application in buildings and control are scarcely reported. Here, this paper presents a control-based dynamic modeling of the temperature-driven coupled MH-TES system for building cooling applications. The dynamic model is developed in MATLAB(R) Simulink environment, considering the thermodynamic and kinetic behaviors of the MH systems. Based on a preliminary analysis of a property database of over 337 hydrides, we select around 1600 MH pairs suitable for building cooling applications. Each of these MH pairs is studied for their performance using the dynamic model, and among all, Zr 0.76 Ti 0.24 Ni 1.16 Mn 0.63 V 0.14 Fe 0.18 -Ti 0.85 Zr 0.15 Cr 1.2 Mn 0.8 MH pair showed fast dynamics along with high coefficient of performance (COP) of 0.71. A parametric investigation is performed on this MH pair to understand the effect of operating temperatures. Finally, three proportional-integral (PI) feedback controllers are investigated to regulate the temperature, pressure and mass exchange between the coupled MH pairs. The developed PI controller is sufficiently capable of rejecting the signal noise from the hydrogen flow and internal heat exchange processes with root mean square error of 5.78 W between reference and actual cooling load.

08 HYDROGEN↗