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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

All-Digital Plug and Play Passive RFID Sensors for Energy Efficient Building Control

This is the final report for the project “All-Digital Plug and Play Passive RFID Sensors for Energy Efficient Building Control”, funded by DOE, and performed by Clemson University, Phase IV Engineering and Harvard University from October 1, 2016 to December 31, 2020. The main objective of this project is to develop, demonstrate and pre-commercialize a novel, plug & play, battery-free, wireless sensor technology to enable low-cost (<$10 per node) indoor and outdoor temperature and humidity measurement for energy efficient building controls and operations. The proposed technology is based on the novel concept of all-digital sensing and its seamless integration with the passive RFID technology. This project focuses on the design, fabrication, material optimization, interrogation electronics, validation, and demonstration of the novel sensor nodes for building applications. The specific objectives of this research program include: (1) Design, fabricate and optimize a compact, robust, and high-resolution digitizer, which could convert rotation angle into digital numbers. (2) Develop the multi-physics-based modeling and simulation of the temperature/humidity transducer to derive a rational design of the architecture, dimension, structure, materials (i.e., mechanical, electrical, thermal and hygroscopic) properties and functions of the sensor node. (3) Design, fabricate and optimize the bi-material based humidity sensitive coil which could linearly transduce the environmental relative humidity variations to rotation angles. (4) Design, fabricate and optimize an UHF RFID platform which could support long-range passive wireless communications of 8-bit digital numbers. (5) Design, fabricate and optimize the miniaturized all-digital sensor using MEMS technology. (6) Validate the integrated all-digital sensing system in a real building environment to test the system’s performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

National Smart Manufacturing Strategic Plan: To Facilitate More Rapid Development, Deployment and Adoption of Smart Manufacturing Technologies

Smart manufacturing technologies provide real-time data and insight to improve the productivity, efficiency, and competitiveness of U.S. manufacturing, creating the potential for new jobs in the manufacturing sector. These technologies can support U.S. manufacturers’ ability to increase throughput and energy efficiency, and decrease waste, defects, and costs. A wide range of manufacturing and industrial subsectors, particularly energy-intensive and energy-dependent industries, have the potential to benefit from smart manufacturing technologies. There are technical improvements still needed to reduce the costs and barriers (trained workforce and upskilling, software-hardware integration, cost, and technical barriers to deployment of advanced sensors, computing, and communication technologies for existing manufacturing assets) to the adoption of smart manufacturing technologies, especially to small and medium-sized manufacturers, and subsequently, increase the overall adoption rate of smart manufacturing technologies. This report outlines the Department of Energy’s (DOE) strategic plan to accelerate the development and implementation of smart manufacturing technologies in the United States and the actions DOE has taken to use these technologies in smart manufacturing for the United States. DOE’s plan to facilitate more rapid development, deployment and widespread adoption of smart manufacturing technologies derive from the 2018 Strategy for American Leadership in Advanced Manufacturing. The strategy outlines a vision for America’s leadership in advanced manufacturing including the development of intelligent manufacturing systems to optimize manufacturing facilities and support the manufacturing of transformative materials. DOE’s Clean Energy Smart Manufacturing Innovation Institute (CESMII), a Manufacturing USA Institute, focuses on accelerating the development and adoption of advanced sensors, controls, platforms, and models needed for smart manufacturing. The objective is to enhance U.S. manufacturing productivity and global competitiveness through the research and development of these technologies. Smart manufacturing has the potential to improve the overall performance, energy productivity, and efficiency of manufacturing, fostering the economic competitiveness of the manufacturing sector.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

High-throughput studies of novel magnetic materials in borides

Borides are a versatile material family with various properties for valuable applications. Conventional magnetism, such as ferromagnetism and antiferromagnetism in borides, have been extensively studied. However, research on unconventional magnetism in borides where quantum effects are dominant is scarce. Here, we implement a high-throughput workflow combining first-principles calculations, materials prediction, and magnetic properties calculations to discover novel magnetism and magnetic materials in borides. Successfully applying the workflow, we report three families of novel magnetic borides, including two families of borides exhibiting quantum magnetism. One is a family of dimerized quantum magnets among YCrB 4 -type borides, which provides a rare platform for studying the spin-gap quantum critical point. The other is a family of altermagnets among FeMo2B 2 -type borides, extending the magnetic orderings exhibited by borides beyond conventional ferromagnetism and antiferromagnetism. We also predict a family of magnetic laminate transition metal borides, known as the MAB phases, in the AlFe 2 B 2 -type family, which provide pure-phase or alloying candidates for studying magnetocaloric materials and the associated magnetic transitions. The workflow is expected to be used in further studies of novel magnetism and magnetic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Temperature-driven BCS-BEC crossover and Cooper-paired metallic phase in coupled boson-fermion systems

Motivated by strongly correlated and frustrated systems, in this work we propose an effective model that describes the dynamics of pairs of opposite spin fermions scattering from localized bosons. Integrating out one of the degrees, either the bosons or fermions, generates temperature-dependent long-range effective interactions between the entities that we investigate using Monte Carlo techniques. The behavior of bosons is dominated by vortex-antivortex unbinding, with effective interboson interactions beyond the nearest-neighbor Josephson coupling of phases. Remarkably, in the fermion sector we observe a temperature-driven phase transition from a SC phase with a “BCS” spectral function that shows a gap minimum on the underlying Fermi surface to a conducting phase of pairs but with a “BEC” spectral function with a gap minimum at k = 0. Tunneling and angle-resolved photoemission spectroscopy on Bose-Fermi mixtures in cold atomic systems and superconducting islands on graphene are some of the promising experimental platforms to test our predictions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Superconductivity in electron-doped PbBi 2 ⁢Te 4

Single crystals of In-doped PbBi 2 ⁢Te 4 are synthesized via a conventional solid-state method. Chemical analysis and Hall measurements indicate that In replaces Pb, introducing n-type carriers, creating Pb 1–x ⁢In x ⁢Bi 2 ⁢Te 4 . A superconducting transition is observed with a maximum transition temperature around 2.06 K for Pb 1–x⁢ In x ⁢Bi 2 ⁢Te 4 . Field-dependent transport measurements reveal type-II superconductivity and yield a maximum upper critical field around 1.55 T. Thermodynamic data indicates bulk superconductivity in the BCS weak-coupling limit. Finally, our findings establish an ambient-pressure superconducting system in the AM 2 ⁢X 4 family, and doped PbBi 2 ⁢Te 4 as a promising platform for the study of topological superconductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Nonlinear Dispersion Relation and Out‐of‐Plane Second Harmonic Generation in MoSSe and WSSe Janus Monolayers

Janus transition metal dichalcogenides are an emerging class of atomically thin materials with engineered broken mirror symmetry that gives rise to long‐lived dipolar excitons, Rashba splitting, and topologically protected solitons. They hold great promise as a versatile nonlinear optical platform due to their broadband harmonic generation tunability, ease of integration on photonic structures, and nonlinearities beyond the basal crystal plane. Here, second and third harmonic generation in MoSSe and WSSe Janus monolayers is studied. Polarization‐resolved spectroscopy is used to map the full second‐order susceptibility tensor of MoSSe, including its out‐of‐plane components. In addition, the effective third‐order susceptibility and the second‐order nonlinear dispersion close to exciton resonances for both MoSSe and WSSe are measured at room and cryogenic temperatures. This work sets a bedrock for understanding the nonlinear optical properties of Janus transition metal dichalcogenides and probing their use in the next‐generation on‐chip multifaceted photonic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images

Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning meaningful image features that would enable extension to new datasets. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work, we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding, for example, the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required. With a focus on unique challenges that arise in high-resolution images, we propose methods for optimizing performance of image segmentation using convolutional neural networks, critically examining the application of complex deep learning models in favor of motivating intentional process design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Observation of stacking engineered magnetic phase transitions within moiré supercells of twisted van der Waals magnets

Abstract Recent demonstrations of moiré magnetism, featuring exotic phases with noncollinear spin order in the twisted van der Waals (vdW) magnet chromium triiodide CrI 3 , have highlighted the potential of twist engineering of magnetic (vdW) materials. However, the local magnetic interactions, spin dynamics, and magnetic phase transitions within and across individual moiré supercells remain elusive. Taking advantage of a scanning single-spin magnetometry platform, here we report observation of two distinct magnetic phase transitions with separate critical temperatures within a moiré supercell of small-angle twisted double trilayer CrI 3 . By measuring temperature-dependent spin fluctuations at the coexisting ferromagnetic and antiferromagnetic regions in twisted CrI 3 , we explicitly show that the Curie temperature of the ferromagnetic state is higher than the Néel temperature of the antiferromagnetic one by ~10 K. Our mean-field calculations attribute such a spatial and thermodynamic phase separation to the stacking order modulated interlayer exchange coupling at the twisted interface of moiré superlattices.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Highly Anisotropic Quasi‐Direct Organic Metal Halide Hybrids: A Platform for Polarization‐Sensitive Optoelectronics

Low-dimensional organic–inorganic metal halide hybrids (OMHHs) exhibit remarkable optical properties and enhanced environmental stability. We investigate a 1D OMHH with formula C 4 N 2 H 14 PbBr 4 , consisting of Pb–Br chains separated by organic cations, which shows a large Stokes shift (0.83 eV) and broadband emission. Through first-principles calculations and polarized Raman spectroscopy, we characterize the material's vibrational properties and identify the specific phonon modes that drive exciton self-trapping. Our novel GW/Bethe-Salpeter equation force formalism reveals that low-frequency phonons (∼ 100 cm −1 , primarily involving Pb–Br motions) couple strongly with excitons, with a remarkably high Huang-Rhys factor of 137 ± 4, and gives a pathway for ultrafast structural analysis during the absorption process. This phonon-exciton coupling mechanism explains the material's broadband emission and provides a pathway for controlling optical properties through vibrations and for tuning vibrations through optical excitations. The material also exhibits highly anisotropic optical properties and electronic transport, with bands that are dispersive along the Pb–Br chains but nearly flat in perpendicular directions, resulting in direction-dependent electrical conductivity that is calculated to be an order of magnitude higher along the chain direction and consistent with measurements. These combined properties make this system an excellent platform for polarization-sensitive optoelectronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Mechanical properties of freestanding few-layer graphene/boron nitride/polymer heterostacks investigated with local and non-local techniques

van der Waals two-dimensional materials and heterostructures combined with polymer films continue to attract research attention to elucidate their functionality and potential applications. This study presents the fabrication and mechanical testing of 2D material heterostacks, consisting of few-layer boron nitride and graphene heterostructures synthesized via chemical vapor deposition, capped with a polymethyl methacrylate layer and suspended across ∼200 μm wide trenches using a combined wet–dry transfer method. The mechanical characterization of the heterostacks was performed using two independent approaches: (a) non-local testing with a custom-built tensile testing platform and (b) local load–displacement testing employing atomic force microscopy probes, complemented by finite element simulations. Both approaches provided new results, which are in good agreement with each other. Overall, our findings offer new insights into a combined load capacity in complex multi-material two-dimensional systems, and can contribute to advancing micro and nano-scale device designs and implementations.

Lespasio, Marcus↗
DOE OSTI2024

Fiber Sorbents – A Versatile Platform for Sorption-Based Gas Separations

Increasing demand for high-purity fine chemicals and a drive for process intensification of large-scale separations have driven significant work on the development of highly engineered porous materials with promise for sorption-based separations. While sorptive separations in porous materials offer energy-efficient alternatives to longstanding thermal-based methods, the particulate nature of many of these sorbents has sometimes limited their large-scale deployment in high-throughput applications such as gas separations, for which the necessary high feed flow rates and gas velocities accrue prohibitive operational costs. These processability limitations have been historically addressed through powder shaping methods aimed at the fabrication of structured sorbent contactors based on pellets, beads or monoliths, commonly obtained as extrudates. These structures overcome limitations such as elevated pressure drops commonly recorded across powder adsorption beds but often accrue thermal limitations arising from elevated particle density and aggregation, which ultimately cap their maximum separation performance. Furthermore, the harsh mechanical strain to which powder particles are subjected during contactor fabrication, in the form of extrusion/compression forces, can result in partial pore occlusion and framework degradation, further limiting their performance. Here, we present the development of porous fiber sorbents as an alternative sorbent contactor design capable of addressing sorbent processability limitations while enabling an array of performance-maximizing heat integration capabilities. This new sorbent form factor leverages pre-existing know-how from hollow fiber spinning to produce fiber-shaped sorbent contactors through the phase inversion of known polymers in a process known as dry-jet/wet quenching. The process of phase inversion allows microporous sorbent particles to be latched onto a macroporous polymer matrix under mild processing conditions, thus making it compatible with soft porous materials prone to amorphization under traditional pelletization conditions. Sorbent fibers can be created with different geometries through control of the spinning apparatus and process, offering the possibility to produce monolithic and hollow fibers alike, the latter of which can be integrated with thermalization fluid flows. In this Account, we summarize our progress in the field of fiber sorbents from both design and application standpoints. We further guide the reader through the evolution of this field from the early inceptive work on zeolite hollow fibers to recent developments on MOF fibers. We highlight the versatile nature of fiber sorbents, both from the composition, fabrication and structure points of view, and further demonstrate how fiber sorbents offer alternative paths in tackling new and challenging chemical separation challenges like direct air capture (DAC), with a final perspective on the future of the field.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Model-based predictive control of multi-stage air-source heat pumps integrated with phase change material-embedded ceilings

This paper presents a model-based predictive control strategy to optimize the operations of phase change material (PCM) ceiling panels coupled with a multi-stage air-source heat pump. A three-stage prototype heat pump unit has been built and tested in the laboratory, with the low and medium stages designed for space heating/cooling and the high compression stage dedicated to charging of the PCM energy storage. To facilitate optimal control of the integrated heat pump system, a mixed-integer linear programming formulation is derived through linearization of the heat pump model and a mixed-integer reformulation of the PCM dynamic governing equations. A predictive control strategy is synthesized based on the resultant control formulation and implemented in a receding horizon scheme that optimizes the PCM charging and the zone temperature schedules simultaneously to leverage both the passive (associated with building construction materials) and active (PCM) storage capacities of a building. The control strategy has been tested along with three benchmarking control scenarios using a co-simulation platform for a prototypical detached house in Atlanta, GA. In this study, test results showed that application of the proposed control strategy to the PCM-integrated heat pump could provide 27.1% electricity cost savings while a fine tuned rule-based control strategy could achieve cost savings of 20.4%, compared to a baseline case without PCM storage, under a time-of-use rate tariff.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Chiral limit and origin of topological flat bands in twisted transition metal dichalcogenide homobilayers

Abstract The observation of zero field fractional quantum Hall analogs in twisted transition metal dichalcogenides (TMDs) asks for a deeper understanding of what mechanisms lead to topological flat bands in two-dimensional heterostructures, and what makes TMDs an excellent platform for topologically ordered phases, surpassing twisted bilayer graphene. To this aim, we explore the chiral limits of massive Dirac theories applicable toC 3 -symmetric moiré materials, and show their relevance for both bilayer graphene and TMD homobilayers. In the latter, the Berry curvature of valence bands leads to relativistic corrections of the moiré potential that promote band flattening, and permit a limit with exactly flat bands with nonzero Chern number. The relativistic corrections enter as alayer-orbit coupling, analogous to spin-orbit coupling for relativistic Dirac fermions, which we show is non-negligible on the moiré scale. The Berry curvature of the TMD monolayers therefore plays an essential role in the flattening of moiré Chern bands in these heterostructures.

Physics↗
DOE OSTI2020

Pulsed-laser epitaxy of metallic delafossite PdCrO 2 films

Alternate stacking of a highly conducting metallic layer with a magnetic triangular layer found in delafossite PdCrO 2 provides an excellent platform for discovering intriguing correlated quantum phenomena. Thin film growth of delafossites may enable not only the tuning of the basic physical properties beyond what bulk materials can exhibit, but also the development of novel hybrid materials by interfacing with dissimilar materials, yet this has proven to be extremely challenging. Here, we report the epitaxial growth of metallic delafossite PdCrO 2 films by pulsed laser epitaxy (PLE). The fundamental role of the PLE growth conditions, epitaxial strain, and chemical and structural characteristics of the substrate is investigated by growing under various growth conditions and on various types of substrates. While strain plays a large role in improving the crystallinity, the direct growth of epitaxial PdCrO 2 films without impurity phases was not successful. We attribute this difficulty to both the chemical and structural dissimilarities with the substrate and volatile nature of the PdO sublayer, which make nucleation of the right phase difficult. This difficulty was overcome by growing CuCrO 2 buffer layers before PdCrO 2 films were grown. Unlike PdCrO 2 , CuCrO 2 films were readily grown with a relatively wide growth window. Only a monolayer thick buffer layer was sufficient to grow the correct PdCrO 2 phase. This result indicates that the epitaxy of Pd-based delafossites is extremely sensitive to the chemistry and structure of the interface, necessitating near perfect substrate materials. The resulting films are commensurately strained and show an antiferromagnetic transition at 40 K that persists down to as thin as 3.6 nm in thickness. This work provides key insights into advancing the epitaxial growth of the broader class of metallic delafossites for both studying the basic physical properties and developing new spintronic and computing devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Unit-density SU(3) Fermi-Hubbard model with spin-flavor imbalance

The advent of ultracold alkaline-earth atoms in optical lattices has established a platform for investigating correlated quantum matter with SU⁡(𝑁) symmetry, offering highly tunable model parameters that allow experiments to access phenomena that are unavailable in conventional materials. Understanding the ground-state physics of SU⁡(𝑁) Fermi-Hubbard models away from the Heisenberg limit and from the spin-flavor balanced setting is important, as examining the flavor imbalance reveals new physics in Fermi-Hubbard models and shows how SU⁡(𝑁) phases react to practical experimental imperfections in optical lattices. Here, in this study, mean-field phase diagrams are presented for the unit-density SU(3) Fermi-Hubbard model at two sets of flavor densities, ($\frac{1}{3}$ − 𝛿, $\frac{1}{3}$ + 𝛿, $\frac{1}{3}$) and ($\frac{1}{4}$ − 𝛿, $\frac{1}{4}$ +𝛿, $\frac{1}{2}$), with the flavor imbalance introduced as 𝛿. Novel phases are identified at moderate interaction strengths for both densities, and their robustness is investigated in the presence of flavor imbalance. Furthermore, we provide microscopic explanations of the phases found and their stability. Analysis of thermal ensembles of random mean-field solutions indicate that, at temperatures accessible in state-of-the-art cold atom experiments, some spin orders are hard for conventional scattering or local observable measurements to detect, but can be more accessible with quantum gas microscopy in optical lattice experiments. This work also shows that nesting and Mottness, intertwined in the usual SU(2) Hubbard model in stark contrast to generic materials, can be tuned in the SU(3) model and play distinct roles. The resulting phase diagrams not only deepen our understanding of SU⁡(𝑁) Fermi-Hubbard models but also inform future experimental search for new phases.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Stabilizing and Improving Qubit Coherence by Engineering the Noise Spectrum of Two-Level Systems

Superconducting circuits are a leading platform for quantum computing. However, their coherence times are still limited and exhibit temporal fluctuations. Those phenomena are often attributed to the coupling between qubits and material defects that can be well described as an ensemble of two-level systems (TLSs). Among them, charge fluctuators inside amorphous oxide layers contribute to both low-frequency 1 / f charge noise and high-frequency dielectric loss, causing fast qubit dephasing and relaxation. Moreover, spectral diffusion from mutual TLS interactions varies the noise amplitude over time, fluctuating the qubit lifetime. Here, we propose to mitigate those harmful effects by engineering the relevant TLS noise spectral densities. Specifically, our protocols smooth the high-frequency noise spectrum and suppress the low-frequency noise amplitude via depolarizing and dephasing the TLSs, respectively. As a result, we predict a drastic stabilization in qubit lifetime and an increase in qubit pure dephasing time. Our detailed analysis of feasible experimental implementations shows that the improvement is not compromised by spurious coupling from the applied noise to the qubit.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Integration of Kokkos into MonteRay [Slides]

Monte Carlo Neutron Transport codes are C++-based and simulate interaction of nuclear particles with materials. MonteRay is a library for accelerating Monte Carlo ray-casting tallies with GPUs. Kokkos is commonly used to write performance portable applications for HPC platforms. A goal is to implement Kokkos into the Expected Path Length file that uses Cuda as a backend. The methodology and results are summarized.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗