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At least 433 records · Page 24
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DOE OSTI2026

Terahertz 2D coherent spectroscopy for probing and controlling multicorrelations in quantum matter

Terahertz 2D coherent spectroscopy (THz-2DCS) is an emerging technique that brings multidimensional resolution to the ultrafast spectral–temporal dynamics of non-equilibrium quantum phases of matter, enabling new capabilities for precise coherent control in many-body dynamics and multiorder correlations. Here, by mapping and disentangling complex excitation and detection pathways across distinct time and frequency dimensions, THz-2DCS provides a form of coherence tomography of light-induced quantum matter — revealing multiquantum coherences, separating nonlinear response functions and capturing collective modes and quantum kinetics on ultrafast THz timescales. This Perspective discusses the technical capabilities of THz-2DCS, provides a comparison to other multidimensional and coherent transient spectroscopies and looks ahead towards opportunities for advancing THz-2DCS instrumentation and experimental strategies towards new frontier discoveries.

Huang, Chuankun [Ames Laboratory (AMES), Ames, IA ↗
DOE OSTI2016

Three-dimensional simulations of turbulent convective mixing in ONe and CO classical nova explosions

Classical novae are thermonuclear explosions that take place in the envelopes of accreting white dwarfs in binary systems. The material piles up under degenerate conditions, driving a thermonuclear runaway. The energy released by the suite of nuclear processes operating at the envelope heats the material up to peak temperatures of ~(1-4) × 10 8 K. During these events, about 10 -3 -10 -7 M ⊙ , enriched in CNO and, sometimes, other intermediate-mass elements (e.g., Ne, Na, Mg, Al) are ejected into the interstellar medium. To account for the gross observational properties of classical novae (in particular, the high concentrations of metals spectroscopically inferred in the ejecta), models require mixing between the (solar-like) material transferred from the secondary and the outermost layers (CO- or ONe-rich) of the underlying white dwarf. Recent multidimensional simulations have demonstrated that Kelvin-Helmholtz instabilities can naturally produce self-enrichment of the accreted envelope with material from the underlying white dwarf at levels that agree with observations. However, the feasibility of this mechanism has been explored in the framework of CO white dwarfs, while mixing with different substrates still needs to be properly addressed. We performed three-dimensional simulations of mixing at the core-envelope interface during nova outbursts with the multidimensional code FLASH, for two types of substrates: CO- and ONe-rich. We also show that the presence of an ONe-rich substrate, as in “neon novae”, yields higher metallicity enhancements in the ejecta than CO-rich substrates (i.e., non-neon novae). Finally, a number of requirements and constraints for such 3D simulations (e.g., minimum resolution, size of the computational domain) are also outlined.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Two-dimensional simulations of mixing in classical novae: The effect of white dwarf composition and mass

Context. Classical novae are explosive phenomena that take place in stellar binary systems. They are powered by mass transfer from a low-mass main sequence star onto either a CO or ONe white dwarf. The material accumulates for 104–105 yr until ignition under degenerate conditions, resulting in a thermonuclear runaway. The nuclear energy released produces peak temperatures of ~0.1–0.4 GK. During these events, 10 –7 –10 –3 M ⊙ enriched in intermediate-mass elements, with respect to solar abundances, are ejected into the interstellar medium. Yet, the origin of the large metallicity enhancements and the inhomogeneous distribution of chemical species observed in high-resolution spectra of ejected nova shells is not fully understood. Aims. Recent multidimensional simulations have demonstrated that Kelvin-Helmholtz instabilities that operate at the core-envelope interface can naturally produce self-enrichment of the accreted envelope with material from the underlying white dwarf at levels that agree with observations. Yet, such multidimensional simulations have been performed for a small number of cases and much of the parameter space remains unexplored. Methods. We explored the dredge-up, driven by Kelvin-Helmholtz instabilities, for white dwarf masses in the range 0.8–1.25 M ⊙ and different core compositions, that is, CO-rich and ONe-rich substrates. We present a set of five numerical simulations performed in two dimensions aimed at analyzing the possible impact of the white dwarf mass, and composition, on the metallicity enhancement and explosion characteristics. Results. At the time we stop the simulations, we observe greater mixing (~30% higher when measured in the same conditions) and more energetic outbursts for ONe-rich substrates than for CO-rich substrates and more massive white dwarfs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Density-functional theory computer simulations of CZTS{sub 0.25}Se{sub 0.75} alloy phase diagrams

Density-functional theory simulations of CZTS, CZTSe, and CZTS{sub 0.25}Se{sub 0.75} photovoltaic compounds have been performed to investigate the stability of the CZTS{sub 0.25}Se{sub 0.75} alloy vs. decomposition into CZTS, CZTSe, and other secondary compounds. The Gibbs energy for vibrational contributions was estimated by calculating phonon spectra and thermodynamic properties at finite temperatures. It was demonstrated that the CZTS{sub 0.25}Se{sub 0.75} alloy is stabilized not by enthalpy of formation but primarily by the mixing contributions to the Gibbs energy. The Gibbs energy gains/losses for several decomposition reactions were calculated as a function of temperature with/without intermixing and vibration contributions to the Gibbs energy. A set of phase diagrams was built in the multidimensional space of chemical potentials at 300 K and 900 K temperatures to demonstrate alloy stability and boundary compounds at various chemical conditions. It demonstrated for CZTS{sub 0.25}Se{sub 0.75} that the chemical potentials for stability differ between typical processing temperature (∼900 K) and operating temperature (300 K). This implies that as cooling progresses, the flux/concentration of S should be increased in MBE growth to maintain the CZTS{sub 0.25}Se{sub 0.75} in a thermodynamically stable state to minimize phase decomposition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Local and transient nanoscale strain mapping during in situ deformation

The mobility of defects such as dislocations controls the mechanical properties of metals. This mobility is determined both by the characteristics of the defect and the material, as well as the local stress and strain applied to the defect. Therefore, the knowledge of the stress and strain during deformation at the scale of defects is important for understanding fundamental deformation mechanisms. In this paper, we demonstrate a method of measuring local stresses and strains during continuous in situ deformation with a resolution of a few nanometers using nanodiffraction strain mapping. Finally, our results demonstrate how large multidimensional data sets captured with high speed electron detectors can be analyzed in multiple ways after an in situ TEM experiment, opening the door for true multimodal analysis from a single electron scattering experiment.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Super-resolution and signal separation in contact Kelvin probe force microscopy of electrochemically active ferroelectric materials

In this work, imaging mechanisms in contact Kelvin probe force microscopy (cKPFM) are explored via information theory-based methods. Gaussian processes are used to achieve super-resolution in the cKPFM signal, effectively extrapolating across the spatial and parameter space. Tensor factorization is applied to reduce the multidimensional signal to the tensor convolution of the scalar functions that show a clear trending behavior with the imaging parameters. These methods establish a workflow for the analysis of the multidimensional datasets that can then be related to the relevant physical mechanisms. We also provide an interactive Google Colab notebook that goes through all the analyses discussed in the paper.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian process-based exploration–exploitation

Statistical physics models ranging from simple lattice to complex quantum Hamiltonians are one of the mainstays of modern physics that have allowed both decades of scientific discovery and provided a universal framework to understand a broad range of phenomena from alloying to frustrated and phase separated materials to quantum systems. Traditionally, exploration of the phase diagrams corresponding to multidimensional parameter spaces of Hamiltonians was performed using a combination of basic physical principles, analytical approximations, and extensive numerical modeling. However, exploration of complex multidimensional parameter spaces is subject to the classic dimensionality problem, and the behaviors of interest concentrated on low dimensional manifolds remain undiscovered. Here, we demonstrate that a combination of exploration and exploration–exploitation with Gaussian process modeling and Bayesian optimization allows effective exploration of the parameter space for lattice Hamiltonians and effectively maps the regions at which specific macroscopic functionalities or local structures are maximized. We argue that this approach is general and can be further extended well beyond the lattice Hamiltonians to effectively explore the parameter space of more complex off-lattice and dynamic models.

42 ENGINEERING↗
DOE OSTI2021

Multi-objective Bayesian optimization of ferroelectric materials with interfacial control for memory and energy storage applications

Optimization of materials’ performance for specific applications often requires balancing multiple aspects of materials’ functionality. Even for the cases where a generative physical model of material behavior is known and reliable, this often requires search over multidimensional function space to identify low-dimensional manifold corresponding to the required Pareto front. In this work, we introduce the multi-objective Bayesian optimization (MOBO) workflow for the ferroelectric/antiferroelectric performance optimization for memory and energy storage applications based on the numerical solution of the Ginzburg–Landau equation with electrochemical or semiconducting boundary conditions. MOBO is a low computational cost optimization tool for expensive multi-objective functions, where we update posterior surrogate Gaussian process models from prior evaluations and then select future evaluations from maximizing an acquisition function. Using the parameters for a prototype bulk antiferroelectric (PbZrO 3 ), we first develop a physics-driven decision tree of target functions from the loop structures. We further develop a physics-driven MOBO architecture to explore multidimensional parameter space and build Pareto-frontiers by maximizing two target functions jointly—energy storage and loss. This approach allows for rapid initial materials and device parameter selection for a given application and can be further expanded toward the active experiment setting. The associated notebooks provide both the tutorial on MOBO and allow us to reproduce the reported analyses and apply them to other systems (https://github.com/arpanbiswas52/MOBO_AFI_Supplements).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Progress toward hydro-equivalent ignition in OMEGA direct-drive DT-layered implosions

Considerable progress has been made in deuterium-tritium-layered implosion experiments on the OMEGA Laser System, bringing the prospects for thermonuclear ignition in direct-drive configurations with megajoule-class lasers closer to reality. Doing so has required navigating the balance between improved 1D performance and multidimensional stability. Using statistical modeling based on over 350 cryogenic implosions to identify various degradation mechanisms, and combined with multidimensional simulations and experimental techniques such as target offsets to combat residual flows, core conditions have repeatably been achieved that extrapolate to the burning-plasma state when scaled to 2.15 MJ of symmetric laser illumination. Using high implosion velocities (⁠> 450 km/s) and moderately high adiabats (⁠~5⁠), these experiments produced record-high scaled Lawson parameters in direct drive equal to 89 ± 2% of that required for ignition with expected yields of up to 1.5 ± 0.2 MJ. To improve these results still further, focused physics studies are performed to improve physics understanding and identify routes to even greater performance. Recent studies include investigations into the impact of mounting features, laser imprint, reduced fuel temperatures, and greater on-target intensities through subscale experiments. This manuscript gives a summary of the cryogenic direct-drive program on the OMEGA laser, including routes taken to achieve the current best performance, the status of recent focused physics investigations, and future designs—such as target solutions to laser imprint and reducing vapor density to increase convergence—that are expected lead to the demonstration of hydro-equivalent ignition on OMEGA.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Imaging nanoscale lattice variations by machine learning of x-ray diffraction microscopy data

In this paper, we present a novel methodology based on machine learning to extract lattice variations in crystalline materials, at the nanoscale, from an x-ray Bragg diffraction-based imaging technique. By employing a full-field microscopy setup, we capture real space images of materials, with imaging contrast determined solely by the x-ray diffracted signal. The data sets that emanate from this imaging technique are a hybrid of real space information (image spatial support) and reciprocal lattice space information (image contrast), and are intrinsically multidimensional (5D). By a judicious application of established unsupervised machine learning techniques and multivariate analysis to this multidimensional data cube, we show how to extract features that can be ascribed physical interpretations in terms of common structural distortions, such as lattice tilts and dislocation arrays. Finally, we demonstrate this 'big data' approach to x-ray diffraction microscopy by identifying structural defects present in an epitaxial ferroelectric thin-film of lead zirconate titanate.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Principles of two-dimensional terahertz spectroscopy of collective excitations: The case of Josephson plasmons in layered superconductors

Two-dimensional terahertz spectroscopy (2DTS), a terahertz analog of nuclear magnetic resonance, is a new technique poised to address many open questions in complex condensed matter systems. The conventional theoretical framework used ubiquitously for interpreting multidimensional spectra of discrete quantum level systems is, however, insufficient for the continua of collective excitations in strongly correlated materials. Here, in this work, we develop a theory for 2DTS of a model collective excitation, the Josephson plasma resonance in layered superconductors. Starting from a mean-field approach at temperatures well below the superconducting phase transition, we obtain expressions for the multidimensional nonlinear responses that are amenable to intuition derived from the conventional single-mode scenario. We then consider temperatures near the superconducting critical temperature T c , where dynamics beyond mean-field become important and conventional intuition fails. As fluctuations proliferate near T c , the dominant contribution to nonlinear response comes from an optical parametric drive of counterpropagating Josephson plasmons, which gives rise to 2D spectra that are qualitatively different from the mean-field predictions. As such, and in contrast to one-dimensional spectroscopy techniques, such as third harmonic generation, 2DTS can be used to directly probe thermally excited finite-momentum plasmons and their interactions. Our theory can readily be tested in cuprates, and we discuss implications beyond the present context of Josephson plasmons.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Measuring scale-dependent shape anisotropy by coarse-graining: Application to inhomogeneous Rayleigh-Taylor turbulence

Here, we generalize the “filtering spectrum” to probe scales along different directions by spatial coarse-graining. This multidimensional filtering spectrum quantifies the spectral content of flows that are not necessarily homogeneous. From multidimensional spectral information, we propose a simple metric for shape anisotropy at various scales. The method is applied to simulations of two-dimensional (2D) and 3D Rayleigh-Taylor (RT) turbulence, which is inhomogeneous and anisotropic. We show that 3D RT has clear shape anisotropy at large scales with approximately 4:3 vertical to horizontal aspect ratio but tends toward isotropy at small scales as expected. In sharp contrast, we find that RT in 2D simulations, which are still the main modeling framework for many applications, is isotropic at large scales and its shape anisotropy increases at smaller scales where structures tend to be horizontally elongated. While this may be surprising, it is consistent with recent results that large-scale isotropy in 2D RT is due to the generation of a large-scale overturning circulation via an upscale cascade, while small-scale anisotropy is due to the stable stratification resultant from such overturning and the inefficient mixing in 2D.

42 ENGINEERING↗
DOE OSTI2017

Statistical projections for multi-dimensional visual data exploration

When working with large, multidimensional and multivariate data, science users are frequently interested in understanding variation in data, as opposed to the actual data values. Our work focuses on exploring how a simple statistical metric, the Coefficient of Variation (or Cv), can be used in several different ways to facilitate understanding variation in large data. As a statistical measure, it offers a key advantage over more widely accepted measures like standard deviation, namely to its ability to capture local variation properties. As a multidimensional projection operator, Cv is an effective way of reducing data size while preserving the key variational signal. Visualizations produced from Cv that target conveying variation in data are highly informative, especially compared to those produced with more widely known methods. We demonstrate these ideas within the context of a two-part application case study focusing on understanding long-Term trends in the the changes in precipitation and winds in large-scale climate model ensemble output.

Nguyen, H↗
DOE OSTI2021

A Computational Study of the Thermodynamic Conditions Leading to Autoignition in Nanosecond Pulsed Discharges

Nanosecond pulsed discharges have attracted the attention of engine manufacturers due to the possibility of attaining distributed ignition sites that accelerate burn rates while resulting in very little electrode erosion. Multidimensional modeling tools currently capture the electrical structure of such discharges accurately, but resolving the chemical structure remains a challenging problem owing to the disparity of time-scales in streamer propagation (nanoseconds) and ignition phenomena (microseconds). The purpose of this study is to extend multidimensional results toward resolving the chemical structure in the wake of streamers (or the afterglow) by using a batch reactor model (BRM). This can afford the use of very detailed chemical kinetic information. The full nonequilibrium nature of the electrons is taken into account, along with fast gas heating, shock wave propagation, and thermal diffusion. The results shed light on ignition phenomena brought about by such discharges.

42 ENGINEERING↗
DOE OSTI2020

A computational study of the thermodynamic conditions leading to autoignition in nanosecond pulsed discharges

Nanosecond pulsed discharges have attracted the attention of engine manufacturers due to the possibility of attaining distributed ignition sites that accelerate burn rates while resulting in very little electrode erosion. Multidimensional modeling tools currently capture the electrical structure of such discharges accurately, but resolving the chemical structure remains a challenging problem owing to the disparity of time-scales in streamer propagation (nanoseconds) and ignition phenomena (microseconds). The purpose of this study is to extend multidimensional results towards resolving the chemical structure in the wake of streamers (or the afterglow) by using a batch reactor model. This can afford the use of very detailed chemical kinetic information. The full non-equilibrium nature of the electrons is taken into account, along with fast gas heating, shock wave propagation, and thermal diffusion. The results shed light on ignition phenomena brought about by such discharges.

ignition, plasma, kinetics↗
DOE OSTI2019

fPINNs: Fractional Physics-Informed Neural Networks

Physics-informed neural networks (PINNs), introduced, are effective in solving integer-order partial differential equations (PDEs) based on scattered and noisy data. PINNs employ standard feedforward neural networks (NNs) with the PDEs explicitly encoded into the NN using automatic differentiation, while the sum of the mean-squared PDE residuals and the mean-squared error in initial-boundary conditions is minimized with respect to the NN parameters. Here we extend PINNs to fractional PINNs (fPINNs) to solve space-time fractional advection-diffusion equations (fractional ADEs), and we study systematically their convergence, hence explaining both fPINNs and PINNs for the first time. Specifically, we demonstrate their accuracy and effectiveness in solving multidimensional forward and inverse problems with forcing terms whose values are only known at randomly scattered spatio-temporal coordinates (black-box (BB) forcing terms). A novel element of the fPINNs is the hybrid approach that we introduce for constructing the residual in the loss function using both automatic differentiation for the integer-order operators and numerical discretization for the fractional operators. This approach bypasses the difficulties stemming from the fact that automatic differentiation is not applicable to fractional operators because the standard chain rule in integer calculus is not valid in fractional calculus. To discretize the fractional operators, we employ the Grünwald--Letnikov (GL) formula in one-dimensional fractional ADEs and the vector GL formula in conjunction with the directional fractional Laplacian in two- and three-dimensional fractional ADEs. We first consider the one-dimensional fractional Poisson equation and compare the convergence of the fPINNs against the finite difference method (FDM). We present the solution convergence using both the mean L 2 error as well as the standard deviation due to sensitivity to NN parameter initializations. Using different GL formulas we observe first-, second-, and third-order convergence rates for small size training sets but the error saturates for larger training sets. We explain these results by analyzing the four sources of numerical errors due to discretization, sampling, NN approximation, and optimization. The total error decays monotonically (below 10 –5 for a third-order GL formula) but it saturates beyond that point due to the optimization error. We also analyze the relative balance between discretization and sampling errors and observe that the sampling size and the number of discretization points (auxiliary points) should be comparable to achieve the highest accuracy. As we increase the depth of the NN up to certain value, the mean error decreases and the standard deviation increases, whereas the width has essentially no effect unless its value is either too small or too large. We next consider time-dependent fractional ADEs and compare white-box (WB) and BB forcing. We observe that for the WB forcing, our results are similar to the aforementioned cases; however, for the BB forcing fPINNs outperform FDM. Subsequently, we consider multidimensional time-, space-, and space-time-fractional ADEs using the directional fractional Laplacian and we observe relative errors of 10 –3 ~ 10 –4 . Lastly, we solve several inverse problems in one, two, and three dimensions to identify the fractional orders, diffusion coefficients, and transport velocities and obtain accurate results given proper initializations even in the presence of significant noise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Inverse problem: What can we tell about the matter and energy in our Universe from its dimensionality and evolution

The most well-known theories of everything are assorted versions of the superstring theory. The theories require existence of additional space dimensions, vibrations of which create the material particles in 3D space. The additional space dimensions are understood as being currently smaller than the Planck Length and due to this not directly observable. We search for multidimensional models of the Universe (one time dimension; three isotropic, flat external dimensions, and n-internal dimensions), which satisfy the multidimensional Einstein equations and which started from the same radius of all of the internal and external dimensions, with an anisotropic energy–momentum tensor. Analytical solution of (N+1)-dimensional Einstein equation in a reparameterized time is reminded and discussed. The energy–momentum tensor is solely responsible for expansion of the external dimensions and shrinking of the internal ones; and to obtain this behavior of the space the tensor needs to fulfill some conditions i.e. the energy–momentum tensor cannot include only radiation, vacuum and baryonic matter. For the behavior of the physical space consistent with the one observed in our Universe, the dark energy and/or dark matter have to exist.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗