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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Band structure, superconductivity, and polytypism in AuSn 4

The orthorhombic compound AuSn 4 is compositionally similar to the Dirac node arc semimetal PtSn 4 . AuSn 4 is, contrary to PtSn 4 , superconducting with a critical temperature of T c = 2.35 K. Recent measurements present indications for quasi-two-dimensional superconducting behavior in AuSn 4 . Here we present measurements of the superconducting density of states and the band structure of AuSn 4 through scanning tunneling microscopy and angular resolved photoemission spectroscopy (ARPES). The superconducting gap values in different portions of the Fermi surface are spread around Δ 0 = 0.4 meV, which is close to but somewhat larger than Δ = 1.76k B T c expected from BCS theory. We observe superconducting features in the tunneling conductance at the surface up to temperatures about 20% larger than bulk T c . The band structure calculated with density functional theory follows well the results of ARPES. The crystal structure presents two possible stackings of Sn layers, giving two nearly degenerate polytypes. This makes AuSn 4 a rather unique case with a three-dimensional electronic band structure but properties ressembling those of low-dimensional layered compounds.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Nonreciprocal Coulomb drag between quantum wires in the quasi-one-dimensional regime

Coulomb drag experiments have been an essential tool to study strongly interacting low-dimensional systems. Historically, this effect has been explained in terms of momentum transfer between electrons in the active and the passive layer. We report Coulomb drag measurements between laterally coupled GaAs/AlGaAs quantum wires in the multiple one-dimensional (1D) sub-band regime that break Onsager's reciprocity upon both layer and current direction reversal, in contrast to prior 1D Coulomb drag results. The drag signal shows nonlinear current-voltage (I-V) characteristics, which are well characterized by a third-order polynomial fit. These findings are qualitatively consistent with a rectified drag signal induced by charge fluctuations. However, the nonmonotonic temperature dependence of this drag signal suggests that strong electron-electron interactions, expected within the Tomonaga-Luttinger liquid framework, remain important and standard interaction models are insufficient to capture the qualitative nature of rectified 1D Coulomb drag.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Short-Range Order in VI 3

We present a detailed investigation of the crystal structure of VI 3 , a two-dimensional van der Waals material of interest for studies of low-dimensional magnetism. As opposed to the average crystal structure that features $R\bar{3}$ symmetry of the unit cell, our Raman scattering and X-ray atomic pair distribution function analysis supported by density functional theory calculations point to the coexistence of short-range ordered $P\bar{3}$1c and long-range ordered $R\bar{3}$phases. The highest-intensity peak, A 1g 3 , exhibits a moderate asymmetry that might be traced back to the spin–phonon interactions, as in the case of CrI 3 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

An implementation of neural simulation-based inference for parameter estimation in ATLAS

Neural simulation-based inference (NSBI) is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. This work develops a NSBI framework for statistical inference, using neural networks to estimate probability density ratios, which enables the application to a full-scale analysis. It incorporates a large number of systematic uncertainties, quantifies the uncertainty due to the finite number of events in training samples, develops a method to construct confidence intervals, and demonstrates a series of intermediate diagnostic checks that can be performed to validate the robustness of the method. As an example, the power and feasibility of the method are assessed on simulated data for a simplified version of an off-shell Higgs boson couplings measurement in the four-lepton final states. This approach represents an extension to the standard statistical methodology used by the experiments at the Large Hadron Collider, and can benefit many physics analyses.

frequentist statistics↗
DOE OSTI2026

Emergent surface resonance from charge density wave symmetry breaking in TiSe 2

Surface confined electronic states provide a fertile ground for discovering emergent phenomena that have no counterpart in the bulk, offering new routes to manipulate correlations, symmetry breaking, and dimensionality at the atomic scale. Here, in this study, we show that charge density wave (CDW) symmetry breaking can yield surface states in 1⁢T−TiSe 2 . Micro–angle-resolved photoemission spectroscopy (µ-ARPES) resolves a sharp, two-dimensional surface resonant state (SRS) that emerges within the CDW reconstructed low energy spectrum. The SRS exhibits notable temperature dependence and its spectral weight collapses around ∼ 160 K, while CDW transition temperature 𝑇 CDW is commonly reported as ≈ 202 K. Slab DFT + 𝑈 calculations reproduce a surface localized resonance when CDW folding brings valence and conduction states into near degeneracy, suggesting a correlation tuned, surface selective origin. These results point to a form of correlation-tuned surface resonance in a layered CDW compound and suggest a framework for engineering low-dimensional quantum states in van der Waals materials via symmetry breaking and electronic structure tuning.

36 MATERIALS SCIENCE↗
DOE OSTI2025

𝑁-dimensional maximum-entropy tomography via particle sampling

We propose a modified maximum-entropy (MENT) algorithm for six-dimensional phase space tomography. The algorithm uses particle sampling and low-dimensional density estimation to approximate large sets of high-dimensional integrals in the original MENT formulation. We implement this approach using Markov Chain Monte Carlo (MCMC) sampling techniques and demonstrate convergence of six-dimensional MENT on both synthetic and measured data.

Hoover, Austin [Oak Ridge National Laboratory (ORN↗
DOE OSTI2023

hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty

Bayesian inference provides a systematic framework for integration of data with mathematical models to quantify the uncertainty in the solution of the inverse problem. However, the solution of Bayesian inverse problems governed by complex forward models described by partial differential equations (PDEs) remains prohibitive with black-box Markov chain Monte Carlo (MCMC) methods. We present hIPPYlib-MUQ, an extensible and scalable software framework that contains implementations of state-of-the art algorithms aimed to overcome the challenges of high-dimensional, PDE-constrained Bayesian inverse problems. These algorithms accelerate MCMC sampling by exploiting the geometry and intrinsic low-dimensionality of parameter space via derivative information and low rank approximation. The software integrates two complementary open-source software packages, hIPPYlib and MUQ. hIPPYlib solves PDE-constrained inverse problems using automatically-generated adjoint-based derivatives, but it lacks full Bayesian capabilities. MUQ provides a spectrum of powerful Bayesian inversion models and algorithms, but expects forward models to come equipped with gradients and Hessians to permit large-scale solution. By combining these two complementary libraries, we created a robust, scalable, and efficient software framework that realizes the benefits of each and allows us to tackle complex large-scale Bayesian inverse problems across a broad spectrum of scientific and engineering disciplines. To illustrate the capabilities of hIPPYlib-MUQ, we present a comparison of a number of MCMC methods available in the integrated software on several high-dimensional Bayesian inverse problems. These include problems characterized by both linear and nonlinear PDEs, various noise models, and different parameter dimensions. The results demonstrate that large (~ 50×) speedups over conventional black box and gradient-based MCMC algorithms can be obtained by exploiting Hessian information (from the log-posterior), underscoring the power of the integrated hIPPYlib-MUQ framework.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1987

EA-6B high-lift wing modifications

NASA-Langley has accomplished the computational design and experimental verification of EA-6B aircraft wing modifications for improved high lift capability. The modifications are comparatively simple, and attempt to improve low speed high lift performance while maintaining high speed cruise efficiency. Several two- and three-dimensional low speed and transonic computational techniques were employed, together with extensive wind tunnel tests. The modified inboard and outboard edge slat/flap system sections yielded efficiency improvements that were verified by three-dimensional wind tunnel experiments to amount to an 11-percent wing-body lift coefficient enhancement at low speed.

Waggoner, E. G.↗
DOE OSTI2023

Energy Funneling in a Noninteger Two-Dimensional Perovskite

Energy funneling is a phenomenon that has been exploited in optoelectronic devices based on low-dimensional materials to improve their performance. Here, we introduce a new class of two-dimensional semiconductor, characterized by multiple regions of varying thickness in a single confined nanostructure with homogeneous composition. This "noninteger 2D semiconductor" was prepared via the structural transformation of two-octahedron-layer-thick (n = 2) 2D cesium lead bromide perovskite nanosheets; it consisted of a central n = 2 region surrounded by edge-lying n = 3 regions, as imaged by electron microscopy. Thicker noninteger 2D CsPbBr 3 nanostructures were obtained as well. Finally, these noninteger 2D perovskites formed a laterally coupled quantum well band alignment with virtually no strain at the interface and no dielectric barrier, across which unprecedented intramaterial funneling of the photoexcitation energy was observed from the thin to the thick regions using time-resolved absorption and photoluminescence spectroscopy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Nodal hybridization in a two-dimensional heavy-fermion material

Metals with partially filled core atomic shells can form quasiparticles at a low temperature arising from the hybridization of the core level and conduction electrons. The thermodynamic and spectroscopic properties of these metals can be understood as those of a simple metal, but with a significant mass enhancement over the free electron mass—commonly referred to as heavy fermions. In most heavy-fermion materials, the hybridization is approximately isotropic in position and momentum space. However, a combination of low dimensionality and symmetry properties of the core-level wavefunctions can give rise to highly anisotropic electronic interactions with the conduction electrons. Here, in this study, we demonstrate anisotropic hybridization that vanishes along specific directions in momentum space—referred to as nodes—in a lanthanide-based two-dimensional van der Waals heavy-fermion compound, CeSiI. Quasiparticle interference measurements reveal a set of discrete hotspots with high spectral intensity on the Fermi surface. Theoretical modelling and comparison with the quasiparticle interference pattern of the non-heavy-fermion isostructural analogue LaSiI suggest that these features arise from an unconventional electron interaction involving hybridization nodes unique to CeSiI. As a result, the effective mass of the quasiparticles varies by orders of magnitude depending on their direction in momentum space.

36 MATERIALS SCIENCE↗
DOE OSTI2022

One-dimensional Rashba states with unconventional spin texture in Bi chains

Spin-polarized electrons confined in low-dimensional structures are of high interest for spintronics applications. Here, in this study, we investigate the electronic structure of an ordered array of Bi monomer and dimer chains on the Ag(110) surface. By means of spin-resolved photoemission spectroscopy, we find Rashba-Bychkov split bands crossing the Fermi level with one-dimensional constant energy contours. These bands are up-spin polarized for positive wave vectors and down-spin polarized for negative wave vectors, at variance with the Rashba-Bychkov model that predicts a pair of states with opposite spin in each half of the surface Brillouin zone. Density functional theory shows that spin-selective hybridization with the Ag bulk bands originates this unconventional spin texture.

1-dimensional spin chains↗
DOE OSTI2023

Effect of environmental screening and strain on optoelectronic properties of two-dimensional quantum defects

Point defects in hexagonal boron nitride (hBN) are promising candidates as single-photon emitters (SPEs) in nanophotonics and quantum information applications. The precise control of SPEs requires in-depth understanding of their optoelectronic properties. However, how the surrounding environment of host materials, including the number of layers, substrates, and strain, influences SPEs has not been fully understood. Here, in this work, we study the dielectric screening effect due to the number of layers and substrates, and the strain effect on the optical properties of carbon dimer and nitrogen vacancy defects in hBN from first-principles many-body perturbation theory. We report that environmental screening causes a lowering of the quasiparticle gap and exciton binding energy, leading to nearly constant optical excitation energy and exciton radiative lifetime. We explain the results with an analytical model starting from the Bethe–Salpeter equation Hamiltonian with Wannier basis. We also show that optical properties of quantum defects are largely tunable by strain with highly anisotropic response, in good agreement with experimental measurements. Our work clarifies the effect of environmental screening and strain on optoelectronic properties of quantum defects in two-dimensional insulators, facilitating future applications of SPEs and spin qubits in low-dimensional systems.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder

Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes. For instance, the QVAE has been proposed as a quantum enhancement to the discrete VAE. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity search in large-scale high-dimensional datasets. While exact and fast similarity search algorithms are available for low dimensional datasets, scaling to high-dimensional data is non-trivial. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. Our experiments show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the MODIS dataset. Further, we find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points.

Data mining, similarity search, quantum machine le↗
DOE OSTI2021

Design principles and physical properties of two-dimensional heterostructured borides

Principles of design to create dynamically stable transition metal-, lanthanide-, and actinide-based low-dimensional borides are presented. A charge transfer analysis of donor metal atoms to electron deficient honeycombed B lattices allows to predict complex covalent heterostructures hosting Dirac states. The applicable guidelines are supported with the analysis of phonon spectra computed with first-principles calculations to demonstrate the physical stability of nanometer-thick heterostructures. Similar or dissimilar layered borides can be stacked on top of each other in a layer-by-layer fashion creating compounds that can be fundamentally different from the individual layers, opening a rich playground to explore novel physical properties and new materials. Functionalities such as multiple Dirac states, highly dispersive electronic bands, and decoupled acoustic-optical phonon branches are studied. The combination of appealing electronic properties and physical realization makes of predicted layered borides promising materials to integrate a new generation of two-dimensional materials.

2-dimensional systems↗
DOE OSTI2024

High-dimensional and permutation invariant anomaly detection

Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities. Particularly at the constituent level, incorporating desirable properties such as permutation invariance and variable-length inputs becomes difficult within popular density estimation methods. In this work, we introduce a permutation-invariant density estimator for particle physics data based on diffusion models, specifically designed to handle variable-length inputs. We demonstrate the efficacy of our methodology by utilizing the learned density as a permutation-invariant anomaly detection score, effectively identifying jets with low likelihood under the background-only hypothesis. To validate our density estimation method, we investigate the ratio of learned densities and compare to those obtained by a supervised classification algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems

Abstract Predicting complex dynamics in physical applications governed by partial differential equations in real-time is nearly impossible with traditional numerical simulations due to high computational cost. Neural operators offer a solution by approximating mappings between infinite-dimensional Banach spaces, yet their performance degrades with system size and complexity. We propose an approach for learning neural operators in latent spaces, facilitating real-time predictions for highly nonlinear and multiscale systems on high-dimensional domains. Our method utilizes the deep operator network architecture on a low-dimensional latent space to efficiently approximate underlying operators. Demonstrations on material fracture, fluid flow prediction, and climate modeling highlight superior prediction accuracy and computational efficiency compared to existing methods. Notably, our approach enables approximating large-scale atmospheric flows with millions of degrees, enhancing weather and climate forecasts. Here we show that the proposed approach enables real-time predictions that can facilitate decision-making for a wide range of applications in science and engineering.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Ultrafast-induced coherent acoustic phonons in the two-dimensional magnet CrSBr

Magnetism in two-dimensional (2D) van der Waals (vdW) crystals offers promising new directions for low-dimensional physics and devices. In this work, mega-electron volt (MeV) ultrafast electron diffraction was employed to investigate the ultrafast atomic dynamics of a novel, 2D vdW magnetic single-crystal CrSBr. Femtosecond (fs) optical pump pulses excited non-equilibrium atomic displacements shown to be coherent acoustic phonons (CAPs). Phonon frequencies were extracted by analyzing oscillations of different Bragg peak (BP) intensities and were determined to be GHz acoustic disturbances that propagated as strain waves. Phonon modes exhibit anisotropy with respect to the a and b crystal axes. Subharmonic phonon frequencies were also observed, and this provided a signature of nonlinear oscillatory coupling between the laser-induced pumping phonon frequency and secondary phonon frequencies. Thus, CrSBr was found to serve as a nonlinear phononic frequency converter. The ultrafast time dependence of the Bragg intensity was simulated by incorporating an oscillating deviation parameter ansatz into expressions for the dynamical scattering intensity yielded excellent modeling of the ultrafast structural dynamics of the photo-excited 2D crystal. Our work provides a foundation for exploring how fs light pulses can influence phonon dynamics in materials with strong spin-lattice coupling. These results suggest that CAPs can match the magnon frequencies and show the promise of CrSBr for use in optical-to-microwave transducers and phononic devices.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Star–Galaxy Image Separation with Computationally Efficient Gaussian Process Classification

Abstract We introduce a novel method for discerning optical telescope images of stars from those of galaxies using Gaussian processes (GPs). Although applications of GPs often struggle in high-dimensional data modalities such as optical image classification, we show that a low-dimensional embedding of images into a metric space defined by the principal components of the data suffices to produce high-quality predictions from real large-scale survey data. We develop a novel method of GP classification hyperparameter training that scales approximately linearly in the number of image observations, which allows for application of GP models to large-size Hyper Suprime-Cam Subaru Strategic Program data. In our experiments, we evaluate the performance of a principal component analysis embedded GP predictive model against other machine-learning algorithms, including a convolutional neural network and an image photometric morphology discriminator. Our analysis shows that our methods compare favorably with current methods in optical image classification while producing posterior distributions from the GP regression that can be used to quantify object classification uncertainty. We further describe how classification uncertainty can be used to efficiently parse large-scale survey imaging data to produce high-confidence object catalogs.

79 ASTRONOMY AND ASTROPHYSICS↗