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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Relativistic ultrafast electron diffraction at high repetition rates

The ability to resolve the dynamics of matter on its native temporal and spatial scales constitutes a key challenge and convergent theme across chemistry, biology, and materials science. The last couple of decades have witnessed ultrafast electron diffraction (UED) emerge as one of the forefront techniques with the sensitivity to resolve atomic motions. Increasingly sophisticated UED instruments are being developed that are aimed at increasing the beam brightness in order to observe structural signatures, but so far they have been limited to low average current beams. Here, we present the technical design and capabilities of the HiRES (High Repetition-rate Electron Scattering) instrument, which blends relativistic electrons and high repetition rates to achieve orders of magnitude improvement in average beam current compared to the existing state-of-the-art instruments. The setup utilizes a novel electron source to deliver femtosecond duration electron pulses at up to MHz repetition rates for UED experiments. Instrument response function of sub-500 fs is demonstrated with < 100 fs time resolution targeted in future. We provide example cases of diffraction measurements on solid-state and gas-phase samples, including both micro- and nanodiffraction (featuring 100 nm beam size) modes, which showcase the potential of the instrument for novel UED experiments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

The molecular dipole moment ( μ ) is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra as well as induction and long-range electrostatic interactions. Furthermore, it can be extracted directly—via the ground state electron density—from high-level quantum mechanical calculations, making it an ideal target for machine learning (ML). Here, we choose to represent this quantity with a physically inspired ML model that captures two distinct physical effects: local atomic polarization is captured within the symmetry-adapted Gaussian process regression framework which assigns a (vector) dipole moment to each atom, while the movement of charge across the entire molecule is captured by assigning a partial (scalar) charge to each atom. The resulting “MuML” models are fitted together to reproduce molecular μ computed using high-level coupled-cluster theory and density functional theory (DFT) on the QM7b dataset, achieving more accurate results due to the physics-based combination of these complementary terms. The combined model shows excellent transferability when applied to a showcase dataset of larger and more complex molecules, approaching the accuracy of DFT at a small fraction of the computational cost. We also demonstrate that the uncertainty in the predictions can be estimated reliably using a calibrated committee model. The ultimate performance of the models—and the optimal weighting of their combination—depends, however, on the details of the system at hand, with the scalar model being clearly superior when describing large molecules whose dipole is almost entirely generated by charge separation. These observations point to the importance of simultaneously accounting for the local and non-local effects that contribute to μ ; furthermore, they define a challenging task to benchmark future models, particularly those aimed at the description of condensed phases.

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

Machine learning substitutional defect formation energies in ABO 3 perovskites

Perovskite oxides are a promising material platform for use in a wide range of technological applications including electronics, sensors, fuel cells, and catalysis. This is owing to the extraordinary tunability of their physical and chemical properties via defect engineering. The feasibility and the stability of a defect, such as a substitutional dopant, in the host lattice is usually obtained via experiments and/or through detailed quantum mechanical calculations. Both of these conventional routes are expensive and time consuming. An alternative is a data-driven machine learning (ML)-based approach. In this work, we have applied ML techniques to identify the factors that influence defect formation energy, which is an important measure of the stability of the defects, in perovskite oxides. Using 13 elemental properties as features and random forest regression, we demonstrate a systematic approach to down-selecting from the larger set of features to those that are important, establishing a framework for accurate predictions of the defect formation energy. We quantitatively show that the most important factors that control the dopant stability are the dopant ionic size, heat of formation, effective tolerance factor, and oxidation state. Our work reveals previously unknown correlations, chemical trends, and the interplay between stability and underlying chemistries. Hence, these results showcase the efficacy of ML tools in identifying and quantifying different feature-dependencies and provide a promising route toward dopant selection in the perovskites. Herein, we have developed a framework that itself is general and can be potentially applied to other material classes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Servo-aero-gravo-elastic (SAGE) scaling and its application to a 13-MW downwind turbine

Reduced scale wind turbines can be extremely cost-effective to test new rotor concepts since prototype costs are heavily dependent on the rotor diameter. Ideally, the scaled model would have the same non-dimensional deflections, dynamics, and control behavior as the full-scale model. This would provide a high-fidelity demonstration of the full-scale performance, which is ideal if the full-scale turbine has significant aeroelastic interactions. To this end, servo-aero-gravo-elastic (SAGE) scaling is developed and applied to a 13-MW turbine that is scaled to a 20% scale model. The scaling preserves the tip-speed ratio, the rotor speed normalized by the flapping frequency, and the tip deflections normalized by the blade length. In addition, the controller employs the same control structure (gain-scheduled pitch control and variable speed torque control) and is scaled dynamically (e.g., matching non-dimensional time constant of the pitch angle, etc.). Furthermore, the thrust, gravity, and centrifugal moments are scaled such that the load angles are preserved as a function of a non-dimensional wind speed. However, the environmental scaling must consider differences in Reynolds number (since this parameter cannot be held constant) and subsequent changes in the axial induction factor. While the presented results showcase these differences during operational conditions, the non-dimensional tip deflections remain comparable through all wind speed ranges, indicating the viability of the SAGE scaling method in matching full-scale aeroelastic responses.

Kaminski, Meghan (ORCID:0000000322334784)↗
DOE OSTI2020

Singlet fission

Singlet fission (SF) is a process whereby a photo-excited singlet exciton spontaneously splits into lower energy triplet excitons. This process has been known to occur in select organic materials for over 50 years but in the past was viewed as a curiosity and not as a problem worthy of focused attention. Over the past decade, this viewpoint has changed as the promising use of SF to improve photovoltaic efficiency has spurred vast activity, which has revealed the richness of the phenomenon. Currently, our understanding of the role played by various bright and dark states and their couplings to vibrations and to each other and by chemical composition and packing, among other factors, has enabled a large increase in the number of viable SF systems as well as a far deeper understanding of the process itself. These advances have allowed for the demonstration of proof-of-concept increases in the solar cell efficiency via the use of SF materials in simple tandem cells and as sensitizers in conjunction with conventional photovoltaics. Indeed, concepts surrounding the field have now invaded other fields, with a prominent example being the use of photon upconversion associated with triplet–triplet fusion (the inverse process to SF) in organic chemistry and photocatalysis. The 23 research articles in this special topic showcase recent cutting-edge advances in the field and highlight the diverse new directions being explored in the field of SF.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Nonadiabatic couplings from a variational excited state method based on constrained DFT

Excited Costrained Density Functional Theory (XCDFT) is a variational excited state method that extends ground state DFT to the computation of low-lying excited states. It borrows much of the machinery of Constrained DFT (CDFT) with a crucial difference: the constraint imposes a population of one electron in the Hilbert space spanned by the virtuals of a reference ground state. Here, we present theory and implementation for evaluating nonadiabatic coupling vectors (NACVs) between the first excited state computed with XCDFT and the ground state. Our NACVs are computed analytically using density functional perturbation theory with a formalism that is general enough that could be applied to CDFT diabatic states. We showcase the new method with pilot NACV calculations for the conical intersection in H 3 , the avoided crossing in selenoacrolein, and the NACV magnitudes in azobenzene. Despite complications from the nonorthogonality of the wavefunctions, XCDFT’s energy surfaces and NACVs reproduce benchmark values and respect known sum rules within a reasonable degree. This shows that XCDFT is a viable method for nonadiabatic dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Tailoring magnetization reversal of a single-domain bar nanomagnet via its end geometry

Nanoscale single-domain bar magnets are building blocks for a variety of fundamental and applied mesoscopic magnetic systems, such as artificial spin ices, magnetic shape-morphing microbots, and magnetic majority logic gates. The magnetization reversal switching field of the bar nanomagnets is a crucial parameter that determines the physical properties and functionalities of their constituted artificial systems. Previous methods on tuning the magnetization reversal switching field of a bar nanomagnet usually relied on modifying its aspect ratio, such as its length, width, and/or thickness. Here, we show that the switching field of a bar nanomagnet saturates when extending its length beyond a certain value, preventing further tailoring of the magnetization reversal via aspect ratios. We showcase a highly tunable switching field of a bar nanomagnet by tailoring its end geometry without altering its size. This provides an easy method to control the magnetization reversal of a single-domain bar nanomagnet. It would enable new research and/or applications, such as designing artificial spin ices with additional tuning parameters, engineering magnetic microbots with more flexibility, and developing magnetic quantum-dot cellular automata systems for low power computing.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Controlling the symmetry of cadmium arsenide films by epitaxial strain

Epitaxial strains offer unique opportunities to obtain topological states in thin films and heterostructures that do not exist in their bulk counterparts. Here, we investigate the point group symmetries of coherently strained films of cadmium arsenide (Cd 3 As 2 ), a prototype three-dimensional Dirac semimetal, by convergent beam electron diffraction. We report a loss of the fourfold rotational axis and adoption of the orthorhombic mmm point group in (112)-oriented films under biaxial compressive stress. (001)-oriented Cd 3 As 2 films that are under a small biaxial tensile stress retain the fourfold rotational symmetry that protects the bulk nodes but adopt the non-centrosymmetric 4mm point group symmetry. This, in turn, suggests that (001) films adopt a different crystal structure in biaxial tension, one that differs in the arrangement of the ordered Cd vacancies that are an inherent feature of the crystal structure of Cd 3 As 2 and that are key to its nodal electronic structure. Density functional theory calculations confirm the experimental findings of the stability of the non-centrosymmetric structure under biaxial tension, whereas the centrosymmetric structure is stable under biaxial compression. The results show that bulk Cd 3 As 2 is already close to structural instability and showcase the extraordinary tunability of the topological states of Cd 3 As 2 .

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning framework for quantum sampling of highly constrained, continuous optimization problems

In recent years, there is growing interest in using quantum computers for solving combinatorial optimization problems. In this work, we developed a generic, machine learning-based framework for mapping continuous-space inverse design problems into surrogate quadratic unconstrained binary optimization (QUBO) problems by employing a binary variational autoencoder and a factorization machine. The factorization machine is trained as a low-dimensional, binary surrogate model for the continuous design space and sampled using various QUBO samplers. Using the D-Wave Advantage hybrid sampler and simulated annealing, we demonstrate that by repeated resampling and retraining of the factorization machine, our framework finds designs that exhibit figures of merit exceeding those of its training set. We showcase the framework’s performance on two inverse design problems by optimizing (i) thermal emitter topologies for thermophotovoltaic applications and (ii) diffractive meta-gratings for highly efficient beam steering. This technique can be further scaled to leverage future developments in quantum optimization to solve advanced inverse design problems for science and engineering applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

High-throughput, in situ imaging of multi-layer powder-blown directed energy deposition with angled nozzle

Laser metal additive manufacturing (AM) has become an increasingly popular technology due to its flexibility in geometry and materials. As one of the commercialized additive processes, powder-blown directed energy deposition (DED) has been used in multiple industries such as aerospace, automotive, medical device, etc. However, a lack of fundamental understanding remains for this process, and many opportunities for alloy development and implementation can be identified. A high-throughput, in situ DED system capable of multi-layer builds that can address these issues is presented here. In this work, implications of layer heights and energy density are investigated through an extensive process parameter sweep, showcasing the power of a high-throughput setup while also discussing multi-layer interactions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

AL4GAP: Active learning workflow for generating DFT-SCAN accurate machine-learning potentials for combinatorial molten salt mixtures

Machine learning interatomic potentials have emerged as a powerful tool for bypassing the spatiotemporal limitations of ab initio simulations, but major challenges remain in their efficient parameterization. We present AL4GAP, an ensemble active learning software workflow for generating multicomposition Gaussian approximation potentials (GAP) for arbitrary molten salt mixtures. The workflow capabilities include: (1) setting up user-defined combinatorial chemical spaces of charge neutral mixtures of arbitrary molten mixtures spanning 11 cations (Li, Na, K, Rb, Cs, Mg, Ca, Sr, Ba and two heavy species, Nd, and Th) and 4 anions (F, Cl, Br, and I), (2) configurational sampling using low-cost empirical parameterizations, (3) active learning for down-selecting configurational samples for single point density functional theory calculations at the level of Strongly Constrained and Appropriately Normed (SCAN) exchange-correlation functional, and (4) Bayesian optimization for hyperparameter tuning of two-body and many-body GAP models. Here, we apply the AL4GAP workflow to showcase high throughput generation of five independent GAP models for multicomposition binary-mixture melts, each of increasing complexity with respect to charge valency and electronic structure, namely: LiCl–KCl, NaCl–CaCl 2 , KCl–NdCl 3 , CaCl 2 –NdCl 3 , and KCl–ThCl 4 . Our results indicate that GAP models can accurately predict structure for diverse molten salt mixture with density functional theory (DFT)-SCAN accuracy, capturing the intermediate range ordering characteristic of the multivalent cationic melts.

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

Prediction of stability constants of metal–ligand complexes by machine learning for the design of ligands with optimal metal ion selectivity

The new LOGKPREDICT program integrates HostDesigner molecular design software with the machine learning (ML) program Chemprop. By supplying HostDesigner with predicted log K values, LOGKPREDICT enhances the computer-aided molecular design process by ranking ligands directly by metal–ligand binding strength. Harnessing reliable experimental data from a historic National Institute of Standards and Technology (NIST) database and data from the International Union of Pure and Applied Chemistry (IUPAC), we train message passing neural net algorithms. The multi-metal NIST-based ML model has a root mean square error (RMSE) of 0.629 ± 0.044 (R 2 of 0.960 ± 0.006), while two versions of lanthanide-only IUPAC-based ML models have, respectively, RMSE of 0.764 ± 0.073 (R 2 of 0.976 ± 0.005) and 0.757 ± 0.071 (R 2 of 0.959 ± 0.007). For relative log K predictions on an out-of-sample set of six ligands, demonstrating metal ion selectivity, the RMSE value reaches a commendably low 0.25. Here we showcase the use of LOGKPREDICT in identifying ligands with high selectivity for lanthanides in aqueous solutions, a finding supported by recent experimental evidence. We also predict new ligands yet to be verified experimentally. Therefore, our ML models implemented through LOGKPREDICT and interfaced with the ligand design software HostDesigner pave the way for designing new ligands with predetermined selectivity for competing metal ions in an aqueous solution.

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

Externally Heated Diamond ANvil Cell Experimentation (EH-DANCE) for studying materials and processes under extreme conditions

Externally heated diamond anvil cells provide a stable and uniform thermal environment, making them a versatile device to simultaneously generate high-pressure and high-temperature conditions in various fields of research, such as condensed matter physics, materials science, chemistry, and geosciences. The present study features the Externally Heated Diamond ANvil Cell Experimentation (EH-DANCE) system, a versatile configuration consisting of a diamond anvil cell with a customized microheater for stable resistive heating, bidirectional pressure control facilitated by compression and decompression membranes, and a water-cooled enclosure suitable for vacuum and controlled atmospheres. This integrated system excels with its precise control of both pressure and temperature for mineral and materials science research under extreme conditions. Here, we showcase the capabilities of the system through its successful application in the investigation of the melting temperature and thermal equation of state of high-pressure ice-VII at temperatures up to 1400 K. The system was also used to measure the elastic properties of solid ice-VII and liquid H 2 O using Brillouin scattering and Raman spectra of carbonates using Raman spectroscopy, highlighting the potential of the EH-DANCE system in high-pressure research.

Instruments & Instrumentation↗
DOE OSTI2023

Room temperature persisting surface charge carriers driven by intense terahertz electric fields in a topological insulator Bi 2 Se 3

Topologically protected surface current is highly promising for next-generation low-dissipation and disorder-tolerant quantum electronics and computing. Yet, electric transport from the co-existing bulk state dominates the responses of the Dirac surface state, especially at elevated temperatures relevant to technological applications. Here, we present an approach that convincingly showcases the generation, disentanglement, and precise control of enduring surface charge carriers on a topological insulator, Bi 2 Se 3 , with high bulk conductivity, all achieved at room temperature. By using pump–probe modulation spectroscopy under ultrabroadband driving tunable from 4 meV to 1.55 eV, we show the terahertz (THz) field-induced surface carriers by discovering their initial temporal responses dominant over high density trivial bulk carriers. Strikingly, the response of the induced surface carrier responses persists for more than ~5 ps and is enhanced by reducing pump photon energy. The dynamics and lifetime of the distinct surface response manifest themselves as the enhanced THz pump-induced THz transmission, which directly correlates with the transient negative THz conductivity. Increasing the THz driving field reduces the induced surface carrier lifetime and identifies, particularly, an optimal pump field of E s ~ 224 kV cm -1 for generating the dominant surface response relative to the bulk. This surface carrier dominant regime is suppressed by a joint effect of enhanced surface-bulk scattering and a more rapid saturation of surface excitation compared to the bulk that sets in above E s . The controllability of room temperature topologically surface carriers through pump photon energy offer compelling possibilities for extending this approach to other topological complex materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Al-rich AlGaN high electron mobility transistor gate metallization study up to 600 °C in air

Here we report a comparative study of three rectifying gate metals, W, Pd, and Pt/Au, on ultrawide bandgap Al 0.86 Ga 0.14 N barrier/Al 0.7 Ga 0.3 N channel high electron mobility transistors for use in extreme temperatures. The transistors were electrically characterized from 30 to 600 °C in air. Of the three gate metals, the Pt/Au stack exhibited the smallest change in threshold voltage (0.15 V, or 9% change between the 30 and 600 °C values, and a maximum change of 42%), the highest on/off current ratio (1.5 × 10 6 ) at 600 °C, and a modest forward gate leakage current (0.39 mA/mm for a 3,V gate bias) at 600 °C. These favorable results showcase AlGaN channel high electron mobility transistors' ability to operate in extreme temperature environments.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Alternative CNDOL Fockians for fast and accurate description of molecular exciton properties

CNDOL is an a priori, approximate Fockian for molecular wave functions. In this study, we employ several modes of singly excited configuration interaction (CIS) to model molecular excitation properties by using four combinations of the one electron operator terms. Those options are compared to the experimental and theoretical data for a carefully selected set of molecules. The resulting excitons are represented by CIS wave functions that encompass all valence electrons in the system for each excited state energy. The Coulomb–exchange term associated to the calculated excitation energies is rationalized to evaluate theoretical exciton binding energies. This property is shown to be useful for discriminating the charge donation ability of molecular and supermolecular systems. Multielectronic 3D maps of exciton formal charges are showcased, demonstrating the applicability of these approximate wave functions for modeling properties of large molecules and clusters at nanoscales. This modeling proves useful in designing molecular photovoltaic devices. Our methodology holds potential applications in systematic evaluations of such systems and the development of fundamental artificial intelligence databases for predicting related properties.

Chemistry↗
DOE OSTI2024

Verification of electromagnetic simulation capabilities in global gyrokinetic particle-in-cell code GTS

Recently, the numerical scheme presented by Mishchenko et al. enabled explicit gyrokinetic simulations of low-frequency electromagnetic instabilities in tokamaks at experimentally relevant values of plasma β⁠. This scheme resolved the long-standing cancellation problem that previously hindered gyrokinetic particle-in-cell code simulations of magnetohydrodynamic phenomena with inherently small parallel electric fields. Moreover, the scheme did not employ approximations that eliminate critical tearing-type instabilities. Here, we report on the implementation of this numerical scheme in the global gyrokinetic particle-in-cell code GTS. This implementation allows for a more complete and accurate picture of interaction between small scale turbulence and MHD modes in tokamaks. Additionally, we present a comprehensive set of verification simulations of numerous electromagnetic instabilities relevant to present-day tokamaks. These simulations encompass the kinetic ballooning mode, the internal kink mode, the tearing mode, the micro-tearing mode, and the toroidal Alfven eigenmode destabilized by energetic ions, which are all instrumental in understanding tokamak physics. We will also showcase the preliminary nonlinear simulations of kinetic ballooning instabilities and (2,1) island formation due to tearing mode instability. These simulations validate the accuracy of the scheme implementation and pave the way for studying how these instabilities affect plasma confinement and performance.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Spin echo small-angle neutron scattering using superconducting magnetic Wollaston prisms

Here, we show the implementation of superconducting magnetic Wollaston prisms for spin echo small-angle neutron scattering. Two calibration methods for the spin echo length are presented: one utilizing spin echo modulated small-angle neutron scattering and the other based on the neutron refraction by quartz wedge crystals. Our experimental results with polystyrene nano-particle colloids showcase the system’s efficacy in measuring both dilute and concentrated colloidal systems. Additionally, investigations into the pore diameter and pitch of a nano-porous alumina membrane demonstrate its capability in analyzing nano-porous materials. Furthermore, we discuss potential optimizations to further extend the accessible spin echo length.

47 OTHER INSTRUMENTATION↗