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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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All sources NASA NTRS DOE OSTI

DOE OSTI2020

Pairing Dynamics in Low-energy Nuclear Collisions

Superfluidity is a generic feature of various quantum systems at low temperatures and it is in particular important for the description of dynam- ics of low energy nuclear reactions. The Time-Dependent Density Func- tional Theory (TDDFT) is, to date, the only microscopic method which takes into account in a consistent way far-from-equilibrium dynamics of pairing field and single-particle degrees of freedom. Here, the local version of TDDFT, so-called TDSLDA, is particularly useful for the description of nuclear reactions and is well-suited for leadership class computers of hybrid (CPU+GPU) architecture. The preliminary results obtained for collisions involving both medium-mass and heavy nuclei at the energies around the Coulomb barrier are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2014

Novel Materials, Processing and Device Technologies for Space Exploration with Potential Dual-Use Applications

We highlight results of a broad spectrum of efforts on lower-temperature processing of nanomaterials, novel approaches to energy conversion, and environmentally rugged devices. Solution-processed quantum dots of copper indium chalcogenide semiconductors and multiwalled carbon nanotubes from lower-temperature spray pyrolysis are enabled by novel (precursor) chemistry. Metal-doped zinc oxide (ZnO) nanostructured components of photovoltaic cells have been grown in solution at low temperature on a conductive indium tin oxide substrate. Arrays of ZnO nanorods can be templated and decorated with various semiconductor and metallic nanoparticles. Utilizing ZnO in a more broadly defined energy conversion sense as photocatalysts, unwanted organic waste materials can potentially be repurposed. Current efforts on charge carrier dynamics in nanoscale electrode architectures used in photoelectrochemical cells for generating solar electricity and fuels are described. The objective is to develop oxide nanowire-based electrode architectures that exhibit improved charge separation, charge collection and allow for efficient light absorption. Investigation of the charge carrier transport and recombination properties of the electrodes will aid in the understanding of how nanowire architectures improve performance of electrodes for dye-sensitized solar cells. Nanomaterials can be incorporated in a number of advanced higher-performance (i.e. mass specific power) photovoltaic arrays. Advanced technologies for the deposition of 4H-silicon carbide are described. The use of novel precursors, advanced processing, and process studies, including modeling are discussed from the perspective of enhancing the performance of this promising material for enabling technologies such as solar electric propulsion. Potential impact(s) of these technologies for a variety of aerospace applications are highlighted throughout. Finally, examples are given of technologies with potential spin-offs for dual-use or terrestrial applications.

solid state devices↗

DOE OSTI2020

Symmetric out-of-plane configurations of diffractive gratings and method

Grating pair architectures used for short pulse stretching, compression and pulse shaping are enabled for use at arbitrary use angles without inducing spatial frequency chirp. These symmetric out-of-plane diffractive grating arrangements are capable of adding or removing spatial, temporal and angular chirp at any incidence angle.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Optically Excited Lasing in a Cavity‐Based, High‐Current‐Density Quantum Dot Electroluminescent Device

Abstract Laser diodes based on solution‐processable materials can benefit numerous technologies including integrated electronics and photonics, telecommunications, and medical diagnostics. An attractive system for implementing these devices is colloidal semiconductor quantum dots (QDs). The progress towards a QD laser diode has been hampered by rapid nonradiative Auger decay of optical‐gain‐active multicarrier states, fast device degradation at high current densities required for laser action, and unfavorable competition between optical gain and optical losses in a multicomponent device stack. Here we resolve some of these challenges and demonstrate optically excited lasing from fully functional high‐current density electroluminescent (EL) devices with an integrated optical resonator. This advance has become possible due to excellent optical gain properties of continuously graded QDs and a refined device architecture, which allows for highly efficient light amplification in a thin, EL‐active QD layer.

36 MATERIALS SCIENCE↗

NASA NTRS2000

High Energy Directly Pumped Ho:YLF Laser

The most commonly used crystal architecture to produce 2 micrometer laser is co-doping Ho and Tm into a single host crystal. In this method, the stored energy transfer from the Tm (3)F4 to the Ho (5)I7 manifold is not fast enough to warrant high efficiency for short pulse applications. By separating the Ho and the Tm ions and doping the Tm in YALO3 and the Ho in YLF, we were able to directly pump the Ho (5)I7 manifold with 1.94 micrometers. The Ho:YLF laser has produced 33 mJ at 2.062 micrometers with a quantum efficiency of 0.88. The performance of each laser will be presented.

Petros, Mulugeta↗

DOE OSTI2021

Microchemomechanical devices using DNA hybridization

Significance With simple DNA origami lever arms arranged in hinges and accordion structures, we amplify the nanometer displacements from DNA hairpin zippers to 4-μm motion, easily observable and quantified in real space and real time with conventional optical microscopy. Mechanically pulling a bead tethered on the accordion end, we measure high-energy recovery and retraction speeds up to 50 μm/s. On longer time scales, we have also opened and closed the hinges with light and heat. DNA nanotechnology, and particularly DNA origami, combined with colloids and emulsions can provide powerful architectures. The present study is a step toward activating such colloidal/cellular scale devices using DNA as a power source/fuel. We envision artificial active flagella, cilia, micropumps, and other cellular scale devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Autoregressive neural quantum states of Fermi Hubbard models

Neural quantum states (NQSs) have emerged as a powerful ansatz for variational quantum Monte Carlo studies of strongly correlated systems. Here, we apply recurrent neural networks (RNNs) and autoregressive transformer neural networks to the Fermi-Hubbard and the (non-Hermitian) Hatano-Nelson-Hubbard models in one and two dimensions. In both cases, we observe that the convergence of the RNN ansatz is challenged when increasing the interaction strength. We present a physically motivated and easy-to-implement strategy for improving the optimization, namely, by ramping of the model parameters. Furthermore, we investigate the advantages and disadvantages of the autoregressive sampling property of both network architectures. For the Hatano-Nelson-Hubbard model, we identify convergence issues that stem from the autoregressive sampling scheme in combination with the non-Hermitian nature of the model. Our findings provide insights into the challenges of the NQS approach and make the first step towards exploring strongly correlated electrons using this ansatz. Published by the American Physical Society 2025

Ibarra-García-Padilla, Eduardo (ORCID:000000019165↗

DOE OSTI2021

Randomized Algorithms for Scientific Computing (RASC)

Randomized algorithms have propelled advances in artificial intelligence (AI) and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability. Advances in data collection and numerical simulation have changed the dynamics of scientific research and motivate the need for randomized algorithms. For instance, advances in imaging technologies such as X-ray ptychography, electron microscopy, electron energy loss spectroscopy, or adaptive optics lattice light-sheet microscopy collect hyperspectral imaging and scattering data in terabytes, at breakneck speed enabled by state-of-the-art detectors. The data collection is exceptionally fast compared with its analysis. Likewise, advances in high-performance architectures have made exascale computing a reality and changed the economies of scientific computing in the process. Floating-point operations that create data are essentially free in comparison with data movement. Thus far, most approaches have focused on creating faster hardware. Ironically, this faster hardware has exacerbated the problem by making data still easier to create. Under such an onslaught, scientists often resort to heuristic deterministic sampling schemes (e.g., low-precision arithmetic, sampling every nth element) and sacrifice potentially valuable accuracy. Dramatically better results can be achieved via randomized algorithms, reducing the data size as much as or more than naive deterministic subsampling can achieve, while retaining the high accuracy of computing on the full data set. By randomized algorithms we mean those algorithms that employ some form of randomness in internal algorithmic decisions to accelerate time to solution, increase scalability, or improve reliability. Examples include matrix sketching for solving large-scale least-squares problems (see Figure 1) and stochastic gradient descent for training machine learning models. We are not recommending heuristic methods but rather randomized algorithms that have certificates of correctness and probabilistic guarantees of optimality and near-optimality. Such approaches can be useful beyond acceleration, for example, in understanding how to avoid measure zero worst-case scenarios that plague methods such as QR matrix factorization.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2015

Novel Materials, Processing, and Device Technologies for Space Exploration with Potential Dual-Use Applications

We highlight results of a broad spectrum of efforts on lower-temperature processing of nanomaterials, novel approaches to energy conversion, and environmentally rugged devices. Solution-processed quantum dots of copper indium chalcogenide semiconductors and multi-walled carbon nanotubes from lower-temperature spray pyrolysis are enabled by novel (precursor) chemistry. Metal-doped zinc oxide (ZnO) nanostructured components of photovoltaic cells have been grown in solution at low temperature on a conductive indium tin oxide substrate. Arrays of ZnO nanorods can be templated and decorated with various semiconductor and metallic nanoparticles. Utilizing ZnO in a more broadly defined energy conversion sense as photocatalysts, unwanted organic waste materials can potentially be re-purposed. Current efforts on charge carrier dynamics in nanoscale electrode architectures used in photoelectrochemical cells for generating solar electricity and fuels are described. The objective is to develop oxide nanowire-based electrode architectures that exhibit improved charge separation, charge collection and allow for efficient light absorption. Investigation of the charge carrier transport and recombination properties of the electrodes will aid in the understanding of how nanowire architectures improve performance of electrodes for dye-sensitized solar cells. Nanomaterials can be incorporated in a number of advanced higher-performance (i.e. mass specific power) photovoltaic arrays. Advanced technologies for the deposition of 4H-silicon carbide are described. The use of novel precursors, advanced processing, and process studies, including modeling are discussed from the perspective of enhancing the performance of this promising material for enabling technologies such as solar electric propulsion. Potential impact(s) of these technologies for a variety of aerospace applications are highlighted throughout. Finally, examples are given of technologies with potential spin-offs for dual-use or terrestrial applications.

thin films↗

DOE OSTI2025

Teacher-student training improves the accuracy and efficiency of machine learning interatomic potentials

Machine learning interatomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Recent MLIPs have tended towards more complex architectures trained on larger datasets. The resulting increase in computational and memory costs may prohibit the application of these MLIPs to perform large-scale MD simulations. Herein, we present a teacher-student training framework in which the latent knowledge from the teacher (atomic energies) is used to augment the students' training. We show that the light-weight student MLIPs have faster MD speeds at a fraction of the memory footprint compared to the teacher models. Remarkably, the student models can even surpass the accuracy of the teachers, even though both are trained on the same quantum chemistry dataset. Our work highlights a practical method for MLIPs to reduce the resources required for large-scale MD simulations.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Supramolecular assembly of blue and green halide perovskites with near-unity photoluminescence

The metal-halide ionic octahedron is the optoelectronic unit for halide perovskites, and a crown ether–assisted supramolecular assembly approach can pack various ionic octahedra into tunable symmetries. In this work, we demonstrate near-unity photoluminescence quantum yield (PLQY) blue and green emission with the supramolecular assembly of hafnium (Hf) and zirconium (Zr) halide octahedral clusters. (18C6@K) 2 HfBr 6 powders showed blue emission with a near-unity PLQY (96.2%), and green emission was also achieved with (18C6@K) 2 ZrCl 4 Br 2 powders at a PLQY of 82.7%. In conclusion, these highly emissive powders feature facile low-temperature solution-based synthesis conditions and maintain high PLQY in solution-processable semiconductor inks under ambient conditions, and they were used in thin-film displays and emissive three-dimensional–printed architectures that exhibited high spatial resolution.

36 MATERIALS SCIENCE↗

DOE OSTI2020

TeraChem: Accelerating electronic structure and ab initio molecular dynamics with graphical processing units

Developed over the past decade, TeraChem is an electronic structure and ab initio molecular dynamics software package designed from the ground up to leverage graphics processing units (GPUs) to perform large-scale ground and excited state quantum chemistry calculations in the gas and the condensed phase. TeraChem’s speed stems from the reformulation of conventional electronic structure theories in terms of a set of individually optimized high-performance electronic structure operations (e.g., Coulomb and exchange matrix builds, one- and two-particle density matrix builds) and rank-reduction techniques (e.g., tensor hypercontraction). Recent efforts have encapsulated these core operations and provided language-agnostic interfaces. Finally, this greatly increases the accessibility and flexibility of TeraChem as a platform to develop new electronic structure methods on GPUs and provides clear optimization targets for emerging parallel computing architectures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Scalable photonic integrated circuits for high-fidelity light control

Advances in laser technology have driven discoveries in atomic, molecular, and optical (AMO) physics and emerging applications, from quantum computers with cold atoms or ions, to quantum networks with solid-state color centers. This progress is motivating the development of a new generation of optical control systems that can manipulate the light field with high fidelity at wavelengths relevant for AMO applications. These systems are characterized by criteria: (C1) operation at a design wavelength of choice in the visible (VIS) or near-infrared (IR) spectrum, (C2) a scalable platform that can support large channel counts, (C3) high-intensity modulation extinction and (C4) repeatability compatible with low gate errors, and (C5) fast switching times. Here, we provide a pathway to address these challenges by introducing an atom control architecture based on VIS-IR photonic integrated circuit (PIC) technology. Based on a complementary metal–oxide–semiconductor fabrication process, this atom-control PIC (APIC) technology can meet system requirements (C1)–(C5). As a proof of concept, we demonstrate a 16-channel silicon-nitride-based APIC with (5.8±0.4)ns response times and >30dB extinction ratio at a wavelength of 780 nm.

Menssen, Adrian J.↗

DOE OSTI2021

Rotational and reflectional equivariant convolutional neural network for data-limited applications: Multiphase flow demonstration

This article deals with approximating steady-state particle-resolved fluid flow around a fixed particle of interest under the influence of randomly distributed stationary particles in a dispersed multiphase setup using convolutional neural network (CNN). The considered problem involves rotational symmetry about the mean velocity (streamwise) direction. Thus, this work enforces this symmetry using SE(3)-equivariant, special Euclidean group of dimension 3, CNN architecture, which is translation and three-dimensional rotation equivariant. This study mainly explores the generalization capabilities and benefits of a SE(3)-equivariant network. Accurate synthetic flow fields for Reynolds number and particle volume fraction combinations spanning over a range of [86.22, 172.96] and [0.11, 0.45], respectively, are produced with careful application of symmetry-aware data-driven approach.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS2012

High-Performance LWIR Superlattice Detectors and FPA Based on CBIRD Design

We report our recent efforts on advancing of antimonide superlattice based infrared photodetectors and demonstration of Focal Plane Arrays (FPA) based on a complementary barrier infrared detector (CBIRD) design. By optimizing design and growth condition we succeeded to reduce the operational bias of CBIRD single pixel detector without increase of dark current or degradation of quantum efficiency. We demonstrated a 1024x1024 pixel long-wavelength infrared focal plane array utilizing CBIRD design. An 11.5 ?m cutoff FPA without anti-reflection coating has yielded noise equivalent differential temperature of 53 mK at operating temperature of 80 K, with 300 K background and cold-stop. In addition, we demonstrated 320x256 format FPA based on the n-CBIRD design. The resulting FPAs yielded noise equivalent differential temperature of 26 mK at operating temperature of 80 K, with 300 K background and cold-stop. These results advance state-of-the art of superlattice detectors and demonstrated advantages of CBIRD architecture for realization of FPA.

unipolar barrier↗

DOE OSTI2020

Lasing from Finite Plasmonic Nanoparticle Lattices

Small lasers can generate coherent light for integrated photonics, in-vivo cellular imaging, and solid-state lighting. Unlike conventional lasers, plasmonic lasers can generate coherent light at sub-wavelength scales, although cavity architectures based on metal films and semiconducting gain exhibit large radiative losses and lack directional emission. In contrast, two-dimensional (2D) metal nanoparticle arrays surrounded by organic dyes can support lasing with high directionality at room temperature. Yet, the relationship between the number of nanoparticles in a finite lattice and their lasing emission characteristics is unknown. Here we show that the number of units in 2D gold nanoparticle lattices is critical to generate robust cavity resonances and lasing emission. Narrower lattice plasmons associated with stronger electromagnetic near fields are observed as nanoparticle number increases. Experimentally, we demonstrate lasing from a 30x30 nanoparticle lattice. Semi-quantum modeling indicates lower lasing thresholds and faster population inversion dynamics with higher nanoparticle numbers. These findings indicate that finite lattices of nanoparticles integrated with gain can function as independent, coherent light sources for optical multiplexing and lab-on-a-chip applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2023

Transfer learning as a method to reproduce high-fidelity non-local thermodynamic equilibrium opacities in simulations

Simulations of high-energy density physics often need non-local thermodynamic equilibrium opacity data. These data, however, are expensive to produce at relatively low fidelity. It is even more so at high fidelity such that the opacity calculations can contribute 95 % of the total computation time. This proportion can even reach large proportions. Neural networks can be used to replace the standard calculations of low-fidelity data, and the neural networks can be trained to reproduce artificial, high-fidelity opacity spectra. In this work, it is demonstrated that a novel neural network architecture trained to reproduce high-fidelity krypton spectra through transfer learning can be used in simulations. Further, it is demonstrated that this can be done while achieving a relative per cent error of the peak radiative temperature of the hohlraum of approximately 1 % to 4 % while achieving a 19.4 $\times$ speed up.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Incorporation of Physics Phenomenology into an Adaptive Algorithm Framework (Final Report)

We attempt to incorporate prior physics knowledge into a machine learning architecture at an applied application level (empirical) as opposed to the level of fundamental physics (first principles). The purpose of this work is to allow for the application of methods of physics-informed machine learning to a broad range of national security problems, while enhancing the trust in machine-learned models by decreasing the “black box” nature of such methods.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗