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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

SO(3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials

This work examines the frame-invariance (and the lack thereof) exhibited in simulated anisotropic elasto-plastic responses generated from supervised machine learning of classical multi-layer and informed-graph-based neural networks, and proposes different remedies to fix this drawback. The inherent hierarchical relations among physical quantities and state variables in an elasto-plasticity model are first represented as informed, directed graphs, where three variations of the graph are tested. While feed-forward neural networks are used to train path-independent constitutive relations (e.g., elasticity), recurrent neural networks are used to replicate responses that depends on the deformation history, i.e. or path dependent. In dealing with the objectivity deficiency, we use the spectral form to represent tensors and, subsequently, three metrics, the Euclidean distance between the Euler Angles, the distance from the identity matrix, and geodesic on the unit sphere in Lie algebra, can be employed to constitute objective functions for the supervised machine learning. In this, the aim is to minimize the measured distance between the true and the predicted 3D rotation entities. Following this, we conduct numerical experiments on how these metrics, which are theoretically equivalent, may lead to differences in the efficiency of the supervised machine learning as well as the accuracy and robustness of the resultant models. Neural network models trained with tensors represented in component form for a given Cartesian coordinate system are used as a benchmark. Our numerical tests show that, even given the same amount of information and data, the quality of the anisotropic elasto-plasticity model is highly sensitive to the way tensors are represented and measured. The results reveal that using a loss function based on geodesic on the unit sphere in Lie algebra together with an informed, directed graph yield significantly more accurate rotation prediction than the other tested approaches.

42 ENGINEERING↗
DOE OSTI2021

Errors introduced in fission neutron spectrum measurements using a single reference

Measurements of physical quantities are often made relative to a reference quantity, simplifying the interpretation of the measurement and its uncertainties. However, if applied without careful considerations, this technique can carry with it a significant and complex systematic error that is usually ignored. For example, measurements of continuous neutron spectra relative to a reference spectrum yield a result that does not properly include the important effects of neutron scattering in the experimental environment. These effects act to distort the measured spectrum shape and create notable errors in spectrum-integrated quantities. While this effect is one of a series of known potential sources of systematic error including, but not limited to target impurities, angular distributions, incident beam flux, and others, the error introduced from differences in environmental scattering effects between the reference and desired spectra are frequently overlooked. In this work, we demonstrate the origin of this error using simulated data corresponding to measurements of the neutron-induced prompt fission neutron spectra of 233 U, 235 U, 238 U, and 239 Pu as well as the spontaneous fission neutron spectrum of 252 Cf. Errors in the spectrum shape can be as high as 10%–15% depending on the combination of incident and outgoing neutron energy. The spectrum integral and average spectrum energy also contain errors that change with incident neutron energy and can be as high as ~2%. Although this study is based on simulated data, we also show a method for proving that this error exists in an experimental data set and we show a correction that can be applied to results of this type to obtain a more accurate result, thereby largely removing systematic errors from environmental scattering effects. The potential for these kinds of errors should be considered in measurements producing continuous neutron spectra.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

The upgraded summing NaI(Tl) (SuN++) absorption spectrometer

Simulations of astrophysical processes require a plethora of nuclear physics input. In particular, models of neutron-capture nucleosynthesis like the s, i, and r processes require β-decay information and experimentally constrained neutron-capture reaction rates. Past experiments with the 4π Summing NaI(Tl) (SuN) total absorption spectrometer have provided these physics quantities. Here, we outline an upgrade of SuN to SuN++, where 20 new segments (12 NaI(Tl) and 8 CeBr 3 ) have been integrated into the pre-existing SuN total absorption spectrometer to provide increased energy and time resolution in β-decay experiments. The details of the newly upgraded SuN++ total absorption spectrometer are discussed with results from the commissioning experiment at the Facility for Rare Isotope Beams (FRIB) utilizing a 70 Cu beam.

CeBr3↗
DOE OSTI2025

A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials

Atomic disorder can strongly influence material properties such as charge transport, optical response, and catalytic activity. However, efficiently modeling these disorder effects remains challenging for first-principles methods due to the cost of sampling large configurational spaces and computing complex physical quantities. Recent advances of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we present a general machine-learning-assisted computational framework that integrates equivariant GNNs with Monte Carlo simulations to compute the thermodynamic and ensemble-averaged functional properties of disordered materials. Using the surface-termination-disordered MXene monolayer Ti 3 C 2 T 2–x as a representative system, we find that electrical conductivity exhibits an emergent peak near the order–disorder phase transition temperature due to the interplay between electron scattering and doping. In contrast, optical conductivity remains largely insensitive to local atomic disorder and reflects the global surface chemical composition. These results highlight the role of atomic disorder in affecting material properties and demonstrate the potential of our approach for statistically modeling disorder effects in a wide range of materials such as high-entropy alloys and spin liquids.

MXene↗
DOE OSTI2026

Models and Algorithms for Equilibrium Analysis of Mixed-Material Nucleic Acid Systems

Dynamic programming algorithms within the NUPACK software suite enable analysis of equilibrium base-pairing properties for complex and test tube ensembles containing arbitrary numbers of interacting nucleic acid strands. Currently, calculations are limited to single-material systems that are either all-RNA or all-DNA. Here, to enable analysis of mixed-material systems that are critical for modern applications in vitro, in situ, and in vivo, we develop physical models and dynamic programming algorithms that allow the material of the system to be specified at nucleotide resolution. Free energy parameter sets are constructed for both RNA/DNA and RNA/2'OMe-RNA mixed-material systems by combining available empirical mixed-material parameters with single-material parameter sets to enable treatment of the full complex and test tube ensembles. New dynamic programming recursions account for the material of each nucleotide throughout the recursive process. For a complex with N nucleotides, the mixed-material dynamic programming algorithms maintain the O(N 3 ) time complexity of the single-material algorithms, enabling efficient calculation of diverse physical quantities over complex and test tube ensembles (e.g., complex partition function, equilibrium complex concentrations, equilibrium base-pairing probabilities, minimum free energy secondary structure(s), and Boltzmann-sampled secondary structures) at a cost increase of roughly 2.0-3.5×. The results of existing single-material algorithms are exactly reproduced when applying the new mixed-material algorithms to single-material systems. Accuracy is significantly enhanced using mixed-material models and algorithms to predict RNA/DNA and RNA/2'OMe-RNA duplex melting temperatures from the experimental literature as well as RNA/DNA melt profiles from new experiments. In conclusion, mixed-material analyses can be performed online using the NUPACK web app (www.nupack.org) or locally using the NUPACK Python module.

2′OMe-RNA↗
DOE OSTI2023

Enhanced active-site electric field accelerates enzyme catalysis

In this study, the design and improvement of enzymes based on physical principles remain challenging. Here we demonstrate that the principle of electrostatic catalysis can be leveraged to substantially improve a natural enzyme’s activity. We enhanced the active-site electric field in horse liver alcohol dehydrogenase by replacing the serine hydrogen-bond donor with threonine and replacing the catalytic Zn 2+ with Co 2+ . Based on the electric field enhancement, we make a quantitative prediction of rate acceleration—50-fold faster than the wild-type enzyme—which was in close agreement with experimental measurements. The effects of the hydrogen bonding and metal coordination, two distinct chemical forces, are described by a unified physical quantity—electric field, which is quantitative, and shown here to be additive and predictive. These results suggest a new design paradigm for both biological and non-biological catalysts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Cost function for low-dimensional manifold topology assessment

Abstract In reduced-order modeling, complex systems that exhibit high state-space dimensionality are described and evolved using a small number of parameters. These parameters can be obtained in a data-driven way, where a high-dimensional dataset is projected onto a lower-dimensional basis. A complex system is then restricted to states on a low-dimensional manifold where it can be efficiently modeled. While this approach brings computational benefits, obtaining a good quality of the manifold topology becomes a crucial aspect when models, such as nonlinear regression, are built on top of the manifold. Here, we present a quantitative metric for characterizing manifold topologies. Our metric pays attention to non-uniqueness and spatial gradients in physical quantities of interest, and can be applied to manifolds of arbitrary dimensionality. Using the metric as a cost function in optimization algorithms, we show that optimized low-dimensional projections can be found. We delineate a few applications of the cost function to datasets representing argon plasma, reacting flows and atmospheric pollutant dispersion. We demonstrate how the cost function can assess various dimensionality reduction and manifold learning techniques as well as data preprocessing strategies in their capacity to yield quality low-dimensional projections. We show that improved manifold topologies can facilitate building nonlinear regression models.

42 ENGINEERING↗
DOE OSTI2024

Microscopic Theory of Nonlinear Hall Effect in Three-Dimensional Magnetic Systems

Abstract Nonlinear Hall effect (NLHE) has been detected in various of condensed matter systems. Unlike linear Hall effect, NLHE may exist in physical systems with broken inversion symmetry in crystals. On the other hand, real space spin texture may also break inversion symmetry and result in NLHE. We employ the Feynman diagrammatic technique to calculate non-linear Hall conductivity (NLHC) in three-dimensional magnetic systems. The results connect NLHE with the physical quantity of emergent electrodynamics which originates from the magnetic texture. The leading order contribution of NLHC, χabb , is proportional to the emergent toroidal moment T a e , which reflects how the spin textures wind in three dimensions.

Hou 侯, Wen-Tao 文涛↗
DOE OSTI2023

A robust estimator of mutual information for deep learning interpretability

Abstract We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning (DL) models. To accurately estimate MI from a finite number of samples, we present GMM-MI (pronounced ‘Jimmie’), an algorithm based on Gaussian mixture models that can be applied to both discrete and continuous settings. GMM-MI is computationally efficient, robust to the choice of hyperparameters and provides the uncertainty on the MI estimate due to the finite sample size. We extensively validate GMM-MI on toy data for which the ground truth MI is known, comparing its performance against established MI estimators. We then demonstrate the use of our MI estimator in the context of representation learning, working with synthetic data and physical datasets describing highly non-linear processes. We train DL models to encode high-dimensional data within a meaningful compressed (latent) representation, and use GMM-MI to quantify both the level of disentanglement between the latent variables, and their association with relevant physical quantities, thus unlocking the interpretability of the latent representation. We make GMM-MI publicly available in this GitHub repository.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Redshift drift cosmography with ELT and SKAO measurements

ABSTRACT Mapping the expansion history of the universe is a compelling task of physical cosmology, especially in the context of the observational evidence for the recent acceleration of the universe, which demonstrates that canonical theories of cosmology and particle physics are incomplete and that there is new physics still to be discovered. Cosmography is a phenomenological approach to cosmology, where (with some caveats) physical quantities are expanded as a Taylor series in the cosmological redshift z, or analogous parameters such as the rescaled redshift y = z/(1 + z) or the logarithmic redshift x = ln (1 + z). Moreover, the redshift drift of objects following cosmological expansion provides a model-independent observable, detectable by facilities currently under construction, viz. the Extremely Large Telescope and the Square Kilometre Array Observatory (at least in its full configuration). Here, we use simulated redshift drift measurements from the two facilities to carry out an assessment of the cosmological impact and model discriminating power of redshift drift cosmography. We find that the combination of measurements from the two facilities can provide a stringent test of the Λ cold dark matter paradigm, and that overall the logarithmic-based expansions of the spectroscopic velocity drift are the most reliable ones, performing better than analogous expansions in the redshift or the rescaled redshift: the former nominally gives the smaller error bars for the cosmographic coefficients but is vulnerable to biases in the higher order terms (in other words, it is only reliable at low redshifts), while the latter always performs poorly.

Rocha, B. A. R.↗
DOE OSTI2023

The impact of baryonic potentials on the gravothermal evolution of self-interacting dark matter haloes

The presence of a central baryonic potential can have a significant impact on the gravothermal evolution of self-interacting dark matter (SIDM) haloes. We extend a semi-analytical fluid model to incorporate the influence of a static baryonic potential and calibrate it using controlled N-body simulations. We construct benchmark scenarios with varying baryon concentrations and different SIDM models, including constant and velocity-dependent self-interacting cross-sections. The presence of the baryonic potential induces changes in SIDM halo properties, including central density, core size, and velocity dispersion, and it accelerates the halo’s evolution in both expansion and collapse phases. Furthermore, we observe a quasi-universality in the gravothermal evolution of SIDM haloes with the baryonic potential, resembling a previously known feature in the absence of the baryons. By appropriately rescaling the physical quantities that characterize the SIDM haloes, the evolution of all our benchmark cases exhibits remarkable similarity. In conclusion, our findings offer a framework for testing SIDM predictions using observations of galactic systems where baryons play a significant dynamical role.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Ultrafast nonequilibrium dynamics and high-harmonic generation in two-dimensional quantum spin Hall materials

For this work, we develop the theoretical framework of nonequilibrium ultrafast photonics in monolayer quantum spin Hall insulators supporting a multitude of topological states. In these materials, ubiquitous strong light-matter interactions in the femtosecond scale lead to nonadiabatic quantum dynamics, resulting in topology-dependent nonlinear optoelectronic transport phenomena. We investigate the mechanism driving topological Dirac fermions interacting with strong ultrashort light pulses and uncover various experimentally accessible physical quantities that encode fingerprints of the quantum material's topological electronic state from the high-harmonic generated spectrum. Our work sets the theoretical cornerstones to realize the full potential of time-resolved harmonic spectroscopy for understanding nonequilibrium processes in quantum topological systems and identifying topological invariants in two-dimensional quantum spin Hall solid state systems.

2-dimensional systems↗
DOE OSTI2025

Further steps toward the next generation of covariant energy density functionals

The present study aims at further development of covariant energy density functionals (CEDFs) towards more accurate description of binding energies across the nuclear chart. Infinite basis corrections to binding energies in the fermionic and bosonic sectors of the covariant density functional theory are taken into account in the fitting protocol within the covariant density functional theory. In addition, total electron binding energies are used in the conversion of atomic binding energies into nuclear ones. Their dependence on neutron excess is investigated across the nuclear chart within the atomic approach. Furthermore, these factors were disregarded in the previous generation of covariant energy density functionals, but their omission leads to substantial global calculation errors for physical quantities of interest. For example, these errors for binding energies are of the order of 0.8 MeV or higher for the three major classes of covariant energy density functionals.

Binding energy & masses↗
DOE OSTI2025

Kerr effective black hole geometries in supergravity

We derive the explicit embedding of the effective Kerr spacetimes, which are pertinent to the vanishing of static Love numbers, soft hair descriptions of Kerr black holes, and low-frequency scalar-Kerr scattering amplitudes, as solutions within 𝑁 = 2 supergravity. These spacetimes exhibit a hidden 𝑆⁢𝐿⁡(2,𝑅) × 𝑈⁡(1) or 𝑆⁢𝑂⁡(4,2) symmetry resembling the so called subtracted geometries with 𝑆⁢𝐿⁡(2,𝑅) × 𝑆⁢𝐿⁡(2,𝑅) symmetry, which accurately represent the near-horizon geometry of Kerr black holes and, as we will argue most accurately represents the internal structure of the Kerr black hole. To quantify the differences among the effective Kerr spacetimes, we compare their physical quantities, internal structures, and geodesic equations. Although their thermodynamic properties, including entropy, match those of Kerr, our study uncovers significant differences in the interiors of these effective Kerr solutions. A careful examination of the internal structure of the spacetimes highlights the distinctions between various effective Kerr geometries and their quasinormal spectra.

quantum aspects of black holes↗
DOE OSTI2021

Multi-Area Model-Free State Estimation via Distributed Tensor Decomposition

This paper proposes a model-free method for distribution system state estimation based on tensor completion using canonical polyadic decomposition. In particular, we consider a setting where the network is divided into multiple areas. The measured physical quantities at buses located in the same area are processed by an area controller. A third-order tensor is constructed to collect these measured quantities. The measurements are analyzed locally to recover the full state information of the network. A closed-form iterative algorithm based on the alternating direction method of multipliers is developed to obtain the low-rank factors of the whole network state tensor where information exchange happens only between neighboring areas. To demonstrate the efficacy of the developed algorithm, numerical simulations are carried out using an IEEE test system.

alternating direction method of multipliers↗
DOE OSTI2022

Multiarea Distribution System State Estimation via Distributed Tensor Completion

Here, this paper proposes a model-free distribution system state estimation method based on tensor completion using canonical polyadic decomposition. In particular, we consider a setting where the network is divided into multiple areas. The measured physical quantities at buses located in the same area are processed by an area controller. A three-way tensor is constructed to collect these measured quantities. The measurements are analyzed locally to recover the full state information of the network. A distributed closed-form iterative algorithm based on the alternating direction method of multipliers is developed to obtain the low-rank factors of the whole network state tensor where information exchange happens only between neighboring areas. The convergence properties of the distributed algorithm and the sufficient conditions on the number of samples for each smaller network that guarantee the identifiability of the factors of the state tensor are presented. To demonstrate the efficacy of the proposed algorithm and to check the identifiability conditions, numerical simulations are carried out using the IEEE 123-bus system and a large-scale real utility feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

In situ plasmonic tip preparation and validation techniques for scanning tunneling microscopy

Among the many parts constituting a scanning tunneling microscope, the metallic tip is the component that directly interacts with the specimen and plays a critical role in visualizing the physical quantity of interest. While tip materials such as W and Pt–Ir are commonly used for topographic imaging and their preparation is well-documented, the preparation of plasmonic materials such as Ag for tip-enhanced Raman spectroscopy is relatively less standardized. Furthermore, we present several in situ Ag tip preparation and validation techniques for the microscopist to use depending on their intended application, including atomic resolution imaging, scanning tunneling spectroscopy (STM), and tip-enhanced Raman spectro-microscopy in ultrahigh vacuum. Besides optical applications, these methods are not limited to Ag but also applicable to other STM tip materials.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Correlations between maximum mass of neutron stars and the nuclear matter properties and the constraints from PSR J0740+6620 and GW190814

The correlations between the maximum mass of neutron stars (NSs) with the properties of nuclear matter at saturation density ρ 0 , i.e., the incompressibility K 0 , the symmetry energy E sym (ρ 0 ), and its slope L(ρ 0 ), the isoscalar effective mass m$^{*}_{s0}$, and the parameter f I , have been investigated using several sets of Skyrme interactions based on the momentum-dependent (MD) Skyrme interactions SAMi-J27 (soft), SAMi-J31 (stiff), and SAMi-J35 (super-stiff), as well as the momentum-independent (MI) Skyrme interaction SkT5. Only one physical quantity is varied at a time for a given set of Skyrme interactions. It is found that the maximum mass of NSs has (1) a strong positive linear correlation with K 0 for stiff and super-stiff equation of state (EOS) and no correlation with K 0 for soft EOS; (2) a negative logarithm correlation with the symmetry energy E sym (ρ 0 ) for all the EOSs; (3) a positive logarithm correlation with the slope L(ρ 0 ) for all the EOSs; (4) a positive logarithm correlation with the parameter f I for MD EOS; (5) a negative power-law correlation with m$^{*}_{s0}$ for MD EOS. Based on these established correlations, the constraints to the nuclear matter properties from the mass of PSR J0740+6620 and GW190814 are investigated. Furthermore, we find that while rotation enhances the maximum mass of NSs, it does not effect the underlying correlations between the maximum mass and the aforementioned nuclear matter parameters.

Zhou, M. [Shaanxi Normal University, Xi’an (China)↗