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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Ambient and High Pressure CuNiSb 2 : Metal-Ordered and Metal-Disordered NiAs-Type Derivative Pnictides

The mineral Zlatogorite, CuNiSb 2 , was synthesized in the laboratory for the first time by annealing elements at ambient pressure (CuNiSb 2 -AP). Rietveld refinement of synchrotron powder X-ray diffraction data indicates that CuNiSb 2 -AP crystallizes in the NiAs-derived structure ( P 3 m 1, #164) with Cu and Ni ordering. The structure consists of alternate NiSb 6 and CuSb 6 octahedral layers via face-sharing. The formation of such structure instead of metal disordered NiAs-type structure ( P 6 3 / mm c, #194) is validated by the lower energy of the ordered phase by first-principle calculations. Interatomic crystal orbital Hamilton population, electron localization function, and charge density analysis reveal strong Ni-Sb, Cu-Sb, and Cu-Ni bonding and long weak Sb-Sb interactions in CuNiSb 2 -AP. The magnetic measurement indicates that CuNiSb 2 -AP is Pauli paramagnetic. First-principle calculations and experimental electrical resistivity measurements reveal that CuNiSb 2 -AP is a metal. The low Seebeck coefficient and large thermal conductivity suggest that CuNiSb 2 is not a potential thermoelectric material. Single crystals were grown by chemical vapor transport. The high pressure sample (CuNiSb 2 -8 GPa) was prepared by pressing CuNiSb 2 -AP at 700 °C and 8 GPa. However, the structures of single crystal and CuNiSb 2 -8 GPa are best fit with a disordered metal structure in the P 3 m 1 space group, corroborated by transmission electron microscopy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2015

Viking GCMS Data Restoral and Perceiving Temperature on Other Worlds: Astrobiology Projects at NASA Ames

The primary task for the summer was to procure the GCMS data from the National Space Science Data Coordinated Archive (NSSDCA) and to assess the current state of the data set for possible reanalysis opportunities. After procurement of the Viking GCMS data set and analysis of its current state, the internship focus shifted to preparing a plan for restoral and archiving of the GCMS data set. A proposal was prepared and submitted to NASA Headquarters to restore and make available the 8000 mass chromatographs that are the basic data generated by the Viking GCMS instrument. The relevance of this restoral and the methodology we propose for restoral is presented. The secondary task for the summer is to develop a thermal model for the perceived temperature of a human standing on Mars, Titan, or Europa. Traditionally, an equation called "Fanger's comfort equation" is used to measure the perceived temperature by a human in a given reference environment. However, there are limitations to this model when applied to other planets. Therefore, the approach for this project has been to derive energy balance equations from first principles and then develop a methodology for correlating "comfort" to energy balance. Using the -20 C walk-in freezer in the Space Sciences building at NASA Ames, energy loss of a human subject is measured. Energy loss for a human being on Mars, Titan and Europa are calculated from first principles. These calculations are compared to the freezer measurements, e.g. for 1 minute on Titan, a human loses as much energy as x minutes in a -20 C freezer. This gives a numerical comparison between the environments. These energy calculations are used to consider the physiological comfort of a human based on the calculated energy losses.

FangeraEuro(TM)s comfort equation↗
DOE OSTI2022

Influence of Annealing and Composition on the Crystal Structure of Mixed-Halide, Ruddlesden–Popper Perovskites

Mixed-halide two-dimensional (2D) hybrid organic–inorganic perovskites offer an important opportunity to control the band gap for applications in optoelectronic devices. In this study, we focus on phenethylammonium lead halide [(PEA) 2 Pb(I 1– x Br x ) 4 ] films, the pure iodide form of which is one of the most widely studied optically active 2D perovskite systems. Resonant infrared, matrix-assisted pulsed laser evaporation is used to grow films to explore the effects of post-growth annealing and mixed-halide composition. The composition, crystal structure, and optical properties are studied for as-grown and annealed films of (PEA) 2 Pb(I 1– x Br x ) 4 for x = 0, 0.25, 0.5, 0.75, and 1. First-principles calculations are used in conjunction with the experimental data to explain the mixed-halide behavior that does not trend monotonically with the bromide content. Important results of this work are as follows: (i) X-ray diffraction reveals evidence for halide phase separation around x = 0.25, consistent with first-principles calculations, whereas no phase separation is observed for x = 0.5 and above and (ii) a unique photoluminescence (PL) peak splitting is observed for x = 0.75, a composition for which no compositional phase separation is observed. The PL splitting is tentatively explained by the coexistence of two distinct types of halide short-range ordering at x = 0.75. Overall, this study demonstrates that mixed-halide n = 1 Ruddlesden–Popper perovskites are not simple random alloys but that instead, they display distinct sites and ordering preferences of the different halide anions. These preferences are critical to understand and rationally tune the properties of the materials.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Machine Learning an Ab-Initio Based Bond-Order Potential for Bismuthene

Bismuthene is a heavy 2D material whose strong spin–orbit coupling and recently observed single-element ferroelectricity have intensified interest in its structural, vibrational, and transport properties. Accurate modeling of these behaviors requires a short-range interatomic potential that can reproduce the underlying bonding physics at a fraction of the computational cost of first-principles methods. However, such a potential is currently unavailable. Here, in this work, we construct a Tersoff bond-order potential for β-bismuthene using a reinforcement-learning framework that integrates a continuous Monte Carlo Tree Search with a simplex-based local optimizer. The optimized parameter sets reproduce first-principles lattice constants, cohesive energy, the equation of state, elastic constants, and phonon dispersion. We validate the models by performing thermal-conductivity calculations and uniaxial fracture simulations our findings confirm the reliability of the resulting models across multiple thermomechanical regimes. Comparison of the three best solutions reveals how differences in pairwise interactions, angular terms, and bond-order behavior govern phonon features and mechanical responses. We demonstrate an interpretable and computationally efficient potential for bismuthene and demonstrate a general reinforcement-learning strategy for developing bond-order models in emerging 2D materials.

deformation↗
DOE OSTI2022

The structure of sc16 GaP obtained at 17.5 GPa and 1400 K

We report using a laser-heated diamond anvil cell, a powdered sample of gallium phosphide was compressed to 17.5 GPa and heated up to 1400 K. The material obtained was characterized at room temperature using synchrotron x-ray diffraction. Experimental results were compared with first-principles calculations. The polymorph observed assumes the simple cubic structure (sc16) initially reported for GaAs at high pressures. Microdiffraction mapping showed variable grain sizes of the synthesized phase, with the largest grains located in the middle of the heated spot. Structural refinements were performed on selected grains. The structure predicted based on first-principles calculations is in close agreement with the experiments. The two Ga–P bonds show similar lengths in sc16-GaP; however, the bond angles differ, resulting in a distorted tetrahedral coordination geometry.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Magnetic Properties of Cables with Meandering Wires through a Multipole-based Cable Braid Electromagnetic Penetration Model

In this paper, we employ our first principles multipole-based cable braid electromagnetic penetration model to evaluate the transfer inductance of cables exhibiting meandering wires. We concentrate on a cable structure with two wires, and consider the dependence on the transfer inductance as a function of braid angle and amplitude of the meandering. We compare the results from the first principles model to analytical estimations, confirming the accuracy and correctness of our model. In turn, this makes the multipole-based model readily available for the modeling of realistic cable geometries by accounting for the full dependence on the actual cable geometry.

36 MATERIALS SCIENCE↗
NASA NTRS2019

Predicting Quadcopter Drone Noise Using the Lattice Boltzmann Method

The market for new vertical takeoff and landing vehicles, including autonomous urban air taxis and drones for applications such as package delivery, imaging, and surveillance, is growing rapidly. However, aerodynamic noise continues to be the biggest roadblock to community acceptance and adoption. To predict the aerodynamic noise generated by an isolated quadcopter drone, derived from from first principles, we used the Lattice Boltzmann flow solver within NASA’s Launch Ascent and Vehicle Aerodynamics (LAVA) solver framework. The solver’s computational efficiency, and the complete absence of labor-intensive manual volume mesh generation in the workflow, are key to making routine aeroacoustic analysis of urban air taxis and drones from first principles possible.

Cadieux, Francois↗
DOE OSTI2021

A Polar Magnetic and Insulating Double Corundum Oxide: Mn2MnSbO6 with Ordered Mn(II) and Mn(III) Ions

A new magnetic insulator Mn2MnSbO6 with a polar crystal structure and an ordered Mn2+ and Mn3+ arrangement was synthesized under a high pressure of 7.5 GPa and 1300 degrees C. The crystal structure of Mn2MnSbO6, investigated by synchrotron powder X-ray diffraction, was found to be isomorphous with that of Ni3TeO6-type, space group R3. The non-centrosymmetric structure was confirmed by the second-harmonic generation measurements. The X-ray absorption near-edge spectroscopy measurement confirmed the nominal oxidation states of Mn22+Mn3+SbO6. Magnetic measurements indicate that Mn2MnSbO6 orders antiferromagnetically below 44 K and undergoes a field-induced spin-flop transition at 5 K. First-principles calculations indicate an antiferromagnetic ground state with up/up/up/down/down/down (uuuddd) spin configuration of the six crystallographically unique Mn ions in the c-axis doubled magnetic structure. The density functional theory calculations also substantiate the experimentally observed charge ordering of the Mn2+/Mn3+ ions and the insulating behavior due to a bandgap of 0.52 eV. To the best of our knowledge, this is the first double corundum oxide containing Jahn-Teller active Mn3+ ions.

Feng, Hai L.↗
DOE OSTI2024

Te Vacancy‐Driven Anomalous Transport in ZrTe 5 and HfTe 5

Abstract The strongly sample‐dependent anomalous transport properties observed in the layered Dirac materials ZrTe 5 and HfTe 5 are known to strongly correlate with the presence of Te vacancies. One phenomenon, a negative longitudinal magnetoresistance (NLMR), is widely speculated to be a signature of broken chiral symmetry. However, the role of electronic structure in the sample dependence of the transport properties of these materials is poorly understood. This prompts the question as to whether the NLMR is a genuine signature of the chiral anomaly in ZrTe 5 and HfTe 5 . In this work, the effect of Te vacancies on the electronic structure of ZrTe 5 and HfTe 5 is investigated via first‐principles calculations. Te vacancies serve two purposes: modification of the cell volume via effective compressive strain and production of local changes to the electronic structure. The reorganization of the electronic structure near the Fermi energy indicates that Te vacancies can rationalize conflicting reports in spectroscopic and transport measurements that have remained elusive in prior first‐principles studies. These results show that Te vacancies contribute, in part, to the anomalous transport properties of ZrTe 5 and HfTe 5 but, critically, do not eliminate the possibility of a genuine manifestation of the chiral anomaly in these materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Efficient use of quantum computers for collider physics

Most observables at particle colliders involve physics at a wide variety of distance scales. Due to asymptotic freedom of the strong interaction, the physics at short distances can be calculated reliably using perturbative techniques, while long distance physics is non-perturbative in nature. Factorization theorems separate the contributions from different scales, allowing to identify the pieces that can be determined perturbatively from those that require non-perturbative information, and if the non-perturbative pieces can be reliably determined, one can use experimental measurements to extract the short distance effects, sensitive to possible new physics. Without the ability to compute the non-perturbative ingredients from first principles one typically identifies observables for which the non-perturbative information is universal in the sense that it can be extracted from some experimental observables and then used to predict other observables. In this paper we argue that the future ability to use quantum computers to calculate non-perturbative matrix elements from first principles will allow to make predictions for observables with non-universal non-perturbative long-distance physics.

Algorithms and Theoretical Developments↗
DOE OSTI2021

Comparative Studies of the Structural and Transport Properties of Molten Salt FLiNaK Using the Machine-Learned Neural Network and Reparametrized Classical Forcefields

Despite surging interest in molten salt reactors and thermal storage systems, knowledge of the physicochemical properties of molten salts are still inadequate due to demanding experiments that require high temperature, impurity control, and corrosion mitigation. Therefore, the ability to predict these properties for molten salts from first-principles computations is urgently needed. Herein, we developed and compared a machine-learned neural network force field (NNFF) and a reparametrized rigid ion model (RIM) for a prototypical molten salt LiF–NaF–KF (FLiNaK). We found that NNFF was able to reproduce both the structural and transport properties of the molten salt with first-principles accuracy and classical-MD computational efficiency. Furthermore, the correlation between the local atomic structures and the dynamics was identified by comparing with RIMs, suggesting the significance of polarization of anions implicitly embedded in the NNFF. Furthermore, this work demonstrated a computational framework that can facilitate the screening of molten salts with different chemical compositions, impurities, and additives, and at different thermodynamic conditions suitable for the next-generation nuclear reactors and thermal energy storage facilities.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Multiscale simulations of electron and ion dynamics in self-irradiated silicon

The interaction of energetic ions with the electronic and ionic system of target materials is an interesting but challenging multiscale problem, and understanding of the early stages after impact of heavy, initially charged ions is particularly poor. At the same time, energy deposition during these early stages determines later formation of damage cascades. In this work, we address the multiscale character by combining real-time time-dependent density functional theory for electron dynamics with molecular dynamics simulations of damage cascades. Our first-principles simulations prove that core electrons affect electronic stopping and have an unexpected influence on the charge state of the projectile. We show that this effect is absent for light projectiles, but dominates the stopping physics for heavy projectiles. By parametrizing an inelastic energy loss friction term in the molecular dynamics simulations using our first-principles results, we also show a qualitative influence of electronic stopping physics on radiation-damage cascades.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Competing energetic states in γ – Fe 2 W O 6 with strong spin-charge-lattice coupling

We report magnetic and electronic properties of γ – Fe 2 W O 6 via neutron powder-diffraction measurements and first-principles density-function theory calculations. We reveal a magnetic ground state with two phases coexisting in which the minor phase is the same as an earlier report, despite the fact that both materials studied have the same space group but with slightly different atomic positions. Interestingly, both magnetic phases are well captured by first-principles calculations. Furthermore, we show that these two magnetic phases are correlated with electronic properties, with one being insulating and the other being metallic. These features suggest that γ – Fe 2 W O 6 exhibits competing energetic states in which spin, charge, and lattice degrees of freedom are strongly coupled to each other.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Engineering Anomalously Large Electron Transport in Topological Semimetals

Abstract Anomalous transport of topological semimetals has generated significant interest for applications in optoelectronics, nanoscale devices, and interconnects. Understanding the origin of novel transport is crucial to engineering the desired material properties, yet their orders of magnitude higher transport than single‐particle mobilities remain unexplained. This work demonstrates the dramatic mobility enhancements result from phonons primarily returning momentum to electrons due to phonon‐electron dominating over phonon–phonon scattering. Proving this idea, proposed by Peierls in 1932, requires tuning electron and phonon dispersions without changing symmetry, topology, or disorder. This is achieved by combining de Haas ‐ van Alphen (dHvA), electron transport, Raman scattering, and first‐principles calculations in the topological semimetals MX 2 (M = Nb, Ta and X = Ge, Si). Replacing Ge with Si brings the transport mobilities from an order magnitude larger than single particle ones to nearly balanced. This occurs without changing the crystal structure or topology and with small differences in disorder or Fermi surface. Simultaneously, Raman scattering and first‐principles calculations establish phonon–electron dominated scattering only in the MGe 2 compounds. Thus, this study proves that phonon‐drag is crucial to the transport properties of topological semimetals and provides insight to engineer these materials further.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Structural Anomalies and Electronic Properties of an Ionic Liquid under Nanoscale Confinement

Ionic liquids (ILs) promise far greater electrochemical performance compared to aqueous systems, yet key physicochemical properties governing their assembly at interfaces within commonly used graphitic nanopores remain poorly understood. Here, we combine synchrotron X-ray scattering with first-principles molecular dynamics simulations to unravel key structural characteristics of 1-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([TFSI] – ) ionic liquids confined in carbon slit pores. X-ray scattering reveals selective pore filling due to size exclusion, while filled pores exhibit disruption in the IL intermolecular structure, the extent of which increases for narrower slit pores. First-principles simulations corroborate this finding and quantitatively describe how perturbations in the local IL structure, particularly the hydrogen-bond network, depend strongly on the degree of confinement. Despite significant deviations in structure under confinement, electrochemical stability remains intact, which is important for energy storage based on nanoporous carbon electrodes (e.g., supercapacitors).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior

The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the integration of empirical measurements, but enable real-time simulations for operational use in fire response. Machine learning techniques have demonstrated the ability to bridge these objectives by learning first-principles physics while achieving computational speedups. While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management. Here, in this work, we evaluate the ability of deep learning approaches in accurately modeling the time-resolved dynamics of wildfires. We use an autoregressive process in which a convolutional recurrent deep learning model makes predictions that propagate a wildfire over 15 min increments. We apply the model to four simulated datasets of increasing complexity, containing both field fires with homogeneous fuel distribution as well as real-world topologies sampled from the California region of the United States. We show that even after 100 autoregressive predictions representing more than 24 h of simulated fire spread, the resulting models generate stable and realistic propagation dynamics, achieving a Jaccard score between 0.89 and 0.94 when predicting the resulting fire scar. The inference time of the deep learning models are examined and compared, and directions for future work are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Cluster expansion by transfer learning for phase stability predictions

Recent progress towards universal machine-learned interatomic potentials holds considerable promise for materials discovery. Yet the accuracy of these potentials for predicting phase stability may still be limited. In contrast, cluster expansions provide accurate phase stability predictions but are computationally demanding to parameterize from first principles, especially for structures of low dimension or with a large number of components, such as interfaces or multimetal catalysts. We overcome this trade-off via transfer learning. Using Bayesian inference, we incorporate prior statistical knowledge from machine-learned and physics-based potentials, enabling us to sample the most informative configurations and to efficiently fit first-principles cluster expansions. Furthermore, this algorithm is tested on Pt:Ni, showing robust convergence of the mixing energies as a function of sample size with reduced statistical fluctuations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Downfolding from ab initio to interacting model Hamiltonians: comprehensive analysis and benchmarking of the DFT+cRPA approach

Abstract Model Hamiltonians are regularly derived from first principles to describe correlated matter. However, the standard methods for this contain a number of largely unexplored approximations. For a strongly correlated impurity model system, here we carefully compare a standard downfolding technique with the best possible ground-truth estimates for charge-neutral excited-state energies and wave functions using state-of-the-art first-principles many-body wave function approaches. To this end, we use the vanadocene molecule and analyze all downfolding aspects, including the Hamiltonian form, target basis, double-counting correction, and Coulomb interaction screening models. We find that the choice of target-space basis functions emerges as a key factor for the quality of the downfolded results, while orbital-dependent double-counting corrections diminish the quality. Background screening of the Coulomb interaction matrix elements primarily affects crystal-field excitations. Our benchmark uncovers the relative importance of each downfolding step and offers insights into the potential accuracy of minimal downfolded model Hamiltonians.

Chemistry↗