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Last Updated: 21 July 2022

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Real-space observation of fluctuating antiferromagnetic domains

Magnetic domains play a vital role in magnetism physics and its industrial applications. In addition to topological quantum research, the Dynamics of antiferromagnets also plays a key role. These findings reveal the high potential use of dynamic domain imaging in phase transition studies and magnetic device research.

Source link: https://www.osti.gov/biblio/1873171


Property space mapping of Pseudomonas aeruginosa permeability to small molecules

In Gram-negative bacteria, selective permeability barriers are present, shielding cells from antibiotics and other small molecules, as shown by two membrane cell envelopes. The approach uses mass spectrometric analysis of accumulation of a library of structurally diverse chemicals in four isogenic strains of P. aeruginosa with different permeability barriers. This model converted good perceptives into P. aeruginosa with an accuracy of 89% and precision above 58%. The good permeators are widely distributed throughout the property space and can be mapped to six distinct regions representing various chemical scaffolds.

Source link: https://www.osti.gov/biblio/1869114


A Scalable Space-Time Domain Decomposition Approach for Solving Large Scale Nonlinear Regularized Inverse Ill Posed Problems in 4D Variational Data Assimilation

Starting from the global functional level that was fully integrated, we transition to a set of subdomains that include the order reductions of both the predictive and the Data Assimilation models. The number of state variables in the model, the number of observations in an assimilation cycle, as well as numerical parameters as the discretization step in time and in space domain are determined by the discretization grid used by the repository Ocean Synthesis/Reanalysis Directory of Hamburg University.

Source link: https://www.osti.gov/biblio/1868974


Learning in continuous action space for developing high dimensional potential energy models

As in chess, Shogi, and Go, Abstract Reinforcement learning techniques that integrate tree search with deep learning have had great success in finding exorbitantly wide, yet discrete action spaces. Multiple real-world materials discovery and design applications, on the other hand, require multi-dimensional search results and learning domains with constant action spaces. To enable effective and scalable search for continuous action space challenges, we present a RL plan based on decision trees that incorporates revised rewards for increased exploration, accurate sampling during playouts, and a u201cwindow scaling scheme (Mexo) for improved exploitation. We investigate error trends across various elements of the latent space, tracing their roots to elemental structural variation and the smoothness of the element's surface. Our RL scheme will be broadly applicable to several other physical science problems that necessitate search in continuous action spaces.

Source link: https://www.osti.gov/biblio/1841503


Theory and application of the vector pair correlation function for real-space crystallographic analysis of order/disorder correlations from STEM images

Deviations of local structure and chemistry from the typical crystalline unit cell are increasingly recognized to have a major effect on the properties of several industrially useful materials. We discuss here the vector pair correlation function, which can be used with atomic-resolution scanning transmission electron microscopy images to measure and analyze structural order/disorder correlations. vPCFs with crystallographic orientation data, are spatially resolved, can be applied directly on a sublattice basis, and are suitable for any substrate that can be imaged with STEM. We measure partial vsPCFs in Ba 5 SmSn 3 Nb 7 O 30, a tetragonal bronze granular complex oxide, to demonstrate the effectiveness of our design.

Source link: https://www.osti.gov/biblio/1864176


Space-Time Quantum Metasurfaces

Metasurfaces have recently entered the field of quantum photonics, enabling quantum light manipulation using a small nanophotonic device. To truly understand metasurfaces at the deepest quantum level, one of the most effective quantum scales demands the ability to precisely control coherent light-matter interactions in space and time. Using a compact photonic platform, we introduce the concept of space-time quantum metasurfaces for arbitrary control of nonclassical light's spectral, spatial, and spin properties. We demonstrate that space-time quantum metasurfaces enable on-demand tailoring of entanglement among all degrees of freedom of a single photon.

Source link: https://www.osti.gov/biblio/1822798


Pore-Space Partition and Optimization for Propane-Selective High-Performance Propane/Propylene Separation

The production of efficient propane adsorbents for the purification of propylene from a C 3 H 8/C 3 H 6 mixture is a promising alternative to replacing the energy-intensive cryogenic distillation. Here, we spotlight a family of pore-space-partitioned crystalline porous materials with a high C 3 H 8 uptake capacity and the highly desirable, yet rare C3 H 8 selectivity. In addition, the high C 3 H 8/C 3 H 6 separation results were also confirmed by the breakthrough experiments.

Source link: https://www.osti.gov/biblio/1843742


Searching for Dwarf Galaxies in Gaia DR2 Phase-space Data Using Wavelet Transforms

In the Milky Way in Gaia DR2 results, we present a wavelet-based algorithm to detect dwarf galaxies. Our algorithm finds overdensities in 4D position-motion space, making it the first search to specifically look for dwarf galaxy candidates using velocity information. We improve our algorithm and quantify its results by looking for mock dwarfs and star galaxies that were introduced into Gaia DR2 results and for known Milky Way satellite galaxies. Based on these findings, we expect our hunt to find five u00b1 2 new satellite galaxies, four in the PS1 footprint and one outside the Dark Energy Survey and PS1 footprints. We apply our algorithm to the Gaia DR2 data set and retrieve 830 high-significance candidates, out of which we find a "gold standard" list of 200 candidates based on cross-matching with potential candidates identified in a preliminary search using Gaia EDR3 results.

Source link: https://www.osti.gov/biblio/1817875


Co-Design of Free-Space Metasurface Optical Neuromorphic Classifiers for High Performance

The incoming photonic field is usually converted into the electronic domain in order to categorize features in a scene. Recently, an alternative strategy has emerged, in which passively developed materials can perform classification tasks by using free-space propagation and diffraction of light. To improve classification accuracy, we found that system architecture, material structure, and input light field are intertwined, and they need to be co-designed to maximize classification accuracy. With an order of magnitude fewer diffractive characteristics than previously reported, our simulations show that a single layer metasurface can achieve classification accuracy higher than conventional linear classifiers. for an optimized distance u03bb to the output plane, MNIST results show that for an aperture density of 100 u03bb, single layer metasurfaces of size 100/u03bb with an aperture density of 96 percent. Multiple layer designs may be a reason why such systems can profit from slower asymptotic scaling with the number of apertures.

Source link: https://www.osti.gov/biblio/1810377


Machine Learning the Sixth Dimension: Stellar Radial Velocities from 5D Phase-space Correlations

The positions and velocities of over a billion Milky Way stars will be recorded by the Gaia satellite. Working with a mock Gaia catalog, we show that the network can successfully recover the distributions and correlations of each velocity component for actors who land within a kpc of the Sun. Even though it represents a small fraction of the total star count, we also show that the network can precisely reconstruct the velocity distribution of a kinematic substructure in the stellar halo that is spatially uniform.

Source link: https://www.osti.gov/biblio/1819369

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions