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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Discrete scale invariance of the quasi-bound states at atomic vacancies in a topological material

Recently, log-periodic quantum oscillations have been detected in the topological materials zirconium pentatelluride (ZrTe 5 ) and hafnium pentatelluride (HfTe 5 ), displaying an intriguing discrete scale invariance (DSI) characteristic. In condensed materials, the DSI is considered to be related to the quasi-bound states formed by massless Dirac fermions with strong Coulomb attraction, offering a feasible platform to study the long-pursued atomic-collapse phenomenon. Here, we demonstrate that a variety of atomic vacancies in the topological material HfTe 5 can host the geometric quasi-bound states with a DSI feature, resembling an artificial supercritical atom collapse. The density of states of these quasi-bound states is enhanced, and the quasi-bound states are spatially distributed in the “orbitals” surrounding the vacancy sites, which are detected and visualized by low-temperature scanning tunneling microscope/spectroscopy. By applying the perpendicular magnetic fields, the quasi-bound states at lower energies become wider and eventually invisible; meanwhile, the energies of quasi-bound states move gradually toward the Fermi energy (E F ). These features are consistent with the theoretical prediction of a magnetic field–induced transition from supercritical to subcritical states. The direct observation of geometric quasi-bound states sheds light on the deep understanding of the DSI in quantum materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Domain-specific compilers for dynamic simulations of quantum materials on quantum computers

Abstract Simulation of the dynamics of quantum materials is emerging as a promising scientific application for noisy intermediate-scale quantum (NISQ) computers. Due to their high gate-error rates and short decoherence times, however, NISQ computers can only produce high-fidelity results for those quantum circuits smaller than some given circuit size. Dynamic simulations, therefore, pose a challenge as current algorithms produce circuits that grow in size with each subsequent time-step of the simulation. This underscores the crucial role of quantum circuit compilers to produce executable quantum circuits of minimal size, thereby maximizing the range of physical phenomena that can be studied within the NISQ fidelity budget. Here, we present two domain-specific (DS) quantum circuit compilers for the Rigetti and IBM quantum computers, specifically designed to compile circuits simulating dynamics under a special class of time-dependent Hamiltonians. The compilers outperform state-of-the-art general-purpose compilers in terms of circuit size reduction by around 25%–30% as well as wall-clock compilation time by around 40% (dependent on system size and simulation time-step). Drawing on heuristic techniques commonly used in artificial intelligence, both compilers scale well with simulation time-step and system size. Code for both compilers is open-source and packaged into a full-stack quantum simulation software with tutorials included for ease of use for future researchers wishing to perform dynamic simulations of quantum materials on quantum computers. As our DS compilers provide significant improvements in both compilation time and simulation fidelity, they provide a building block for accelerating progress toward physical quantum supremacy.

Physics↗
DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Programmable hyperbolic polaritons in van der Waals semiconductors

Lighting a route for hyperbolic dispersion The propagation of light within a material is usually well defined, with the propagation described by scattering and dispersion. In artificially designed metamaterials and in anisotropic layered materials, the dispersion can be hyperbolic, giving rise to subwavelength confinement of the light. Sternbach et al. show that the hyperbolic dispersion can be optically switched on and off on demand in the layered transition metal dichalcogenide tungsten diselenide (see the Perspective by Deng and Chen). Illuminating the material with ultrafast pulses of sub-bandgap light creates a transient waveguide, resulting in hyperbolic dispersion in the material. The ability to tune the dispersion characteristics on demand using optical pumping is an effective approach for developing ultrafast switching photonic devices and controlling the propagation of light on the nanoscale. Science , this issue p. 617 ; see also p. 572

Science & Technology - Other Topics↗
DOE OSTI2021

Signatures of moiré trions in WSe 2 /MoSe 2 heterobilayers

Moiré superlattices formed by van der Waals materials can support a wide range of electronic phases, including Mott insulators superconductors and generalized Wigner crystals2. When excitons are confined by a moiré superlattice, a new class of exciton emerges, which holds promise for realizing artificial excitonic crystals and quantum optical effects. When such moiré excitons are coupled to charge carriers, correlated states may arise. However, no experimental evidence exists for charge-coupled moiré exciton states, nor have their properties been predicted by theory. In this work, we report the optical signatures of trions coupled to the moiré potential in tungsten diselenide/molybdenum diselenide heterobilayers. The moiré trions show multiple sharp emission lines with a complex charge-density dependence, in stark contrast to the behaviour of conventional trions. We infer distinct contributions to the trion emission from radiative decay in which the remaining carrier resides in different moiré minibands. Variation of the trion features is observed in different devices and sample areas, indicating high sensitivity to sample inhomogeneity and variability. The observation of these trion features motivates further theoretical and experimental studies of higher-order electron correlation effects in moiré superlattices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Exponential Escape Rate of Filamentary Incubation in Mott Spiking Neurons

Mott materials such as vanadium oxides, when subject to a strong applied voltage, present an inhomogeneous insulator-to-metal transition with formation of metallic filaments within the insulating bulk. This property is enabling the development of compact and power-efficient neuromorphic devices known as Mott neurons. However, the nature of the transition has not been fully understood yet, as it may be attributed to different effects, including Joule self-heating and hot-carrier injection. Moreover, the experimental determination of the threshold voltage needed to induce the transition has proven to be challenging, as the transition becomes increasingly unpredictable when the threshold is approached. The physical understanding of these issues would not only deepen our understanding of Mott insulators, but would also be an important step toward the realization of neuromorphic devices based on such materials. In this work we use numerical simulations based on the Mott resistor network model to study the nature of the filament incubation and formation process. Here, we show that both electronic and thermal effects, in the form of current density focusing and Joule self-heating, respectively, contribute to the filamentary incubation and growth. Remarkably, we find that the percolation of the metallic filaments near the threshold is intrinsically stochastic, qualitatively similar to the familiar Arrhenius activated behavior and to the stochastic firing of biological neurons. More precisely, we characterize the filament percolation as a Poisson point process, which has the same probability distribution as mathematical models of neuronal firing with an exponential escape rate. Finally, we support the numerical simulation results by performing experiments in VO 2 that are in agreement with the exponential escape rate behavior. Thus, we establish a functionality of Mott insulators that opens a path toward implementing neuromorphic hardware with quantum materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Probing Electron Correlations in 1D Electronic Materials Using Single Quantum Channels (Final Technical Report)

This research focuses on the investigation of strong electron correlations in quasi-one-dimensional oxide nanostructures. The archetypical system consisting of heterostructures formed from an ultrathin layer of LaAlO 3 and TiO 2 -terminated SrTiO 3 offers a unique laboratory for probing strong correlations due to intrinsic richness of the physical system and its ability to be modulated at extreme nanoscale dimensions. Under suitable conditions, SrTiO 3 is known to exhibit a variety of important properties associated with strong electronic correlations, especially superconductivity, magnetism. Understanding the nature of these electronic correlations at fundamental scales, and in reduced spatial dimensions, will provide the scientific basis for new energy technologies, information technologies, and materials. The research program is motivated by recent discoveries and technical advances in the PIs' lab, specifically, (1) pioneering work to create conductive nanostructures to be created in LaAlO 3 /SrTiO 3 heterointerfaces with 2 nm spatial resolution, (2) the discovery of a novel correlated electronic phases in which electrons pair without forming a superconductor, and (3) the demonstrated ability to form high-quality quantum point contacts (QPCs) and achieve ballistic electron transport over micrometer-scales in LaAlO 3 /SrTiO 3 nanowires. The program has three major components. First, we have press forward to develop a full microscopic understanding of the mechanism for electron pairing in LaAlO 3 /SrTiO 3 nanostructures. Understanding the “glue” that leads to pairing and superconductivity in a doped semiconductor will help to form a scientific basis for the correlated oxide nanoelectronics platform being developed. Second, we have used their ability to create well-defined quantum channels in nanowires allows to allow novel “scattering” experiments to be performed that probe fundamentally new quasiparticle excitations using an unprecedented combination of experimental methodologies. Experiments already underway with artificially defined superlattices (5 nm period) demonstrate the feasibility of this approach. Third, we have investigated 1D correlations in proximally coupled nanowires, which are predicted exhibit Majorana-like zero modes in the absence of superconductivity or spin-orbit coupling. These studies provide insight not only into new and emergent phenomena in the rich class of complex oxides, but also fundamental mechanisms of superconductivity and magnetism. Thematically, the research aims to combine some of the most important and interesting challenges in the physics of semiconductor nanostructures and correlated electronic materials. The increased understanding of electron correlations can pave the way to the development of new materials with outstanding or novel properties, which could be relevant for energy-related technologies such as low-power high performance computing, quantum computation, or the development of novel materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Accelerated Selectrion of Optimal Perovskite Alloys for Solar PV using a Combined Quantum and Machine Learning Hierachiral Approach

The project aims to: (i) accelerate the discovery of “Missing HP alloys” by combining quantum mechanics and artificial intelligence machine learning approaches, and (ii) analyze the stabilities of candidate alloys, including those that do not pass selection filters (and are thus expected to degrade over time) to decipher the nature of the instabilities to guide the development of durable solar cell materials. Successful candidates will be subjected to validation experiments at NREL's state-of-the-art facilities. The discoveries this effort will provide will be directly testable and implementable and will greatly impact U.S. progress in HP PV as they will provide clear direction and motivation for experimental studies including specific material synthetic targets, device optimization, and device stability protocols. A key advantage of this effort is the feedback and guidance provided by the Industry Collaborative Work Group that we established to coordinate academic and national lab research with industry needs. The proposed work will provide a basis for and direct the development of robust and reliable HP PV. It will also provide a timely, valuable and extensive roadmap to the experimental HP PV community to enable it to focus its efforts on improving and fine-tuning promising HP compositions that this effort predicts will likely be the best performers rather than wandering in the vast chemical space for decades spending enormous resources mostly evaluating unpromising candidate materials.

14 SOLAR ENERGY↗
DOE OSTI2022

Representations and strategies for transferable machine learning improve model performance in chemical discovery

Strategies for machine-learning (ML)-accelerated discovery that are general across material composition spaces are essential, but demonstrations of ML have been primarily limited to narrow composition variations. By addressing the scarcity of data in promising regions of chemical space for challenging targets such as open-shell transition-metal complexes, general representations and transferable ML models that leverage known relationships in existing data will accelerate discovery. Over a large set (~1000) of isovalent transition-metal complexes, we quantify evident relationships for different properties (i.e., spin-splitting and ligand dissociation) between rows of the Periodic Table (i.e., 3d/4d metals and 2p/3p ligands). We demonstrate an extension to the graph-based revised autocorrelation (RAC) representation (i.e., eRAC) that incorporates the group number alongside the nuclear charge heuristic that otherwise overestimates dissimilarity of isovalent complexes. To address the common challenge of discovery in a new space where data are limited, we introduce a transfer learning approach in which we seed models trained on a large amount of data from one row of the Periodic Table with a small number of data points from the additional row. We demonstrate the synergistic value of the eRACs alongside this transfer learning strategy to consistently improve model performance. Analysis of these models highlights how the approach succeeds by reordering the distances between complexes to be more consistent with the Periodic Table, a property we expect to be broadly useful for other material domains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2001

Quantum Dots Based Rad-Hard Computing and Sensors

Quantum Dots (QDs) are solid-state structures made of semiconductors or metals that confine a small number of electrons into a small space. The confinement of electrons is achieved by the placement of some insulating material(s) around a central, well-conducting region. Thus, they can be viewed as artificial atoms. They therefore represent the ultimate limit of the semiconductor device scaling. Additional information is contained in the original extended abstract.

Fijany, A.↗
DOE OSTI2021

AENET–LAMMPS and AENET–TINKER : Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials

Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the ænet–TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet–LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications: the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials.

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

Cadmium-Free QD Building Blocks for Human Centric Lighting

This project developed Cd-free quantum dot (QD) downconverters for efficacious human-centric lighting (HCL) light emitting diode (LED) devices. Solid state HCL devices address the lack of light in the cyan wavelength region (460-490 nm) in white LEDs by enhancing the melanopic daylight efficacy ratio (MDER) value. MDER is a metric that indicates the degree to which artificial lighting stimulates nonvisual biological processes in the retina responsible for regulating circadian rhythms compared with natural lighting. The peak sensitivity of melanopsin in the retina is at 479 nm, therefore artificial lighting which can fill the well-known “cyan gap” may improve human health. Due to the Restriction of Hazardous Substances (RoHS) regulations, cadmium-free core/shell/shell QDs are the primary targets for low toxicity, tunable downconverters. Cyan- and red-emitting core/shell/shell QDs were implemented in LED devices toward achieving a brightness of 210 lm/W at 4000 K, CRI 90, and MDER > 0.7. Synthetic development of Cd-free materials yielded cyan InP/ZnS QDs with a photoluminescence quantum yield (PLQY) of ~60% between 480-490 nm, and red InP/ZnSe/(ZnSeS)/ZnS QDs reaching ~80% PLQY at 620-630 nm. We successfully demonstrated that the inclusion of cyan QDs increased the MDER value to ≥0.7, but the brightness of the HCL LED devices was hindered due to low PLQY values. However, upon substitution of the Cd-free cyan QD with a low-Cd QD with >90% PLQY, brightness improved by 25% over the Cd-free device to 179 lm/W. Despite the technical obstacles remaining toward improving emission characteristics and stability of QDs under high flux, we have confirmed the promise of narrow, tunable QD emitters in SSL packages toward the goal of healthy, human-centric lighting.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

High magnetic field response of superconductivity dome in quantum artificial high−⁢𝑇 𝐶 superlattices with variable geometry

It is known that cuprate artificial high-𝑇 𝐶 superlattices (AHTS) with period 𝑑, composed of quantum wells confining interface space charge in stoichiometric Mott insulator layers (𝑆), with thickness 𝐿, at the interface with overdoped normal metallic cuprate layers (𝑁) show a superconducting dome by tuning the geometric 𝐿 over 𝑑 ratio of the SNSN superlattice with the top predicted by quantum material design engineering quantum size effects. Here we report high-field magnetotransport measurements up to 41 Tesla of AHTS across the entire superconducting dome. The results show the universal upward-concave behavior of the temperature-dependent upper critical magnetic field in low-𝑇 𝐶 samples at the rising edge and drop edge of the dome, providing strong evidence consistent with two-band superconductivity in agreement with multigap theory used for quantum design of the SNSN superlattices. The measured superconducting coherence length demonstrates that atomic-scale engineering controls not only the critical temperature but also the intrinsic pair size at Fano-Feshbach resonances physics paving the way toward next-generation quantum devices and shedding light on unconventional superconductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Neural-adjoint method for the inverse design of all-dielectric metasurfaces

All-dielectric metasurfaces exhibit exotic electromagnetic responses, similar to those obtained with metal-based metamaterials. Research in all-dielectric metasurfaces currently uses relatively simple unit-cell designs, but increased geometrical complexity may yield even greater scattering states. Although machine learning has recently been applied to the design of metasurfaces with impressive results, the much more challenging task of finding a geometry that yields a desired spectra remains largely unsolved. We propose and demonstrate a method capable of finding accurate solutions to ill-posed inverse problems, where the conditions of existence and uniqueness are violated. A specific example of finding the metasurface geometry which yields a radiant exitance matching the external quantum efficiency of gallium antimonide is demonstrated. We also show how the neural-adjoint method can intelligently grow the design search space to include designs that increasingly and accurately approximate the desired scattering response. The neural-adjoint method is not restricted to the case demonstrated and may be applied to plasmonics, photonic crystal, and other artificial electromagnetic materials.

42 ENGINEERING↗
NASA NTRS2006

Long-Wavelength Infrared (LWIR) Quantum Dot Infrared Photodetector (QDIP) Focal Plane Array

We have exploited the artificial atomlike properties of epitaxially self-assembled quantum dots for the development of high operating temperature long wavelength infrared (LWIR) focal plane arrays. Quantum dots are nanometer-scale islands that form spontaneously on a semiconductor substrate due to lattice mismatch. QDIPs are expected to outperform quantum well infrared detectors (QWIPs) and are expected to offer significant advantages over II-VI material based focal plane arrays. QDIPs are fabricated using robust wide bandgap III-V materials which are well suited to the production of highly uniform LWIR arrays. We have used molecular beam epitaxy (MBE) technology to grow multi-layer LWIR quantum dot structures based on the InAs/InGaAs/GaAs material system. JPL is building on its significant QWIP experience and is basically building a Dot-in-the-Well (DWELL) device design by embedding InAs quantum dots in a QWIP structure. This hybrid quantum dot/quantum well device offers additional control in wavelength tuning via control of dot-size and/or quantum well sizes. In addition the quantum wells can trap electrons and aide in ground state refilling. Recent measurements have shown a 10 times higher photoconductive gain than the typical QWIP device, which indirectly confirms the lower relaxation rate of excited electrons (photon bottleneck) in QDPs. Subsequent material and device improvements have demonstrated an absorption quantum efficiency (QE) of approx. 3%. Dot-in-the-well (DWELL) QDIPs were also experimentally shown to absorb both 45 deg. and normally incident light. Thus we have employed a reflection grating structure to further enhance the quantum efficiency. JPL has demonstrated wavelength control by progressively growing material and fabricating devices structures that have continuously increased in LWIR response. The most recent devices exhibit peak responsivity out to 8.1 microns. Peak detectivity of the 8.1 micrometer devices has reached approx. 1 x 10(exp 10) Jones at 77 K. Furthermore, we have fabricated the first long-wavelength 640x512 pixels QDP focal plane array. This QDIP focal plane may has produced excellent infrared imagery with noise equivalent temperature difference of 40 mK at 60K operating temperature. In addition, we have managed to increase the quantum efficiency of these devices from 0.1% (according to the data published in literature) to 20% in discrete devices. This is a factor of 200 increase in quantum efficiency. With these excellent results, for the first time QDIP performance has surpassed the QWIP performance. Our goal is to operate these long-wavelength detectors at much higher operating temperature than 77K which can be passively achieved in space. This will be a huge leap in high performance infrared detectors specifically applicable to space science instruments.

infrared imaging↗
DOE OSTI2023

Bond-dependent anisotropy and magnon decay in cobalt-based Kitaev triangular antiferromagnet

Following tremendous success of the graphene-derived fundamental and applied research, the magnetic van der Waals materials that can be cleaved into monolayer two-dimensional atomic crystals have emerged as a new platform in the studies of low-dimensional physics and in the design of artificial heterostructures with novel properties. Because of that, the family of dihalides and trihalides of the 3d transition group receive a strong renewed interest. Together with our experimental collaborators, we have investigated the novel dynamical properties of one of such promising material CoI 2 , and demonstrated that spin excitations in it are prone to substantial breakdown and complex level repulsion, the important quantum effects whose theoretical understanding has been significantly advanced by our group in the last two decades. Both phenomena are dramatically revealed by experiments and verified by the theory in our joint study, published in Nature Physics. Altogether, our results pave the way toward a new research direction for the magnetic van der Waals materials.

36 MATERIALS SCIENCE↗