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DOE OSTI2022

A Massive, Position-Resolving, High-Energy-Resolution Detector for Non-Accelerator Cosmic and Intensity Frontier Particle Physics

We proposed to take the first steps in the development of a detector that promises energy resolution of tens of eV FWHM combined with robust nuclear-recoil discrimination and high fidelity, mm-precision position reconstruction for applications in non-accelerator particle physics at the Cosmic and Intensity Frontiers. The detector would obtain these excellent resolutions by sensing athermal phonons produced by particle interactions in crystalline, dielectric targets using a sensitive, highly multiplexable superconducting phonon sensor, the kinetic inductance detector (KID). This detector would be applicable to: the search for low-mass particle dark matter candidates with masses below 5~GeV via direct detection of scattering of dark matter particles with terrestrial nuclei; detection of coherent elastic neutrino-nucleus scattering to test for new physics such as a non-standard value of the weak nuclear charge, non-standard neutrino interactions (perhaps driven by a neutrino magnetic moment), or the existence of sterile neutrinos; and, searches for neutrinoless double-beta decay. During the funding period, we demonstrated scaling up of the detector concept from a 22-mm by 22-mm by 1-mm, 1-g prototype with 0.9~keV FWHM energy resolution to a 75-mm diameter by 1-mm, 9-g prototype while improving the inferred energy resolution to 0.7~keV~FWHM. In the process, we solved many problems associated with scaling device fabrication to large wafers and vastly reduced the fraction of the surface occupied by inactive but energy-absorbing metal. We also demonstrated a new technique that substantially simplifies the process of characterizing a new detector. These results provide a good foundation for future work scaling up the design to 4-mm thickness substrates and improving the resolution to reach 0.035~keV~FWHM, yielding a detector with compelling potential for the above applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRSDate not supplied

Machine Learning Approaches for Rare-Earth Silicate Environmental Barrier Coating Thermochemical and Thermomechanical Property Predictions

Environmental barrier coatings (EBCs) are a necessary enabling technology for the transition from superalloys to silicon carbide (SiC) ceramic matrix composites (CMCs) in gas turbine engines for increased efficiency and decreased fuel costs. SiC-based CMCs are prone to oxidation-based degradation in the engine hot section, and rare-earth (RE) silicates are promising candidates for EBCs due to their close thermal expansion match to the composite substrate and oxidation resistance. However, the design of EBCs is hindered by the large chemical space of candidate materials and the difficulty in obtaining material properties for engineering optimization. This is especially difficult as research continues into mixed-cation or “high-entropy” RE silicates. First-principles computational methods such as density functional theory (DFT) are highly effective at calculating material properties to guide coating design but are limited by their computational cost. Atomistic simulations have the potential to both accelerate property calculations and expand the properties able to be calculated due to their lower computational compared to DFT. However, they require interatomic potentials (IAPs) specific to the material system of interest, and, to our knowledge, there are no suitable IAPs for RE silicates. Machine learning (ML) is a promising technique to accelerate material property predictions indirectly by generating IAPs for atomistic simulations or via direct prediction. In this work, we present two ML approaches to accelerate the calculation of RE silicate properties relevant to EBC design: 1) a ML-derived interatomic potential (IAP) for atomistic simulations of yttrium disilicate (Y2Si2O7) from DFT training data, and 2) a neural network (NN) model to directly predict thermochemical properties of RE silicates and oxides directly from easily obtainable unit cell parameters. Classical MD simulations using the IAP yield lattice properties and bond lengths in good agreement with both DFT and experimental results from x-ray diffraction. Thermodynamic properties calculated using the finite-displacement phonon method and quasi-harmonic approximation were orders of magnitude faster than DFT with good agreement to the DFT results. The IAP was also used to calculate properties such as coefficient of thermal expansion (CTE) that require large simulation supercells and are therefore difficult with DFT. The IAP correctly predicted the anisotropic nature of the CTE in three different phases of Y2Si2O7. The NN model predicts constant pressure heat capacity, Cp, orders of magnitude faster than DFT calculations, which can enable its use as a surrogate model for multiscale simulations. The two methods presented in this work demonstrate the utility of ML for accelerating the prediction of RE silicate properties, which can in turn accelerate EBC design and optimization.

machine learning↗

DOE OSTI2023

Spin dynamics in quantum dots on liquid helium

Liquid He-4 is free from magnetic defects, making it an ideal substrate for electrons with long-lived spin states. Such states can serve as qubit states. Here, in this study, we consider the spin states of electrons electrostatically localized in quantum dots on a helium surface. Efficient gate operations in this system require spin-orbit coupling. It can be created by a nonuniform magnetic field from a current-carrying wire, can be turned on and off, and allows one to obtain large electrodipole moment and comparatively fast coupling of spins in neighboring dots. Of central importance is to understand spin decay due to the spin-orbit coupling. We establish the leading mechanism of such decay and show that the decay is sufficiently slow to enable high-fidelity single- and two-qubit gate operations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗