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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Improved coarse-graining methods for two dimensional tensor networks including fermions

We show how to apply renormalization group algorithms incorporating entanglement filtering methods and a loop optimization to a tensor network which includes Grassmann variables which represent fermions in an underlying lattice field theory. As a numerical test a variety of quantities are calculated for two dimensional Wilson-Majorana fermions and for the two flavor Gross-Neveu model. The improved algorithms show much better accuracy for quantities such as the free energy and the determination of Fisher’s zeros.

Phase transitions↗
DOE OSTI2023

Polarization-independent photonic Bragg grating filter with cladding asymmetry

A photonic Bragg grating is a fundamental building block that reflects the direction of wave propagation through spatial phase modulation and can be implemented using sidewall corrugation. However, due to the asymmetric aspect ratio of a waveguide cross section, typical Bragg gratings exhibit a strong polarization sensitivity. Here, we show that photonic Bragg gratings with cladding asymmetry can enable polarization-independent notch filters by rotating input polarizations. Such Bragg gratings strongly couple transverse electric (TE) and transverse magnetic (TM) modes propagating in opposite directions, filtering the input signal and reflecting the rotated mode. We analyzed this polarization-rotating Bragg grating using the coupled-mode theory and experimentally demonstrated it on a silicon-on-insulator platform. Our device concept is simple to implement and compatible with other platforms, readily available as polarization transparent Bragg components.

Pimbi, Daniel (ORCID:000000025933203X)↗
DOE OSTI2023

Convex Q-Learning in Continuous Time with Application to Dispatch of Distributed Energy Resources

Convex Q-learning is a recent approach to reinforcement learning, motivated by the possibility of a firmer theory for convergence, and the possibility of making use of greater a priori knowledge regarding policy or value function structure. This paper explores algorithm design in the continuous time domain, with a finite-horizon optimal control objective. The main contributions are (i) The new Q-ODE: a model-free characterization of the Hamilton-Jacobi-Bellman equation. (ii) A formulation of Convex Q-learning that avoids approximations appearing in prior work. The Bellman error used in the algorithm is defined by filtered measurements, which is necessary in the presence of measurement noise. (iii) Convex Q-learning with linear function approximation is a convex program. It is shown that the constraint region is bounded, subject to an exploration condition on the training input. (iv) The theory is illustrated in application to resource allocation for distributed energy resources, for which the theory is ideally suited.

Lu, Fan↗
DOE OSTI2024

Estimating scalar turbulent fluxes with slow-response sensors in the stable atmospheric boundary layer

Conventional and recently developed approaches for estimating turbulent scalar fluxes under stable atmospheric conditions are evaluated, with a focus on gases for which fast sensors are not readily available. First, the relaxed eddy accumulation (REA) classical approach and a recently proposed mixing length parameterization, labeled A22, are tested against eddy-covariance computations. Using high-frequency measurements collected from two contrasting sites (the frozen tundra near Utqiaġvik, Alaska, and a sparsely vegetated grassland in Wendell, Idaho, during winter), it is shown that the REA and A22 models outperform the conventional Monin–Obukhov similarity theory (MOST) utilized widely to infer fluxes from mean gradients. Second, scenarios where slow trace gas sensors are the only viable option in field measurements are investigated using digital filtering applied to fast-response sensors to simulate their slow-response counterparts. With a filtered scalar signal, the observed filtered eddy-covariance fluxes are referred to here as large-eddy-covariance (LEC) fluxes. A virtual eddy accumulation (VEA) approach, akin to the REA model but not requiring a mechanical apparatus to separate the gas flows, is also formulated and tested. A22 outperforms VEA and LEC in predicting the observed unfiltered (total) eddy-covariance (EC) fluxes; however, VEA can still capture the LEC fluxes well. This finding motivates the introduction of a sensor response time correction into the VEA formulation to offset the effect of sensor filtering on the underestimated net averaged fluxes. The only needed parameter for this correction is the mean velocity at the instrument height, a surrogate of the advective timescale. The VEA approach is very suitable and simple to use with gas sensors of intermediate speed (∼ 0.5 to 1 Hz) and with conventional open- or closed-path setups.

58 GEOSCIENCES↗
DOE OSTI2025

Field-level reconstruction from foreground-contaminated 21-cm maps

Current and upcoming 21-cm experiments will soon be able to map 21-cm spatial fluctuations in three dimensions for a wide range of redshifts. However, bright foreground contamination and the nature of radio interferometry create significant challenges, making it difficult to access rich cosmological information from the Fourier modes that lie within the “foreground wedge”. Here, in this work, we introduce two approaches aiming to reconstruct the full 21-cm density field, including the missing modes in the wedge: (a) a field-level inference under an effective field theory (EFT) framework; (b) a diffusion-based deep generative model trained on simulations. Under the EFT framework, we implement a fully differentiable forward model that maps the initial conditions of matter fluctuations to the observed, foreground-filtered 21-cm maps. This enables a gradient-based sampler to simultaneously sample the initial conditions and bias parameters, allowing a physically motivated mode reconstruction. Alternatively, we apply a variational diffusion model to perform 21-cm density reconstruction at the map level. Our model is trained on semi-numerical simulations over a wide range of astrophysical parameters. Our results from both approaches should provide improved cosmological constraints from the field level and also enable cross-correlation between experiments that have little or no overlapping modes.

cosmological perturbation theory↗
DOE OSTI2023

Solving the electronic structure problem for over 100000 atoms in real space

Using a real-space high-order finite-difference approach, we investigate the electronic structure of large spherical silicon nanoclusters. Within Kohn-Sham density functional theory and using pseudopotentials, we report the self-consistent field convergence of a system with over 100000 atoms: a Si 107,641 ⁢H 9,084 nanocluster with a diameter of 16 nm. Our approach uses Chebyshev-filtered subspace iteration to speed up the convergence of the eigenspace, and blockwise Hilbert space-filling curves to speed up sparse matrix-vector multiplications, all of which are implemented in the parsec code. For the largest system, we utilized 2048 nodes (114 688 cores) on the Frontera machine in the Texas Advanced Computing Center. Our quantitative analysis of the electronic structure shows how it gradually approaches its bulk counterpart as a function of nanocluster size. The band gap is enlarged due to quantum confinement in nanoclusters, but decreases as the system size increases, as expected. In conclusion, our work serves as a proof of concept for the capacity of the real-space approach in efficiently parallelizing very large calculations using high-performance computer platforms, which can straightforwardly be replicated in other systems with more than 10 5 atoms.

0-dimensional systems↗
DOE OSTI2022

Dynamic signal recovery in distribution grids using compressive lossy measurements

Distribution system state estimation requires reliable aggregation of the measured data. However, the large volume of the measured data imposes a significant stress on the underlying communication infrastructure. With the challenges associated with measurement availability, current distribution systems are typically unobservable. To cope with the unobservability issue, compressive sensing theory allows us to recover system state information from a small number of measurements provided the states of the distribution system exhibit sparsity. In this paper, we evaluate the robustness of an updated Kalman filtered modified compressive sensing (KF-ModCS) technique that dynamically estimates the grid states using a small fraction of measured data. In practice, measurements used for sparsity based state estimation may also be intermittent due to communication network induced losses. Further, to understand the effect of packet losses on KF-ModCS, we provide an upper bound for the expected variances of the state estimation error for a given rate of information loss. This upper bound is further improved if the support set of the sparse signal that characterizes the state dynamics does not change over time and/or the reduced model is observable. Simulations based on two practical data sets collected from actual customers in a distribution grid validate the theoretical results.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Chiral induction at the nanoscale and spin selectivity in electron transmission in chiral methylated BEDT-TTF derivatives

Great efforts have been made in the last few decades to realize electronic devices based on organic molecules. A possible approach in this field is to exploit the chirality of organic molecules for the development of spintronic devices, an applicative way to implement the chiral-induced spin selectivity (CISS) effect. In this work we exploit enantiopure tetrathiafulvalene (TTF) derivatives as chiral inducers at the nanoscale. The aim is to make use of TTF's well-known and unique semiconducting properties, to be expressed in the fields of enantio-selectivity and the chiral-induced spin selectivity (CISS) effect. Further, the experimental results shown in this paper further demonstrate how chirality and spin are deeply interrelated, as foreseen within the CISS effect theory, paving the way for the application of TTF derivatives in the field of spintronics. In this work, we demonstrate that tetramethyl-bis(ethylenedithio)-tetrathiafulvalene (TM-BEDT-TTF) (1) behaves as an efficient spin filter, as evidenced by magneto-atomic force microscopy (mc-AFM) measurements. Additionally, it is shown to be effective in transferring chirality to CdS/CdSe core–shell nanoparticles, as inferred from the analysis of circularly resolved photoluminescence spectra. This makes 1 a promising candidate for a variety of applications, ranging from plasmonics to quantum computing.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Probing two-path electron quantum interference in strong-field ionization with time-correlation filtering

We report attosecond dynamics in strong-field tunnel ionization are encoded in intricate holographic patterns in the photoelectron momentum distributions. These patterns show the interference between two or more superposed quantum electron trajectories, which are defined by their ionization times and subsequent evolution in the laser field. We determine the ionization time separation between interfering pairs of electron orbits by performing a differential Fourier analysis on the measured momentum spectrum. We identify electron holograms formed by trajectory pairs whose ionization times are separated by less than a single quarter cycle, between a quarter cycle and half cycle, between a half cycle and three fourths of a cycle, and a full cycle apart. We compare our experimental results to the predictions of the Coulomb quantum orbit strong-field approximation (CQSFA) with significant success. We also time-filter the CQSFA trajectory calculations to demonstrate the validity of the technique on spectra with known time correlations. As a general analysis technique, the filter can be applied to all energy- and angularly resolved data sets to recover time correlations between interfering electron pathways, providing an important tool to analyze any strong-field ionization spectra. Moreover, it is independent of theory and can be applied directly to experiments, without the need of a direct comparison with orbit-based theoretical methods.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Filtering states with total spin on a quantum computer

Starting from a general wave-function described on a set of spins or qubits, we propose several quantum algorithms to extract the components of this state on eigenstates of total spin S 2 and its z component S z . The method plays the role of total spin projection and gives access to the amplitudes of the initial state on a total spin basis. Here, the different algorithms have various degrees of sophistication depending on the requested tasks. They can either solely project onto the subspace with good total spin or completely uplift the degeneracy in this subspace. In the former case, when the projection on the total spin j is made, we show that the number of operations for the projection can be reduced from a quadratic to a linear dependence in j. After each measurement, the state collapses to one of the spin eigenstates that could be used for postprocessing. For this reason, we call the method total quantum spin filtering.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

NO 2 Interactions with MoO 3 and CuO at Atmospherically Relevant Pressures

NO x concentrations in some geographic regions are harmful to human health. Gas filters to trap NO x and other toxic chemicals contain metal oxides, including MoO 3 and CuO. These materials are also being investigated for NO x gas sensors. In a step to understand the fundamental adsorption mechanism in sensors and the effect on binding site availability in gas filters, ambient-pressure X-ray photoelectron spectroscopy (APXPS) was used to study the interaction of NO 2 with polycrystalline MoO 3 and CuO surfaces under pressures up to 0.01 Torr (14 parts per million volume (ppmv)). Density functional theory-based computational modeling was performed to reveal the mechanisms of NO 2 interactions with the MoO 3 (010) and CuO(111) surfaces to aid interpretation of the experimental results. With pressure dependence, NO 2 interacts with reduced Mo 5+ atoms generated by oxygen vacancies and abstracts hydrogen atoms from hydroxyl groups on MoO 3 without accumulating N-containing species on the surface; vacancy-induced electronic states in the band gap are also removed, hinting toward an increase in the resistivity of the material. N-containing species begin accumulating on the CuO surface at atmospherically relevant pressures of 140 ppbv. NO 2 only decomposes at oxygen vacancy sites of CuO. The nitrogen species leave the CuO surface upon evacuation, highlighting the importance of in situ surface characterization when studying gas sensing and adsorption mechanisms. Finally, these results imply that NO 2 removes hydroxyl and O vac binding sties on these materials when used in gas filtration and sensing applications. Furthermore, the results show the key role of O vac sites in the gas sensing mechanism of MoO 3 and highlight the potential of APXPS for further studies of gas sensors.

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

Accelerating self-consistent field iterations in Kohn-Sham density functional theory using a low-rank approximation of the dielectric matrix

We present an efficient preconditioning technique for accelerating the fixed-point iteration in real-space Kohn-Sham density functional theory (DFT) calculations. The preconditioner uses a low-rank approximation of the dielectric matrix (LRDM) based on Gâteaux derivatives of the residual of fixed-point iteration along appropriately chosen direction functions. We develop a computationally efficient method to evaluate these Gâteaux derivatives in conjunction with the Chebyshev filtered subspace iteration procedure, an approach widely used in large-scale Kohn-Sham DFT calculations. Further, we propose a variant of LRDM preconditioner based on adaptive accumulation of low-rank approximations from previous self-consistent field iterations, and also extend the LRDM preconditioner to spin-polarized Kohn-Sham DFT calculations. We demonstrate the robustness and efficiency of the LRDM preconditioner against other widely used preconditioners on a range of benchmark systems with sizes ranging from ~100 to 1100 atoms (~500–20,000 electrons). The benchmark systems include various combinations of metal-insulating-semiconducting heterogeneous material systems, nanoparticles with localized d orbitals near the Fermi energy, nanofilm with metal dopants, and magnetic systems. In all benchmark systems, the LRDM preconditioner converges robustly within 20–30 iterations. In contrast, other widely used preconditioners show slow convergence in many cases, as well as divergence of the fixed-point iteration in some cases. Lastly, we demonstrate the computational efficiency afforded by the LRDM method, with up to 3.4-fold reduction in computational cost for the total ground-state calculation compared to other preconditioners.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Enhancing operational safety: The NSTX-U Shorted Turn Protection (STP) system

The NSTX-U Shorted Turn Protection (STP) system is a vital safety feature designed to protect the tokamak’s coils during operations. It employs a Kalman filter-based algorithm to monitor coil currents and detect faults in real-time. If an anomaly is detected, the system swiftly terminates the tokamak pulse to prevent damage. Developed with Matlab and Simulink, the STP system has been validated through real-time simulations, ensuring its effectiveness for the upcoming upgrades of the NSTX-U facility. In conclusion, this article presents the theory for implementation of the STP algorithm and the results from simulation and real-time implementation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model

Multi-principal element alloys (MPEAs) continue to gain research prominence due to their promising high-temperature microstructural and mechanical properties. Recently, machine learning (ML) and materials informatics have been used extensively for screening MPEAs, however, most of these efforts were focused on constructing classification and regression models for predicting phase stability and mechanical properties of known compositions. These approaches may accelerate the screening process but optimizing new compositions with desirable properties within a practical time frame from an infinitely large design space of MPEA systems remains a grand challenge. To tackle this composition optimization challenge, a generative adversarial network coupled with a neural-network ML model was utilized to design MPEAs by filtering compositions that have high hardness. Even in a high-dimensional space with 18 elements as descriptors, the ML model was able to generate optimized compositions from which one composition was found to have 10% higher hardness (941 HV) than the maximum in the training data (857 HV). Density-functional theory was used to provide thermodynamic and electronic insights to higher hardness of the new MPEA found. The present work can optimize compositions from a wide design space of 18 elements (including W, Ta and Nb) that presents an opportunity to synthesize new compositions for applications ranging from corrosion-resistant alloys to nuclear materials. Here the findings suggest that generative ML can greatly accelerate materials discovery by identifying novel compositions, which can serve as a data-informed tool to guide experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Non-perturbative Approach to Computing Seismic Normal Modes in Rotating Planets

In this work, a continuous Galerkin method based approach is presented to compute the seismic normal modes of rotating planets. Special care is taken to separate out the essential spectrum in the presence of a fluid outer core using a polynomial filtering eigensolver. The relevant elastic-gravitational system of equations, including the Coriolis force, is subjected to a mixed finite-element method, while self-gravitation is accounted for with the fast multipole method. Our discretization utilizes fully unstructured tetrahedral meshes for both solid and fluid regions. The relevant eigenvalue problem is solved by a combination of several highly parallel and computationally efficient methods. We validate our three-dimensional results in the non-rotating case using analytical results for constant elastic balls, as well as numerical results for an isotropic Earth model from standard “radial” algorithms. We also validate the computations in the rotating case, but only in the slowly-rotating regime where perturbation theory applies, because no other independent algorithms are available in the general case. The algorithm and code are used to compute the point spectra of eigenfrequencies in several Earth and Mars models studying the effects of heterogeneity on a large range of scales.

58 GEOSCIENCES↗
DOE OSTI2021

Comment on ‘Carbon Intensity of corn ethanol in the United States: state of the science’

Abstract In their recent contribution, Scully et al (2021 Environ. Res. Lett. 16 043001) review and revise past life cycle assessments of corn-grain ethanol’s carbon (C) intensity to suggest that a current ‘central best estimate’ is considerably less than all prior estimates. Their conclusion emerges from selection and recombination of sector-specific greenhouse gas emission predictions from disparate studies in a way that disproportionately favors small values and optimistic assumptions without rigorous justification nor empirical support. Their revisions most profoundly reduce predicted land use change (LUC) emissions, for which they propose a central estimate that is roughly half the smallest comparable value they review (figure 1). This LUC estimate represents the midpoint of (a) values retained after filtering the predictions of past studies based on a set of unfounded criteria; and (b) a new estimate they generate for domestic (i.e. U.S.) LUC emissions. The filter the authors apply endorses a singular means of LUC assessment which they assert as the ‘best practice’ despite a recent unacknowledged review (Malins et al 2020 J. Clean. Prod. 258 120716) that shows this method almost certainly underestimates LUC. Moreover, their domestic C intensity estimate surprisingly suggests that cropland expansion newly sequesters soil C, counter to ecological theory and empirical evidence. These issues, among others, prove to grossly underestimate the C intensity of corn-grain ethanol and mischaracterize the state of our science at the risk of perversely affecting policy outcomes.

Spawn-Lee, Seth A. (ORCID:0000000188215345)↗
DOE OSTI2023

EUTERPE: A global gyrokinetic code for stellarator geometry

The current state of the EUTERPE code is described with emphasis on the implemented models and their numerical implementation. The code solves the multi-species electromagnetic gyrokinetic equations in the full volume of a three-dimensional domain. Noise reduction of the particle-in-cell method is achieved by using a δf-method and Fourier filters. The field equations are discretized with B-splines and the resulting system of equations is solved iteratively. For linear simulations a phase-factor transformation is applied in order to strongly reduce the necessary grid resolution. Apart from the full gyrokinetic model, other numerically less expensive hybrid models are also implemented. They are mainly tailored for comparison with fluid theory and for studying the interaction of the bulk plasma with fast particles. The code is parallelized for CPUs by particle and domain decomposition. Good scalability up to several thousand nodes is demonstrated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Fast and robust all-electron density functional theory calculations in solids using orthogonalized enriched finite elements

Here, we present a computationally efficient approach to perform systematically convergent real-space all-electron Kohn-Sham density functional theory calculations for solids using an enriched finite element (FE) basis. The enriched FE basis is constructed by augmenting the classical FE basis with atom-centered numerical basis functions, comprising of atomic solutions to the Kohn-Sham problem. Notably, to improve the conditioning, we orthogonalize the enrichment functions with respect to the classical FE basis, without sacrificing the locality of the resultant basis. In addition to improved conditioning, this orthogonalization procedure also renders the overlap matrix block diagonal, greatly simplifying its inversion. Subsequently, we use a Chebyshev polynomial based filtering technique to efficiently compute the occupied eigenspace in each self-consistent field iteration. We demonstrate the accuracy and efficiency of the proposed approach on periodic unit cells and supercells. The benchmark studies show a staggering 130× speedup of the orthogonalized enriched FE basis over the classical FE basis. We also present a comparison of the orthogonalized enriched FE basis with the linearized augmented plane-wave + local orbitals basis, both in terms of accuracy and efficiency. Notably, we demonstrate that the orthogonalized enriched FE basis can handle large system sizes of ~10 000 electrons. Finally, we observe good parallel scalability of our implementation with 92% efficiency at 22× speedup for a system with 620 electrons

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗