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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing

MOTIVATION: High-throughput sequencing (HTS) is a modern sequencing technology used to profile microbiomes by sequencing thousands of short genomic fragments from the microorganisms within a given sample. This technology presents a unique opportunity for artificial intelligence to comprehend the underlying functional relationships of microbial communities. However, due to the unstructured nature of HTS data, nearly all computational models are limited to processing DNA sequences individually. This limitation causes them to miss out on key interactions between microorganisms, significantly hindering our understanding of how these interactions influence the microbial communities as a whole. Furthermore, most computational methods rely on post-processing of samples which could inadvertently introduce unintentional protocol-specific bias. RESULTS: Addressing these concerns, we present SetBERT, a robust pre-training methodology for creating generalized deep learning models for processing HTS data to produce contextualized embeddings and be fine-tuned for downstream tasks with explainable predictions. By leveraging sequence interactions, we show that SetBERT significantly outperforms other models in taxonomic classification with genus-level classification accuracy of 95%. Furthermore, we demonstrate that SetBERT is able to accurately explain its predictions autonomously by confirming the biological-relevance of taxa identified by the model. AVAILABILITY AND IMPLEMENTATION: All source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna.

Ludwig, David W↗
DOE OSTI2024

Increasing aggregate size reduces single-cell organic carbon incorporation by hydrogel-embedded wetland microbes

Abstract Microbial degradation of organic carbon in sediments is impacted by the availability of oxygen and substrates for growth. To better understand how particle size and redox zonation impact microbial organic carbon incorporation, techniques that maintain spatial information are necessary to quantify elemental cycling at the microscale. In this study, we produced hydrogel microspheres of various diameters (100, 250, and 500 μm) and inoculated them with an aerobic heterotrophic bacterium isolated from a freshwater wetland (Flavobacterium sp.), and in a second experiment with a microbial community from an urban lacustrine wetland. The hydrogel-embedded microbial populations were incubated with 13C-labeled substrates to quantify organic carbon incorporation into biomass via nanoSIMS. Additionally, luminescent nanosensors enabled spatially explicit measurements of oxygen concentrations inside the microspheres. The experimental data were then incorporated into a reactive-transport model to project long-term steady-state conditions. Smaller (100 μm) particles exhibited the highest microbial cell-specific growth per volume, but also showed higher absolute activity near the surface compared to the larger particles (250 and 500 μm). The experimental results and computational models demonstrate that organic carbon availability was not high enough to allow steep oxygen gradients and as a result, all particle sizes remained well-oxygenated. Our study provides a foundational framework for future studies investigating spatially dependent microbial activity in aggregates using isotopically labeled substrates to quantify growth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

An finite element analysis surrogate model with boundary oriented graph embedding approach for rapid design

Abstract In this work, we present a boundary oriented graph embedding (BOGE) approach for the graph neural network to assist in rapid design and digital prototyping. The cantilever beam problem has been solved as an example to validate its potential of providing physical field results and optimized designs using only 10 ms. Providing shortcuts for both boundary elements and local neighbor elements, the BOGE approach can embed unstructured mesh elements into the graph and performs an efficient regression on large-scale triangular-mesh-based finite element analysis (FEA) results, which cannot be realized by other machine-learning-based surrogate methods. It has the potential to serve as a surrogate model for other boundary value problems. Focusing on the cantilever beam problem, the BOGE approach with 3-layer DeepGCN model achieves the regression with mean square error (MSE) of 0.011 706 (2.41% mean absolute percentage error) for stress field prediction and 0.002 735 MSE (with 1.58% elements having error larger than 0.01) for topological optimization. The overall concept of the BOGE approach paves the way for a general and efficient deep-learning-based FEA simulator that will benefit both industry and Computer Aided Design (CAD) design-related areas.

42 ENGINEERING↗
DOE OSTI2022

Differences in chemical evolution between isolated and embedded prestellar cores

Models of prestellar cores often assume that the cores are isolated from their environment – material outside the core boundary plays no role in the subsequent evolution. This is unlikely to be the case in reality, where cores are located within hierarchically substructured molecular clouds. Here, we investigate the dynamical and chemical evolution of prestellar cores, modelled as Bonnor–Ebert spheres, and show that the density of the ambient medium has a large impact on the resulting chemical properties of the cores. Models embedded in high-density, low-temperature surroundings have greatly enhanced abundances of several molecules, such as CO and CS, compared to models with more diffuse surroundings, corresponding to relatively isolated cores. The predicted intensities and profile shapes of molecular lines are also affected. The density of the ambient medium has a stronger effect on the chemical evolution than whether the cores are initially in or out of equilibrium. This suggests that the impact of environment cannot be neglected when modelling chemistry in prestellar cores; the results of these models are highly sensitive to the assumptions made about the core surroundings.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Structure of a 28.5 kDa duplex-embedded G-quadruplex system resolved to 7.4 Å resolution with cryo-EM

Genomic regions with high guanine content can fold into non-B form DNA four-stranded structures known as G-quadruplexes (G4s). Extensive in vivo investigations have revealed that promoter G4s are transcriptional regulators. Little structural information exists for these G4s embedded within duplexes, their presumed genomic environment. Here, we report the 7.4 Å resolution structure and dynamics of a 28.5 kDa duplex-G4-duplex (DGD) model system using cryo-EM, molecular dynamics, and small-angle X-ray scattering (SAXS) studies. The DGD cryo-EM refined model features a 53° bend induced by a stacked duplex-G4 interaction at the 5’ G-tetrad interface with a persistently unstacked 3’ duplex. The surrogate complement poly dT loop preferably stacks onto the 3’ G-tetrad interface resulting in occlusion of both 5’ and 3’ tetrad interfaces. Structural analysis shows that the DGD model is quantifiably more druggable than the monomeric G4 structure alone and represents a new structural drug target. Our results illustrate how the integration of cryo-EM, MD, and SAXS can reveal complementary detailed static and dynamic structural information on DNA G4 systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Composite quarks and leptons with embedded QCD

We construct a model of quark and lepton compositeness based on an 𝑆⁢𝑈⁡(15) gauge interaction that confines chiral preons, which are also charged under the weakly coupled 𝑆⁢𝑈⁢(4) PS ×𝑆⁢𝑈⁢(2) 𝐿 ×𝑆⁢𝑈⁢(2) 𝑅 gauge group. The breaking of the latter, down to the Standard Model group, is achieved by scalar 𝑆⁢𝑈⁡(15) bound states at a scale in the 30–100 TeV range. The embedding of the QCD gauge group in 𝑆⁢𝑈⁢(4) PS slows down the running of 𝛼 𝑠 in the UV. We estimate the effects of the strongly coupled 𝑆⁢𝑈⁡(15) dynamics on the running of the 𝑆⁢𝑈⁢(4) PS ×𝑆⁢𝑈⁢(2) 𝐿 ×𝑆⁢𝑈⁢(2) 𝑅 gauge couplings, which likely remain perturbative beyond the compositeness scale of about 10 3 –10 4 TeV, and even above a unification scale. A composite vectorlike lepton doublet acquires a mass in the TeV range probed at future colliders, and an extended Higgs sector arises from six-preon bound states.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Electron correlations in the cubic paramagnetic perovskite Sr ( V , Mn ) O 3 : Results from fully self-consistent self-energy embedding calculations

In this study, we use the thermodynamically consistent and conserving self-energy embedding theory (SEET) to study the spectra of the prototypical undistorted cubic perovskites SrVO 3 and SrMnO 3 . In the strongly correlated metallic SrVO 3 we find that the usual attribution of the satellite peaks at –1.8 eV to Hund or Hubbard physics in the t 2 g orbitals is inconsistent with our calculations. In the strongly correlated insulator SrMnO 3 we recover insulating behavior due to a feedback effect between the strongly correlated orbitals and the weakly correlated environment. Our calculation shows a systematic convergence of spectral features as the space of strongly correlated orbitals is enlarged, paving the way to a systematic parameter-free study of correlated perovskites.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Quantum embedding study of strain- and electric-field-induced Stark effects on the NV - center in diamond

The NV - color center in diamond has been demonstrated as a powerful nanosensor for quantum metrology due to the sensitivity of its optical and spin properties to external electric, magnetic, and strain fields. In view of these applications, we use quantum embedding to derive a many-body description of strain- and electric-field-induced Stark effects on the NV - center. Here, we quantify how strain longitudinal to the axis of NV - shifts the excited states in energy, while strain with a component transverse to the NV - axis splits the degeneracies of the 3 E and 1 E states. The largest effects are for the optically relevant 3 E manifold, which splits into E x and E y with transverse strain. From these responses we extract strain susceptibilities for the E x/y states within the quasilinear regime. Additionally, we study the many-body dipole matrix elements of the NV - and find a permanent dipole 1.6 D at zero strain, which is somewhat smaller than that obtained from recent density functional theory calculations. We also determine the transition dipole between the E x and E y and how it evolves with strain.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Forces for structural optimizations in correlated materials within a DFT+embedded DMFT functional approach

Here, we implemented the derivative of the free energy functional with respect to the atom displacements, so called force, within the combination of density functional theory and the embedded dynamical mean-field theory. We show that in combination with the numerically exact quantum Monte Carlo (MC) impurity solver, the MC noise cancels to a great extend, so that the method can be used very efficiently for structural optimization of correlated electron materials. As an application of the method, we show how strengthening of the fluctuating moment in FeSe superconductor leads to a substantial increase of the anion height, and consequently to a very large effective mass, and also strong orbital differentiation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Cluster size convergence of the density matrix embedding theory and its dynamical cluster formulation: A study with an auxiliary-field quantum Monte Carlo solver

In this work, we investigate the cluster size convergence of the energy and observables using two forms of density matrix embedding theory (DMET): the original cluster form (CDMET) and a new formulation motivated by the dynamical cluster approximation (DCA-DMET). Both methods are applied to the half-filled one- and two-dimensional Hubbard models using a sign-problem free auxiliary-field quantum Monte Carlo impurity solver, which allows for the treatment of large impurity clusters of up to 100 sites. While CDMET is more accurate at smaller impurity cluster sizes, DCA-DMET exhibits faster asymptotic convergence towards the thermodynamic limit. We use our two formulations to produce new accurate estimates for the energy and local moment of the two-dimensional Hubbard model for U/t=2,4,6. These results compare favorably with the best data available in the literature, and help resolve earlier uncertainties in the moment for U/t=2.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Structural and dynamic properties of liquid tin from a new modified embedded-atom method force field

We developed a new modified embedded-atom method (MEAM) force field for liquid tin. Starting from the Ravelo and Baskes force field [Phys. Rev. Lett. 79, 2482 (1997)], the parameters are adjusted using a simulated annealing optimization procedure in order to obtain better agreement with liquid-phase data. The predictive capabilities of the new model and the Ravelo and Baskes force field are evaluated using molecular dynamics by comparing to a wide range of first-principles and experimental data. The quantities studied include crystal properties (cohesive energy, bulk modulus, equilibrium density, and lattice constant of various crystal structures), melting temperature, liquid structure, liquid density, self-diffusivity, viscosity, and vapor-liquid surface tension. We show that although the Ravelo and Baskes force field generally gives better agreement with the properties related to the solid phases of tin, the new MEAM force field gives better agreement with liquid tin properties.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Dynamical mean-field theory, density-matrix embedding theory, and rotationally invariant slave bosons: A unified perspective

In this paper, we present a unified perspective on dynamical mean-field theory (DMFT), density-matrix embedding theory (DMET), and rotationally invariant slave bosons (RISB). We show that DMET can be regarded as a simplification of the RISB method where the quasiparticle weight is set to unity. Finally, this relation makes it easy to transpose extensions of a given method to another: For instance, a temperature-dependent version of RISB can be used to derive a temperature-dependent free-energy formula for DMET.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Embedding space approach to Lorentzian CFT amplitudes and causal spherical functions

Conformal field theory in a Minkowski setting is discussed in an embedding space approach, paying special attention to causality constraints for four-point amplitudes. The physics of dilatation and Lorentz boost is emphasized in specifying the noncompact maximal Abelian subgroup of S O ( d , 2 ) . Reduction of a conformal field theory four-point amplitudes as functions of cross ratios is shown to be equivalent to enforcing H bi-invariance, i.e., F ( h g h ′ ) = F ( g ) , with g ∈ S O ( d , 2 ) and H an appropriate subgroup. Causality is imposed by introducing appropriate semigroups. Causal zonal spherical functions are constructed, making contact with Minkowski conformal blocks introduced previously. Published by the American Physical Society 2024

Agarwal, Pulkit (ORCID:0000000346581691)↗
DOE OSTI2019

Complete asymptotically safe embedding of the standard model

We introduce and discuss the “Tetrad Model”, a large color/flavor embedding of the Standard model which has an interacting ultraviolet fixed point. It is demonstrated that its extended-Pati-Salam symmetry is broken radiatively via the Coleman-Weinberg mechanism, while the remaining electroweak symmetry is broken when mass-squared terms run negative. In the IR the theory yields just the Standard Model, augmented by the fact that the Higgs fields carry the same generation indices as the matter fields. It is also shown that the Higgs mass-squareds develop a hierarchical structure in the IR, from a UV theory that is asymptotically flavor symmetric, opening up an interesting direction for explaining the emergence of the observed flavor structure.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Embedded training of neural-network subgrid-scale turbulence models

We report that the weights of a deep neural-network model are optimized in conjunction with the governing flow equations to provide a model for subgrid-scale stresses in a temporally developing plane turbulent jet at Reynolds number Re 0 = 6000 . The objective function for training is first based on the instantaneous filtered velocity fields from a corresponding direct numerical simulation, and the training is by a stochastic gradient descent method, which uses the adjoint Navier-Stokes equations to provide the end-to-end sensitivities of the model weights to the velocity fields. In-sample and out-of-sample testing on multiple dual-jet configurations show that its required mesh density in each coordinate direction for prediction of mean flow, Reynolds stresses, and spectra is half that needed by the dynamic Smagorinsky model for comparable accuracy. The same neural-network model trained directly to match filtered subgrid-scale stresses, without the constraint of being embedded within the flow equations during the training, fails to provide a qualitatively correct prediction. The coupled formulation is generalized to train based only on mean-flow and Reynolds stresses, which are more readily available in experiments. The mean-flow training provides a robust model, which is important, though a somewhat less accurate prediction for the same coarse meshes, as might be anticipated due to the reduced information available for training in this case. The anticipated advantage of the formulation is that the inclusion of resolved physics in the training increases its capacity to extrapolate. This is assessed for the case of passive scalar transport, for which it outperforms established models due to improved mixing predictions.

42 ENGINEERING↗
DOE OSTI2023

Embedding hard physical constraints in neural network coarse-graining of three-dimensional turbulence

In recent years, deep learning approaches have shown much promise in modeling complex systems in the physical sciences. A major challenge in deep learning of partial differential equations is enforcing physical constraints and boundary conditions. In this work, we propose a general framework to directly embed the notion of an incompressible fluid into convolutional neural networks, and apply this to coarse-graining of turbulent flow. These physics-embedded neural networks leverage interpretable strategies from numerical methods and computational fluid dynamics to enforce physical laws and boundary conditions by taking advantage the mathematical properties of the underlying equations. Here, we demonstrate results on three-dimensional fully developed turbulence, showing that this technique drastically improves local conservation of mass, without sacrificing performance according to several other metrics characterizing the fluid flow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Spin-flop coupling and exchange bias in embedded complex oxide micromagnets

The magnetic domains of embedded micromagnets defined in epitaxial La0.7Sr0.3MnO3 (LSMO) thin films and LaFeO3/LSMO bilayers were investigated using soft x-ray magnetic microscopy. Square micromagnets with 2 x 2 m dimensions aligned with their edges parallel to the easy axes of LSMO provide an ideal experimental geometry for probing the influence of interface exchange coupling on the magnetic domain patterns. The observation of unique domain patterns not found in ferromagnetic metal microstructures, namely divergent antiferromagnetic vortex domains and Z -type domains, suggests the simultaneous presence of spin-flop coupling and local exchange bias in this system.

Takamura, Yayoi↗
DOE OSTI2019

Quantum Oscillations in Flux-Grown SmB 6 with Embedded Aluminum

SmB 6 is a candidate topological Kondo insulator that displays surface conduction at low temperatures. In this study, we perform torque magnetization measurements as a means to detect de Haas–van Alphen (dHvA) oscillations in SmB 6 crystals grown by aluminum flux. We find that dHvA oscillations occur in single crystals containing embedded aluminum, originating from the flux used to synthesize SmB 6 . Measurements on a sample with multiple, unconnected aluminum inclusions show that aluminum crystallizes in a preferred orientation within the SmB 6 cubic lattice. The presence of aluminum is confirmed through bulk susceptibility measurements, but does not show a signature in transport measurements. Lastly, we discuss the ramifications of our results.

36 MATERIALS SCIENCE↗