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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

SPEARS: A Database-Invariant Spectral modeling API

The Spectral Physics Environment for Advanced Remote Sensing (SPEARS) application programming interface (API) is a Python-based, line-by-line, local thermal equilibrium (LTE) spectral modeling code which is optimized for simultaneously synthesizing optical spectra from any combination of fundamental spectroscopic databases. In this article, we contribute two novel spectral modeling techniques to the scientific literature. First we describe how SPEARS integrates a physics-based collisional model for calculating pressure broadening in the absence of available broadening coefficients. With this collisional model implementation, a generalized approach to fundamental spectroscopic databases can be achieved across multiple databases. We also detail our adaptive grid mesh algorithm developed to make the code scalable for simulating large spectral bandwidths at high spectral fidelity using intuitive grid parameters. Here, we present comparisons to other modeling tools, experiments, and provide a discussion on the SPEARS user interface.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Non-conformal attractor in boost-invariant plasmas

We study the dissipative evolution of (0+1)-dimensionally expanding media with Bjorken symmetry using the Boltzmann equation for massive particles in relaxation-time approximation. Breaking conformal symmetry by a mass induces a non-zero bulk viscous pressure in the medium. It is shown that even a small mass (in units of the local temperature) drastically modifies the well-known attractor for the shear Reynolds number previously observed in massless systems. For generic nonzero particle mass, neither the shear nor the bulk viscous pressure relax quickly to a non-equilibrium attractor; they approach the hydrodynamic limit only late, at small values of the inverse Reynolds numbers. Only the longitudinal pressure, which is a combination of thermal, shear and bulk viscous pressures, continues to show early approach to a far-off-equilibrium attractor, driven by the rapid longitudinal expansion at early times. Second-order dissipative hydrodynamics based on a gradient expansion around locally isotropic thermal equilibrium fails to reproduce this attractor.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

E2 rotational invariants of 0$^{+}_{1}$ and 2$^{+}_{1}$ states for 106 Cd: The emergence of collective rotation

The collective structure of 106 Cd is elucidated by multi-step Coulomb excitation of a 3.849 MeV/A beam of 106 Cd on a 1.1 mg/cm 2 208 Pb target using GRETINA-CHICO2 at ATLAS. Fourteen E2 matrix elements were obtained. The nucleus 106 Cd is a prime example of emergent collectivity that possesses a simple structure: it is free of complexity caused by shape coexistence and has a small, but collectively active number of valence nucleons. This work follows in a long and currently active quest to answer the fundamental question of the origin of nuclear collectivity and deformation, notably in the cadmium isotopes. The results are discussed in terms of phenomenological models, the shell model, and Kumar-Cline sums of E2 matrix elements. The < 0$^{+}_{2}$ ||E2||2$^{+}_{1}$ > matrix element is determined for the first time, providing a total, converged measure of the electric quadrupole strength, < Q 2 >, of the first-excited 2$^{+}_{1}$ level relative to the 0$^{+}_{1}$ ground state, which does not show an increase as expected of harmonic and anharmonic vibrations. Strong evidence for triaxial shapes in weakly collective nuclei is indicated; collective vibrations are excluded. This is contrary to the only other cadmium result of this kind in 114 Cd by C. Fahlander et al., Nucl. Phys. A485, 327 (1988), which is complicated by low-lying shape coexistence near midshell.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Seismological Stress Drops for Confined Ruptures Are Invariant to Normal Stress

Seismic moment and rupture length can be combined to infer stress drop, a key parameter for assessing earthquakes. In natural earthquakes, stress drops are largely depth-independent, which is surprising given the expected dependence of frictional stress on normal stresses and hence overburden. We have developed a transparent experimental fault that allows direct observation of thousands of slip events, with ruptures that are fully contained within the fault. Surprisingly, the observed stress drops are largely independent of both the magnitude of normal stress and its heterogeneity, capturing the independence seen in nature. However, we observe larger, normal stress-dependent stress drops when the fault area is reduced, which allows slip events to frequently reach the edge of the interface. We conclude that confined ruptures have normal stress independent stress drops, and thus the depth-independent stress drops of tectonic earthquakes may be a consequence of their confined nature.

58 GEOSCIENCES↗
DOE OSTI2023

The scaled-invariant Planckian metal and quantum criticality in Ce1−xNdxCoIn5

Abstract The mysterious Planckian metal state, showing perfect T -linear resistivity associated with universal scattering rate, 1/ τ = α k B T /ℏ with α ~ 1, has been observed in the normal state of various strongly correlated superconductors close to a quantum critical point. However, its microscopic origin and link to quantum criticality remains an outstanding open problem. Here, we observe quantum-critical T / B -scaling of the Planckian metal state in resistivity and heat capacity of heavy-electron superconductor Ce 1− x Nd x CoIn 5 in magnetic fields near the edge of antiferromagnetism at the critical doping x c ~ 0.03. We present clear experimental evidences of Kondo hybridization being quantum critical at x c . We provide a generic microscopic mechanism to qualitatively account for this quantum critical Planckian state within the quasi-two dimensional Kondo-Heisenberg lattice model near Kondo breakdown transition. We find α is a non-universal constant and depends inversely on the square of Kondo hybridization strength.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Defect detection in atomic-resolution images via unsupervised learning with translational invariance

Abstract Crystallographic defects can now be routinely imaged at atomic resolution with aberration-corrected scanning transmission electron microscopy (STEM) at high speed, with the potential for vast volumes of data to be acquired in relatively short times or through autonomous experiments that can continue over very long periods. Automatic detection and classification of defects in the STEM images are needed in order to handle the data in an efficient way. However, like many other tasks related to object detection and identification in artificial intelligence, it is challenging to detect and identify defects from STEM images. Furthermore, it is difficult to deal with crystal structures that have many atoms and low symmetries. Previous methods used for defect detection and classification were based on supervised learning, which requires human-labeled data. In this work, we develop an approach for defect detection with unsupervised machine learning based on a one-class support vector machine (OCSVM). We introduce two schemes of image segmentation and data preprocessing, both of which involve taking the Patterson function of each segment as inputs. We demonstrate that this method can be applied to various defects, such as point and line defects in 2D materials and twin boundaries in 3D nanocrystals.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Optimal invariant sets for atomistic machine learning

The representation of atomic configurations for machine learning models has led to numerous sets of descriptors. However, many descriptor sets are incomplete and/or functionally dependent. Incomplete sets cannot faithfully represent atomic environments. Yet complete constructions often suffer from a high degree of functional dependence, where some descriptors are functions of others. These redundant descriptors do not improve discrimination between atomic environments. We employ pattern recognition techniques to remove dependent descriptors to produce the smallest possible set that satisfies completeness. We apply this in two ways: First, we refine an existing description, the atomic cluster expansion. Second, we augment an incomplete construction, yielding a new message-passing neural network architecture that can recognize up to 5-body patterns. This architecture shows strong accuracy on state-of-the-art benchmarks while retaining low computational cost. Our results demonstrate the utility of this strategy to optimize descriptor sets across a range of descriptors and application datasets.

97 MATHEMATICS AND COMPUTING↗