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

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

Activation of Semicore Electrons in Alkali Metals and Their Role in the B1–B2 Phase Transition under Pressure

Alkali fluorides are often thought of as archetypical ionic compounds whose structures can be understood in terms of the packing of rigid spheres. At ambient pressure, they assume the rocksalt (B1) structure, while under only a few GPa of pressure, the cesium chloride (B2) structure, with higher coordination numbers, is assumed for KF, RbF, and CsF. NaF requires almost 10 times more pressure to undergo this same phase transition, which has not been observed for LiF. Herein, we provide a detailed analysis, based upon quantum chemical calculations, explaining this behavior. We show that for the heavier alkali metals, the semicore p orbitals engage in metal-metal bonding in the B2 phase, facilitating the pressure-induced B1 → B2 structural transition. Furthermore, these findings suggest that the semicore orbitals of heavy alkali metals can be activated without the need of strong oxidants at very mild levels of compression, resulting in the formation of chemical bonds, challenging both traditional and modern core-valence distinctions. In addition, we argue that Cs 5p-5p bonding in CsCl occurs already at ambient pressure, stabilizing the B2 phase, and suggest experiments that may be able to detect signatures of such bonding.

Anions↗
DOE OSTI2023

Real-time latent heat emission during dynamic-compression freezing of water

Abstract Dynamic compression studies have been used to study the nucleation kinetics of water to ice VII for decades. Diagnostics such as photon Doppler velocimetry, transmission loss, and imaging have been used to measure pressure/density, and phase fraction, while temperature has remained the difficult thermodynamic property to quantify. In this work, we measured pressure/density and implemented a diagnostic to measure the temperature. In doing so the temperature shows quasi-isentropically compressed liquid water forms ice at pressures below the previously defined metastable limit, and the liquid phase is not hypercoooled as previously thought above that limit. Instead, the latent heat raises the temperature to the liquid-ice-VII melt line, where it remains with increasing pressure. We propose a hypothesis to corroborate these results with previous work on dynamic compression freezing. These results provide constraints for nucleation models, and suggest this technique be used to investigate phase transitions in other materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Towards critical and supercritical electromagnetic fields

Abstract The availability of ever stronger, laser-generated electromagnetic fields underpins continuing progress in the study and application of nonlinear phenomena in basic physical systems, ranging from molecules and atoms to relativistic plasmas and quantum electrodynamics. This raises the question: how far will we be able to go with future lasers? One exciting prospect is the attainment of field strengths approaching the Schwinger critical field ${E}_{\mathrm{cr}}$ in the laboratory frame, such that the field invariant ${E}^2-{c}^2{B}^2>{E}_{\mathrm{cr}}^2$ is reached. The feasibility of doing so has been questioned, on the basis that cascade generation of dense electron–positron plasma would inevitably lead to absorption or screening of the incident light. Here we discuss the potential for future lasers to overcome such obstacles, by combining the concept of multiple colliding laser pulses with that of frequency upshifting via a tailored laser–plasma interaction. This compresses the electromagnetic field energy into a region of nanometre size and attosecond duration, which increases the field magnitude at fixed power but also suppresses pair cascades. Our results indicate that laser facilities with peak power of tens of PW could be capable of reaching ${E}_{\mathrm{cr}}$ . Such a scenario opens up prospects for the experimental investigation of phenomena previously considered to occur only in the most extreme environments in the universe.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Mapping the intrinsic photocurrent streamlines through micromagnetic heterostructure devices

Photocurrent in quantum materials is often collected at global contacts far away from the initial photoexcitation. This collection process is highly nonlocal. It involves an intricate spatial pattern of photocurrent flow (streamlines) away from its primary photoexcitation that depends sensitively on the configuration of current collecting contacts as well as the spatial nonuniformity and tensor structure of conductivity. Direct imaging to track photocurrent streamlines is challenging. Here, we demonstrate a microscopy method to image photocurrent streamlines through ultrathin heterostructure devices comprising platinum on yttrium iron garnet (YIG). We accomplish this by combining scanning photovoltage microscopy with a uniform rotating magnetic field. Here, local photocurrent is generated through a photo-Nernst type effect with its direction controlled by the external magnetic field. This enables the mapping of photocurrent streamlines in a variety of geometries that include conventional Hall bar-type devices, but also unconventional wing-shaped devices called electrofoils. In these, we find that photocurrent streamlines display contortion, compression, and expansion behavior depending on the shape and angle of attack of the electrofoil devices, much in the same way as tracers in a wind tunnel map the flow of air around an aerodynamic airfoil. This affords a powerful tool to visualize and characterize charge flow in optoelectronic devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Improving equations of state calibrations in the toroidal DAC—The case study of molybdenum

We report an updated isothermal equation of state (EoS) of molybdenum (Mo) obtained by compression in beveled and toroidal diamond-anvil cells (DACs). For an improved compression environment, we developed a copper (Cu) pressure-transmitting medium (PTM) for the toroidal diamond-anvil cell samples, as it is a soft metal compared to Mo with a well calibrated EoS. A Ne PTM was used for the conventional beveled DAC samples. The unit-cell volumes of Mo were measured to 336(1) GPa in the Cu PTM and 231.2(6) GPa in the Ne PTM at room temperature. We additionally calculated elastic stiffness and compliance constants and evaluated the uniaxial stress of Mo and Cu with pressure. A new EoS for Mo is presented from data collected in all sample environments and compared to our theoretical predictions as well as previous compression studies of Mo. The (200) lattice plane of Mo produced the lowest volumes across the pressure range of this study for all compression environments, suggesting that it is less affected by nonhydrostatic stresses in the DAC compared to the other observed diffraction planes. The presented Mo EoS is compatible with extrapolations of EoS fits of Mo in helium (He) within ~1% at 330 GPa. Results from this work demonstrate that compressing a sample in a softer metal in the toroidal DAC can improve the compression environment and result in measured sample volumes comparable to those collected in noble-gas media at multi-megabar conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Recent Advances toward Efficient Calculation of Higher Nuclear Derivatives in Quantum Chemistry

In this article, we provide an overview of state-of-the-art techniques that are being developed for efficient calculation of second and higher nuclear derivatives of quantum mechanical (QM) energy. Calculations of nuclear Hessians and anharmonic terms incur high costs and memory and scale poorly with system size. Three emerging classes of methods—machine learning (ML), automatic differentiation (AD), and matrix completion (MC)—have demonstrated promise in overcoming these challenges. We illustrate studies that employ unsupervised ML methods to reduce the need for multiple Hessian calculations in dynamics simulations and those that utilize supervised ML to construct approximate potential energy surfaces and estimate Hessians and anharmonic terms at reduced cost. By extension, if electronic structure operations could be written in a manner similar to functions underlying ML methods, rapid differentiation or AD routines can be employed to inexpensively calculate higher arbitrary-order derivatives. While ML approaches are typically black-box, we describe methods such as compressed sensing (CS) and MC, which explicitly leverage problem-specific mathematical properties of higher derivatives such as sparsity and low-rank, to complete higher derivative information using only a small, incomplete sample. The three classes of methods facilitate reliable predictions of observables ranging from infrared spectra to thermal conductivity and constitute a promising way forward in accurately capturing otherwise intractable higher-order responses of QM energy to nuclear perturbations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Pressure-driven symmetry transitions in dense H 2 O ice

X-ray diffraction and Raman spectroscopy of H 2 O (ice) structures are measured under static compression in combination with grain normalizing heat treatment via direct laser heating. Here, we report the transition from cubic ice-VII to a structure of tetragonal symmetry, ice-VII t at 5.1 ± 0.5 GPa. This is succeeded by the H-bond symmetrization transition occurring at a pressure of 30.9 ± 3 GPa. Both experimental observations are supported by simulated Raman spectra from density-functional theory quantum calculations. The transition to H-bond symmetrization is evidenced by the reversible emergence of its characteristic Raman mode and a 2.5-fold increase in bulk modulus, implying a significant increase in bonding strength.

58 GEOSCIENCES↗
DOE OSTI2021

MechElastic: A Python library for analysis of mechanical and elastic properties of bulk and 2D materials

We report the MechElastic Python package evaluates the mechanical and elastic properties of bulk and 2D materials using the elastic coefficient matrix ( C ij ) obtained from any ab-initio density-functional theory (DFT) code. The current version of this package reads the output of VASP, ABINIT, and Quantum Espresso codes (but it can be easily generalized to any other DFT code) and performs the appropriate post-processing of elastic constants as per the requirement of the user. This program can also detect the input structure's crystal symmetry and test the mechanical stability of all crystal classes using the Born-Huang criteria. Various useful material-specific properties such as elastic moduli, longitudinal and transverse elastic wave velocities, Debye temperature, elastic anisotropy, 2D layer modulus, hardness, Pugh's ratio, Cauchy's pressure, Kleinman's parameter, and Lame's coefficients, can be estimated using this program. Another existing feature of this program is to employ the ELATE package (2016) [29] and plot the spatial variation of several elastic properties such as Poisson's ratio, linear compressibility, shear modulus, and Young's modulus in three dimensions. Further, the MechElastic package can plot the equation of state (EOS) curves for energy and pressure for a variety of EOS models such as Murnaghan, Birch, Birch-Murnaghan, and Vinet, by reading the inputted energy/pressure versus volume data obtained via numerical calculations or experiments. This package is particularly useful for the high-throughput analysis of elastic and mechanical properties of materials.

2D materials↗
DOE OSTI2023

HQ-Sim: High-performance State Vector Simulation of Quantum Circuits on Heterogeneous HPC Systems

Quantum circuit simulations are applied in more and more circum- stances as the quantum computing community becomes broader. It helps researchers to evaluate quantum algorithms and relieve the burden of limited quantum computing resources. However, most of the state-of-the-art quantum simulators utilize either CPU or GPU to store and calculate the state vector, which results in resource starvation. Moreover, the maximum number of qubits supported by the simulator is bounded by the memory, since the memory utilization increases exponentially with the number of qubits. In this study, we leverage Heterogeneous computing to utilize both CPU and GPU to store and update state vectors. We also integrate lossy data compression to reduce memory requirements. Specifically, we develop a heterogeneous framework that has a dynamic scheduler to fully utilize the computing resources. We apply lossy compression to chunked state vector to make the maximum number of qubits higher than the regular simulators, the compression also benefits the data movement between CPU and GPU.

Zhang, Boyuan↗
DOE OSTI2023

Dimensionality Reduction with Variational Encoders Based on Subsystem Purification

Efficient methods for encoding and compression are likely to pave the way toward the problem of efficient trainability on higher-dimensional Hilbert spaces, overcoming issues of barren plateaus. Here, we propose an alternative approach to variational autoencoders to reduce the dimensionality of states represented in higher dimensional Hilbert spaces. To this end, we build a variational algorithm-based autoencoder circuit that takes as input a dataset and optimizes the parameters of a Parameterized Quantum Circuit (PQC) ansatz to produce an output state that can be represented as a tensor product of two subsystems by minimizing $Tr(ρ^2)$. The output of this circuit is passed through a series of controlled swap gates and measurements to output a state with half the number of qubits while retaining the features of the starting state in the same spirit as any dimension-reduction technique used in classical algorithms. The output obtained is used for supervised learning to guarantee the working of the encoding procedure thus developed. We make use of the Bars and Stripes (BAS) dataset for an 8 × 8 grid to create efficient encoding states and report a classification accuracy of 95% on the same. Thus, the demonstrated example provides proof for the working of the method in reducing states represented in large Hilbert spaces while maintaining the features required for any further machine learning algorithm that follows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Tunable spin and valley excitations of correlated insulators in Γ-valley moiré bands

Moiré superlattices formed from transition metal dichalcogenides (TMDs) have been shown to support a variety of quantum electronic phases that are highly tunable using applied electromagnetic fields. While the valley character of the low-energy states dramatically affects optoelectronic properties in the constituent TMDs, this degree of freedom has yet to be fully explored in moiré systems. Here, we establish twisted double bilayer WSe 2 as an experimental platform to study electronic correlations within Γ-valley moiré bands. Through a combination of local and global electronic compressibility measurements, we identify charge-ordered phases at multiple integer and fractional moiré band fillings ν. By measuring the magnetic field dependence of their energy gaps and the chemical potential upon doping, we reveal spin-polarized ground states with novel spin polaron quasiparticle excitations. In addition, an applied displacement field allows us to realize a new mechanism of metal-insulator transition at ν=–1 driven by tuning between Γ- and K-valley moiré bands. Together, our results demonstrate control over both the spin and valley character of the correlated ground and excited states in this system.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Efficient Data Query for Gaussian Process Compressed Data through Value Range Estimation [Slides]

When the resolution of the data increases, data reduction methods are applied to simulation output, including Gaussian process, neural representation and compression algorithms. Lots of data analysis/visualization techniques requires data query, but data query from reduced representation is still challenging. This report will provide examples and provide possible answers to why data query from reduced representation is still challenging.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Linear Velocity Problems Part 2: Thermodynamic Considerations

A compressible polytropic gas, in one dimensional planar, cylindrical, or spherical coordinate systems, is examined through four thermodynamic properties: mass density, pressure, specific internal energy (SIE), and entropy, under the assumptions that the fluid velocity is linearly proportional to the radial coordinate and that the Euler gas dynamics equations assert a self similar solution class. The behavior of the thermodynamics is almost entirely determined by an arbitrary function that results from the derivation of the density distribution. Through physical and other arguments, the arbitrary function can be plotted spatially with time profiles, providing a deeper understanding of the system and how the arbitrary function affects the behavior of the compressible gas.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Shock-compressed silicon: Hugoniot and sound speed up to 2100 GPa

In this work, the high-pressure equation of state and isentropic sound speed of fluid silicon were studied using laser-driven shock waves. Principal Hugoniot measurements to 2100 GPa were performed using impedance matching to an α-quartz reference. Sound speed was determined by time correlating imposed shock-velocity perturbations in both the sample (Si) and reference material (α-quartz). A change in shock velocity versus particle velocity (u s –u p ) slope on the fluid silicon principal Hugoniot was detected at 200 GPa. Density functional theory-based quantum molecular dynamics simulations suggest that an increase in ionic coordination and 50% increase in average ionization is coincident with the experimentally observed change in slope.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Superstructure magnetic anisotropy in Fe 3 O 4 nanoparticle chains

Magnetic anisotropy is essential for many applications of ferromagnetic/ferrimagnetic materials, including permanent magnets and magnetic recording media. Attempts have been made recently to build up 3-D nanoparticle and quantum dot assemblies, however, it is not understood yet if a nanoparticle assembly can possess high magnetic anisotropy with low anisotropic materials. In this article, we report our discovery of high magnetic anisotropy resulted from Fe 3 O 4 nanoparticle chains. We started with closely-packed nanoparticle assemblies of spherical Fe 3 O 4 nanoparticles that exhibit low magnetocrystalline anisotropy and shape anisotropy, and corresponding negligible coercivity. When the nanoparticle assemblies are compressed under pressure, they form bundles or arrays that consist of Fe 3 O 4 chains with a length scale of several hundred nanometers. Magnetic measurements show that these Fe 3 O 4 chain arrays possess a high uniaxial magnetic anisotropy (K eff ~ 2.9×10 5 J/m 3 ) and significant magnetic coercivity. Our simulations reveal that interparticle magnetic dipolar interactions contribute to this type of superstructure magnetic anisotropy. This study demonstrates the feasibility and approaches to create “patterned” high magnetic anisotropy in nanoparticle superstructures/assemblies.

Mohapatra, Jeotikanta (ORCID:0000000151968626)↗
DOE OSTI2024

A new re-redistribution scheme for weighted state redistribution with adaptive mesh refinement

State redistribution (SRD) is a recently developed technique for stabilizing cut cells that result from finite-volume embedded boundary methods. SRD has been successfully applied to a variety of compressible and incompressible flow problems. When used in conjunction with adaptive mesh refinement (AMR), additional steps are needed to preserve the accuracy and conservation properties of the solution if the embedded boundary is not restricted to a single level of the mesh hierarchy. In this work, we extend the weighted state redistribution algorithm to cases where cut cells live at or near a coarse-fine interface within the domain. Here, we present numerical results that demonstrate that the algorithm is conservative when the coarse-fine interface intersects the embedded boundary. Additionally we compare the numerical solution of the Sod shock tube problem in an inclined cylinder with the analytic solution, and we compare the simulation of a shock hitting a cylindrical obstacle with experimental data. Finally we demonstrate the methodology for simulation of the multicomponent compressible Navier-Stokes equations in a piston-bowl geometry, and discuss the computational efficiency gained by not requiring the entire embedded boundary to be defined at the finest level.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Experimental and kinetic modeling study of tetralin: A naphtheno-aromatic fuel for gasoline, jet and diesel surrogates

Distillate fuels contain significant proportions of naphtheno-aromatic components and tetralin is a suitable surrogate component to represent this molecular moiety. The presence of aromatic and naphthyl rings makes kinetic modeling of tetralin very challenging. Primary radicals formed during the oxidation of tetralin can be aryl, benzylic or paraffinic in nature. Using available information on reaction paths and rate constants of naphthenes and alkyl-aromatics, in this study a kinetic model of tetralin has been developed with emphasis on low-temperature chemistry and high-pressure conditions. Due to the lack of high-level quantum chemical calculations on reaction pathways of tetralin, analogous rates from ab-initio studies on benzylic and paraffinic radicals have been adopted here. Some modifications to the reaction rate rules are incorporated to account for the unique characteristics of tetralin's molecular structure. Important reaction channels have been identified using reaction path and brute force sensitivity analyses. In order to investigate the model performance at low temperatures, new experiments are carried out in a rapid compression machine on blends of tetralin and 3-methylpentane. Blending of low-reactivity tetralin with a high-reactivity alkane allowed the investigation of tetralin ignition at very low temperatures (665 – 856 K). The kinetic model developed in the current study is found to predict the current experiments and literature data adequately. The new model will aid in high-fidelity surrogate predictions at engine-relevant conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗