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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 OSTI2020

Computational and Experimental Characterization of Intermediate Amorphous Phases in Geological Materials

In the subsurface, MgO engineered barriers are employed at the Waste Isolation Pilot Plant (WIPP), a transuranic waste repository near Carlsbad, NM. During service, the MgO will be exposed to high concentration brine environments and may form stable intermediate phases that can alter the barriers effectiveness. Here, MgO was aged in water and three different brine solutions. X-ray diffraction (XRD) and 1 H nuclear magnetic resonance (NMR) analysis were performed to identify the formation of secondary phases. After aging, ~4% of the MgO was hydrated and fine-grained powders resulted in greater loss of crystallinity than hard granular grains. 1 H magic angle spinning (MAS) NMR spectra resolved minor phases not visible in XRD, indicating that diverse 1 H environments are present along with Mg(OH) 2 . Density functional theory (DFT) simulations for several proposed Mg-O-H, Mg-CI-O-H, and Na-O-H containing phases were performed to index peaks in the experimental 1 H MAS NMR spectra. While proposed intermediate crystal structures exhibited overlapping 1 H NMR peaks, Mg-O-H intermediates were attributed to the growth of the 1.0-0.0ppm peak while the Mg-CI-O-H structures contributed to the 2.5- 5.0ppm peak in the chloride containing brines. Overall, NMR analysis of aged MgO indicates the formation of a range of possible intermediate structures that cannot be resolved with XRD analysis alone.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Carbon Capture in Novel Porous Liquids

Direct air capture (DAC) of CO 2 is one of the negative emission technologies under development to limit the impacts of climate change. The dilute concentration of CO 2 in the atmosphere (~400 ppm) requires new materials for carbon capture with increased CO 2 selectivity that is not met with current materials. Porous liquids (PLs) are an emerging material that consist of a combination of solvents and porous hosts creating a liquid with permanent porosity. PLs have demonstrated excellent CO 2 selectivity, but the features that control how and why PLs selectively capture CO 2 is unknown. To elucidate these mechanisms, density functional theory (DFT) simulations were used to investigate two different PLs. The first is a ZIF-8 porous host in a water/glycol/2-methylimidazole solvent. The second is the CC13 porous organic cage with multiple bulky solvents. DFT simulations identified that in both systems, CO 2 preferentially bound in the pore window rather than in the internal pore space, identifying that the solvent-porous host interface controls the CO 2 selectivity. Additionally, SNL synthesized ZIF-8 based PL compositions. Evaluation of the long-term stability of the PL identified no change in the ZIF-8 crystallinity after multiple agitation cycles, identifying its potential for use in carbon capture systems. Through this project, SNL has developed a fundamental understanding of solvent-host interactions, as well as how and where CO 2 binds in PLs. Through these results, future efforts will focus not on how CO 2 behaves inside the pore, but on the porous host-solvent interface as the driving force for PL stability and CO 2 selectivity.

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

Tritium Trapping Thermodynamics by Point Defects at Interfacial Fe-Al Phases of the Aluminide Coating

Density functional theory (DFT) simulations have been carried out to evaluate the potential for tritium trapping by metal vacancies in four phases (i.e., (Fe, Cr, Ni), AlFe 3 , NiAl, and AlFe) identified near the interface between the Al coating and 316 stainless steel (316 SS) cladding. In addition, an ab initio thermodynamics approach has been employed to predict the temperature and T 2 -partial pressure dependence on the thermodynamics of tritiated defects. Key results in this work suggest that metal vacancies in the four phases have the potential to favorably trap tritium species. This thermodynamic trend can be correlated to the high energy cost of having an interstitial tritium in the lattice, which has been calculated to be at least 0.48 eV. By combining the results of this study with previous theoretical works investigating tritium behavior in other Fe-Al phases identified in the aluminide coating, suggest that metal vacancies are generally able to trap tritium in various Fe-Al aluminide phases. Especially, it was found that Al vacancies are the most efficient to trap tritium, followed by Fe vacancies, then Ni and Cr vacancies. Strong interactions between tritium and the metal vacancies are occurring by the formation of Fe—T bonds. The formation of Cr—T, Ni—T, or Al—T bonds are found less energetically favorable than Fe—T bonds.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Thermodynamics of Tritium Trapping by Point Defects at Interfacial Cr-based Phases of the Al Coating

Density functional theory (DFT) simulations have been carried out to evaluate the potential for tritium trapping by metal vacancies in four phases Cr-containing phases (i.e., Cr3Si, Al0.3Cr0.7, Al8Cr5-HT, and Al8Cr5) identified near the interface between the Al coating and 316 stainless steel (316 SS) cladding. In addition, an ab initio thermodynamics approach has been employed to predict the temperature and T2-partial pressure dependence on the thermodynamics of singly tritiated defects. Key results in this work suggest that metal vacancies in the four phases have the potential to favorably trap tritium species, especially Si vacancies in Cr3Si phase. This overall thermodynamic trend can be correlated to the energy cost of having an interstitial tritium in the lattice, which has been calculated to range from 0.17 eV in Al8Cr5-HT to 0.89 eV in Cr3Si. A comparison of the results from this study with previous theoretical works investigating tritium behavior in other Fe-Al phases identified in the aluminide coating, suggests that metal vacancies are generally able to trap tritium in various Fe-Al aluminide phases. Especially, it was found that Si and Al vacancies would be the most efficient to trap for tritium, followed by Cr vacancies, then Fe and Ni vacancies. In the four Cr-based material phases investigated in this work, it is interesting to note that, in the absences of Fe or Ni species, strong interactions between tritium and the metal vacancies are always occurring by the formation of preferential Cr—T bonds (i.e., no Si—T or Al—T bonds were formed).

Sassi, Michel J.↗
DOE OSTI2021

Towards Efficient Uncertainty estimation in deep learning for robust energy prediction in crystal materials

In material science, recent studies have started to explore the potential of using deep learning to improve property prediction from high-fidelity simulations, e.g, density functional theory (DFT). However, the design spaces are sometimes too large and intractable to sample completely. This results in a critical question that is how to evaluate the confidence and robustness of the prediction. In this paper, we propose an efficient approach to estimate uncertainty in deep learning using a single forward pass and then apply it for robust prediction of the total energy in crystal lattice structures. Our approach is built upon the deep kernel learning (DKL) that originally introduces to leverage the expressiveness of deep neural networks as input with a probabilistic prediction of Gaussian processes (GPs) as output. Existing DKL methods have difficulties in the accuracy of predictive uncertainty, training stability, and scaling to large datasets, which lead to significant barriers in real-world applications. We propose to address these challenges by using an inducing point approximate GP in feature space combined with spectral normalization as a regularization. We finally demonstrate our robust performance on an artificial example and a real-world application from materials chemistry.

Bi, Sirui↗
DOE OSTI2022

EDAT

SAND2022-13281 O EDAT is a suite of tools for analyzing time-dependent electron densities produced by time-dependent density functional theory (TDDFT) simulations of ion-irradiated materials and surfaces. The code’s key capabilities include various routines to extract the number of electrons emitted from a surface, the energy spectrum of these emitted electrons, and the number of electrons captured by the ion. The code is written in Python and can interface with multiple TDDFT codes. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Kononov, Alina↗
DOE OSTI2020

Covariant density functional theory input for r-process simulations in actinides and superheavy nuclei: The ground state and fission properties

A systematic investigation of the ground-state and fission properties of even-even actinides and superheavy nuclei with Z = 90–120 from the two-proton up to two-neutron drip lines with proper assessment of systematic theoretical uncertainties has been performed for the first time in the framework of covariant density functional theory (CDFT). Furthermore, these results provide a necessary theoretical input for the r-process modeling in heavy nuclei and, in particular, for the study of fission cycling. Four state-of-the-art globally tested covariant energy density functionals (CEDFs), namely, DD-PC1, DD-ME2, NL3*, and PC-PK1, representing the major classes of the CDFT models are employed in the present paper. Ground-state deformations, binding energies, two-neutron separation energies, α-decay Q α values and half-lives, and the heights of fission barriers have been calculated for all these nuclei. Theoretical uncertainties in these physical observables and their evolution as a function of proton and neutron numbers have been quantified and their major sources have been identified. Spherical shell closures at Z = 120, N = 184, and N = 258 and the structure of the single-particle (especially, high-j) states in their vicinities as well as nuclear matter properties of employed CEDFs are two major factors contributing to theoretical uncertainties. However, different physical observables are affected in a different way by these two factors. For example, theoretical uncertainties in calculated ground-state deformations are affected mostly by the former factor, while theoretical uncertainties in fission barriers depend on both of these factors.

190 ≤ A ≤ 219↗
DOE OSTI2024

Comparing ab initio and quantum-kinetic approaches to electron transport in warm dense matter

Accurate knowledge of the electronic transport properties of warm dense matter is one of the main concerns of research in high-energy-density physics. Three modern approaches with vastly different levels of fidelity are reviewed and compared: the Kubo–Greenwood (KG) approach based on density-functional-theory molecular dynamics simulations (QMD), quantum kinetic theory based on average-atom models, and time-dependent density functional theory. Throughout, emphasis is placed on the connection between static properties of the electrons (e.g., density of states) and transport properties. Overall, it is found that whenever the conduction electrons can be modeled as being nearly free, fair to excellent agreement is found between QMD and kinetic theory approaches. Such a circumstance is required for modeling warm dense matter as a plasma of ions and free electrons, which is assumed in most kinetic theory approaches. The sensitivity of transport properties to the electronic structure is further highlighted by comparing different exchange–correlation approximations in QMD and KG calculations. It is found that the inclusion of exact exchange via thermal hybrid functionals can make a pronounced impact on electrical and thermal conduction in warm dense matter. We also investigate dynamic screening physics via kinetic theory and time-dependent density functional theory calculations of the mean free path of an electron in a hot dense plasma. In sum, we identify three axes along which to make progress in predicting electron transport in warm dense matter.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

3D Heisenberg universality in the van der Waals antiferromagnet NiPS 3

Van der Waals (vdW) magnetic materials are comprised of layers of atomically thin sheets, making them ideal platforms for studying magnetism at the two-dimensional (2D) limit. These materials are at the center of a host of novel types of experiments, however, there are notably few pathways to directly probe their magnetic structure. We confirm the magnetic order within a single crystal of NiPS 3 and show it can be accessed with resonant elastic X-ray diffraction along the edge of the vdW planes in a carefully grown crystal by detecting structurally forbidden resonant magnetic X-ray scattering. We find the magnetic order parameter has a critical exponent of β ~ 0.36, indicating that the magnetism of these vdW crystals is more adequately characterized by the three-dimensional (3D) Heisenberg universality class. We verify these findings with first-principles density functional theory, Monte-Carlo simulations, and density matrix renormalization group calculations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Unraveling Local Structure of Molten Salts via X-ray Scattering, Raman Spectroscopy, and Ab Initio Molecular Dynamics

In this work, we resolve a long-standing issue concerning the local structure of molten MgCl 2 by employing a multimodal approach, including X-ray scattering and Raman spectroscopy, along with the theoretical modeling of the experimental spectra based on ab initio molecular dynamics (AIMD) simulations utilizing several density functional theory (DFT) methods. We demonstrate the reliability of AIMD simulations in achieving excellent agreement between the experimental and simulated spectra for MgCl 2 and 50 mol % MgCl 2 + 50 mol % KCl, and ZnCl 2 , thus allowing structural insights not directly available from experiment alone. A thorough computational analysis using five DFT methods provides a convergent view that octahedrally coordinated magnesium in pure MgCl 2 upon melting preferentially coordinates with five chloride anions to form distorted square pyramidal polyhedra that are connected via corners and to a lesser degree via edges. This is contrasted with the results for ZnCl 2 , which does not change its tetrahedral coordination on melting. Although the five-coordinate MgCl 5 3– complex was not considered in the early literature, together with an increasing tendency to form a tetrahedrally coordinated complex with decreasing the MgCl 2 content in the mixture with alkali metal chloride systems, current work reconciles the results of most previous seemingly contradictory experimental studies.

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

Early Career: First-Principles Tools for Nonadiabatic Attosecond Dynamics in Materials

The overarching goal of this project was to develop computer tools for predicting how electrons move in molecules and solids at the attosecond (billionth-of-a-billionth of a second) time scale, during and after interaction with intense and/or high energy laser light. An associated goal was to also determine how X-ray spectroscopy could be used as a probe of these dynamics. The project resulted in multiple methodology developments that allow for these processes to be simulated from first-principles, most notably the use of small bulk-mimicking clusters to model solids and fixes for a deficiency in a commonly used method (time-dependent density functional theory). Additionally, the simulations showed that X-ray absorption peaks can be directly related to the electron density above the absorbing atom, and can thus be used as an intuitive probe of "where the electrons are" at a given time in the system. Collectively, these tools and results expected to be valuable for predicting and interpreting future attosecond experiments, especially for X-ray pump/probe studies at free-electron laser facilities.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water

Accurately simulating the properties of bulk water, despite the apparent simplicity of the molecule, is still a challenge. In order to fully understand and reproduce its complex phase diagram, it is necessary to perform simulations at the ab initio level, including quantum mechanical effects both for electrons and nuclei. This comes at a high computational cost, given that the structural and dynamical properties tend to require long timescales and large simulation cells. In this work, we evaluate the errors that density functional theory (DFT)-based simulations routinely incur into due time- and size-scale limitations. These errors are evaluated using neural-network-trained force fields that are accurate at the level of DFT methods. We compare different exchange and correlation potentials for properties of bulk water that require large timescales. Here we show that structural properties are less dependent on the system size and that dynamical properties such as the diffusion coefficient have a strong dependence on the simulation size and timescale. Our results facilitate comparisons of DFT-based simulation results with experiments and offer a path to discriminate between model and convergence errors in these simulations.

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

Quantum fluctuations in dense plasma simulations

Molecular dynamics (MD) simulations are a powerful tool for modeling warm and hot dense matter. Density functional theory (DFT) MD simulations are often preferred in dense plasmas in order to accurately model quantum electronic structure. However, DFT-MD simulations neglect interaction effects due to fluctuations in excited states. In this work, we present an MD approach that uses excited state method pseudoatoms to run dense plasma simulations with many different core-electron configurations at classical MD speeds. We also allow for transitions between different configurations in our simulations and find that these fluctuations are especially important for highly excited states. Our results suggest that finite configuration lifetimes that are comparable to the inverse ion plasma frequency need to be accounted for in order to accurately model ion distributions in dense plasma simulations. We also demonstrate that excited state fluctuations have a direct impact on ion plasma microfields, generate different plasma microfields for different excitation levels, and thereby induce absorption–emission line shape asymmetries even in steady-state plasmas.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modulation of thermal conductivity of iron-doped ß-Ga2O3 by helium-ion irradiation

This study examines the impact of helium-ion irradiation on the thermal conductivity of ß-Ga2O3. A laser-based spatial domain thermoreflectance technique is used to investigate thermal conductivity map for both un-irradiated and irradiated ß-Ga2O3, which are then validated against simulation results derived from density functional theory-based phonon transport simulations. Since helium bubble evolution was ob- served at the nanoscale using transmission electron microscopy, the simulation study was carried out on eight distinct helium-induced sites in ß-Ga2O3. Our findings indicate a reduction in thermal conductivity for the irradiated samples. Experimental results show a significant reduction in thermal conductivity in irradiated samples, with de- creases of approximately 25% along the [100] direction and 40% along [001] directions. Phonon transport simulations closely replicate these findings, particularly when helium occupying interstitial sites, predicting reductions of ˜53% along [100] and ˜50% along [001] directions. This work underscores the role of irradiation-induced microstructural changes in the heat transport properties of ß-Ga2O3 which is crucial for its application in sensor devices in extreme environments.

36 - MATERIALS SCIENCE↗
DOE OSTI2020

Subsurface Nitrogen Dissociation Kinetics in Lithium Metal from Metadynamics

The dissociation of molecular nitrogen in lithium is of interest for several promising technologies, such as the catalytic synthesis of ammonia in ambient or mild conditions. In this work we simulate nitrogen dissociation in the lithium BCC (110) surface at ambient and elevated temperatures using density functional theory (DFT) metadynamics simulations. The rate constants at temperatures of 300, 400, and 500 K are calculated by statistical analysis of the reaction time distributions from the accelerated simulations. This approach finds and estimates rate constants for transition pathways out of the initial state; the required input is the stable initial state and a reasonable choice of collective variable. A single collective variable is used in this case: the N–N distance. The results are robust to changes in metadynamics parameters, and the reaction time distributions follow the expected exponential distribution. We show that the metadynamics-derived rate constants are in agreement with results from the conventional harmonic approximation approach using a climbing image nudged elastic band (NEB) transition state search. The reaction barriers from metadynamics and the NEB/harmonic approximation agree to within 0.02–0.04 eV at all temperatures studied. This paper demonstrates that the harmonic approximation provides an accurate description of the rate constants for nitrogen dissociation in lithium metal, even at temperatures near or above the melting point of lithium, lending credence to previous and future theoretical studies using this approximation. Moreover, this work demonstrates a step toward the automated exploration and discovery of reaction mechanisms and associated rate constants for elementary surface-catalyzed reactions using DFT-based metadynamics.

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

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Changes in polarization dictate necessary approximations for modeling electronic deexcitation intensity: Application to x-ray emission

Accurate simulation of electronic excitations and deexcitations are critical for complementing complex spectroscopic experiments and can provide validation to theoretical approaches. Here, using a generalized framework, we contrast the accuracy and validity of orbital-constrained and linear-response approaches that build upon Kohn-Sham density functional theory (DFT) to simulate emission spectra of electronic origin and propose an efficient approximation, named many-body x-ray emission spectroscopy or MBXES, for simulating such processes. We show analytically as well as with computed examples that for electronic (de)excitation leading to an appreciable change in polarization (i.e., density rearrangement), the adiabatic approximation in a response-based formalism will be inadequate for the calculation of oscillator strength. Thus, such a change (e.g., in the net electrostatic dipole moment of a finite system) can be used as a metric for evaluating the applicability of the adiabatic response-based approach and can be particularly valuable in x-ray emission spectroscopy. On the other hand, MBXES, the flexible method introduced in this paper, can compute oscillator strengths accurately at a much lower computational expense on the basis of two DFT-based self-consistent field calculations. Using illustrative examples of emission spectra, the efficacy of the MBXES method is demonstrated by comparison with its parent theory, orbital-optimized DFT, and with experiments.

74 ATOMIC AND MOLECULAR PHYSICS↗