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DOE OSTI2025

Magnetic Solitons and Thickness‐Dependent Magnetization Reversal in Interconnected Helical Nanowire Arrays

By expanding magnetic nanostructures into the third dimension, it is possible to introduce new interactions and realize new forms of magnetic textures and emergent phenomena. Consequently, this unlocks new opportunities for applications in data storage, unconventional computing and sensing by utilizing 3D devices with enhanced functionalities. Connected magnetic nanowires offer a unique platform for applications such as neuromorphic computing due to their tunability and the presence of multiple transport pathways. However to realize this promise, it is necessary to further our understanding of how to locally control the magnetization in 3D, nanowire-based geometries. In this work we show the formation of magnetic domain walls, vortices, anti-vortices, and linked vortex-anti-vortex pairs in interconnected helical nanowire arrays. We show how wire diameter and 3D geometric design can control the states that form and reveal the magnetization reversal mechanism. Hence, we demonstrate this to be a highly tunable system, where the magnetization can be readily reconfigured by an external magnetic field.

3D Nanomagnetism↗

DOE OSTI2021

Cation and anion topotactic transformations in cobaltite thin films leading to Ruddlesden-Popper phases.

Topotactic transformations involve structural changes between related crystal structures due to a loss or gain of material while retaining a crystallographic relationship. The perovskite oxide La0.7Sr0.3CoO3 (LSCO) is an ideal system for investigating phase transformations due to its high oxygen vacancy conductivity, relatively low oxygen vacancy formation energy, and strong coupling of the magnetic and electronic properties to the oxygen stoichiometry. While the transition between cobaltite perovskite and brownmillerite (BM) phases has been widely reported, further reduction beyond the BM phase lacks systematic studies. In this paper, we study the evolution of the physical properties of LSCO thin films upon exposure to highly reducing environments. We observe the rarely reported crystalline Ruddlesden-Popper phase, which involves the loss of both oxygen anions and cobalt cations upon annealing where the cobalt is found as isolated Co ions or Co nanoparticles. First-principles calculations confirm that the concurrent loss of oxygen and cobalt ions is thermodynamically possible through an intermediary BM phase. The strong correlation of the magnetic and electronic properties to the crystal structure highlights the potential of utilizing ion migration as a basis for emerging applications such as neuromorphic computing.

Chiu, I-Ting↗

DOE OSTI2017

Sensitivity Coefficients for Diffusion Coefficients and Other Reactor Physics Parameters using CE TSUNAMI-3D

The TSUNAMI (Tools for Sensitivity and Uncertainty Analysis Methodology Implementation) capabilities within the SCALE code system leverage sensitivity coefficients for many criticality safety applications, including quantifying the data-induced uncertainty in the eigenvalue of critical systems, quantifying computational biases, and selecting applicable benchmark experiments. TSUNAMI sensitivity and uncertainty analysis tools have traditionally been limited to criticality safety application spaces, but the introduction of Generalized Perturbation Theory (GPT) sensitivity methods by Jessee in 2009 and Perfetti in 2013 has extended the scope of SCALE adjoint-weighted sensitivity and uncertainty analysis to include reactor physics applications. The continuous-energy (CE) TSUNAMI-3D GPT sensitivity analysis methods were originally developed to calculate sensitivity coefficients for a limited number of reaction-rate responses using CE Monte Carlo methods. These tools were recently applied in an Oak Ridge National Laboratory (ORNL)-directed research and development project to improve the efficiency of {sup 252}Cf production in the High Flux Isotope Reactor (HFIR) by understanding the sensitivity of key heavy actinide capture-to- fission ratios to potential system design changes. This work further extends the applicability of the CE TSUNAMI- 3D GPT methods to reactor physics applications by enabling the calculation of sensitivity coefficients for an expanded set of reaction rate responses-including a first-ever capability for conducting sensitivity analysis of neutron diffusion coefficients.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

DEVELOPMENT OF A VERIFICATION BENCHMARK SUITE FOR THE ATR REACTOR PHYSICS UPGRADE

The result of a numerical algorithm is only as good as the effort made toward its verification. Comprehensive computer codes, especially for application to nuclear reactors, require in-depth assessment of the numerical methods used and their computational implementation. This presentation describes a suite of semi-analytical benchmarks for the ATR Reactor Physics Upgrade as part of the ATR Life Extension Project (LEP). Several neutron transport codes including deterministic codes HELIOS, NEWT and probabilistic codes MCNP6, SERPANT and MC21 are upgrade candidates. To properly evaluate code performance capabilities, code verification is an essential element. A credible verification program requires education to guide proper application, training in proper input preparation and output expectation and verification to instill confidence in the coded numerical procedures. The subset of the benchmark suite to be described here is primarily of the 1D transport variety (however, a 2D case does indeed exist) with the intent to provide unquestionably high order accurate semi-analytical numerical solutions to the transport equation. We base the analytical solutions on analytical Fourier series and discrete ordinates representations. Here we present two 1D benchmarks based on Fourier series.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

A Brief Overview of Radiochemistry: Californium-252: Unique and Versatile Man-made Radioisotope

This chapter summarizes radiochemistry and its applications with a focus on areas where computational science can be beneficial. This is presented for a target audience of computational scientists and chemists, and the general public who are interested in radiochemistry topics that may benefit from the use of computational methods. Past, present, and future applications of radiochemistry are presented along with a brief examination of the field’s workforce. Discussions of the current state of several radiochemistry specialty areas are also included. These discussions generally exemplify topics that are readily amenable to computational tools. Topics discussed include medical uses for radioactive materials and related computational needs; the current and future trends in the separations of the f-block elements; production and uses of Californium-252; and the need for exascale computing to solve current problems in the chemistry of Americium and Plutonium.

Ezold, Julie↗

DOE OSTI2021

A Brief Overview of Radiochemistry: f-Element Separations: The State of the Art and Future Directions

Kimberlin, Ashleigh↗

DOE OSTI2020

ECP ST Project 2.3.1.06-STPM08-RAJA (Final Report)

Modern high-performance computing architectures are diverse, with hardware designs that feature multicore CPUs and CPUs plus accelerators, such as GPUs or FPGAs. Achieving good application performance often requires using a programming model and managing application data in ways that are best suited to a particular platform. For large codes that are used daily in production, and are under continual development, architecture-specific ports are untenable. Maintainability requires single-source code that is performance portable across a range of architectures and programming model back-ends. This report describes RAJA, Umpire, and CHAI, three software libraries that provide complementary capabilities to enable application portability for high performance computing (HPC) platforms. In particular, these libraries provide software abstractions that insulate application source code from hardware architecture and programming model-specific implementation details. These development of these libraries was motivated by the needs of multiphysics applications in the Weapons Simulation and Computing Program (WSC) at Lawrence Livermore National Laboratory (LLNL). Funding though the Exascale Computing Project (ECP) Software Technology (ST) project 2.3.1.06 RAJA has fostered outreach and collaboration with the ECP application and software technology development communities. In this report, we describe ECP adoption and integration of these tools to this point. The larger goal of the report is to describe to the ECP community what these projects offer, how they are used, and how to approach adoption and integration of them.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

WE-B-BRD-01: Innovation in Radiation Therapy Planning II: Cloud Computing in RT

As defined by the National Institute of Standards and Technology, cloud computing is “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.” Despite the omnipresent role of computers in radiotherapy, cloud computing has yet to achieve widespread adoption in clinical or research applications, though the transition to such “on-demand” access is underway. As this transition proceeds, new opportunities for aggregate studies and efficient use of computational resources are set against new challenges in patient privacy protection, data integrity, and management of clinical informatics systems. In this Session, current and future applications of cloud computing and distributed computational resources will be discussed in the context of medical imaging, radiotherapy research, and clinical radiation oncology applications. Learning Objectives: Understand basic concepts of cloud computing. Understand how cloud computing could be used for medical imaging applications. Understand how cloud computing could be employed for radiotherapy research.4. Understand how clinical radiotherapy software applications would function in the cloud.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

A hierarchal model for bacterial cell inactivation in solution by direct and indirect treatment using cold atmospheric plasmas

Cold atmospheric plasma devices have shown promise for a variety of plasma medical applications, including wound healing and bacterial inactivation often performed in liquids. In the latter application, plasma-produced reactive oxygen and nitrogen species (RONS) interact with and damage bacterial cells, though the exact mechanism by which cell damage occurs is unclear. Computational models can help elucidate relationships between plasma-produced RONS and cell killing by enabling direct comparison between dissimilar plasma devices and by examining the effects of changing operating parameters in these devices. In biological applications, computational models of plasma-liquid interactions would be most effective in design and optimization of plasma devices if there is a corresponding prediction of the biological outcome. In this work, we propose a hierarchal model for planktonic bacterial cell inactivation by plasma produced RONS in liquid. A previously developed reaction mechanism for plasma induced modification of cysteine was extended to provide a basis for cell killing by plasma-produced RONS. Results from the model are compared to literature values to provide proof of concept. Differences in time to bacterial inactivation as a function of plasma operating parameters including gas composition and plasma source configuration are discussed. Results indicate that optimizing gas-phase reactive nitrogen species production may be key in the design of plasma devices for disinfection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Measurement and applications: Exploring the challenges and opportunities of hierarchical federated learning in sensor applications

Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delays [2], [3].

Po-Leen Ooi, Melanie↗

DOE OSTI2016

High-Throughput Computational Screening of Perovskites for Thermochemical Water Splitting Applications

The use of hydrogen as fuel is a promising avenue to aid in the reduction of greenhouse effect gases released in the atmosphere. In this work, we present a high-throughput density functional theory (HT-DFT) study of 5,329 cubic and distorted perovskite ABO 3 compounds to screen for thermodynamically favorable two-step thermochemical water splitting (TWS) materials. From a data set of more than 11,000 calculations, we screened materials based on the following: (a) thermodynamic stability and (b) oxygen vacancy formation energy that allow favorable TWS. From our screening strategy, we identify 139 materials as potential new candidates for TWS application. Several of these compounds, such as CeCoO 3 and BiVO 3 , have not been experimentally explored yet for TWS and present promising avenues for further research. We show that taking into consideration all phases present in the A–B–O ternary phase, as opposed to only calculating the formation energy of a compound, is crucial to assess correctly the stability of a compound as it reduces the number of potential candidates from 5,329 to 383. Finally, our large data set of compounds containing stabilites, oxidation states, and ionic sizes allowed us to revisit the structural maps for perovskites by showing stable and unstable compounds simultaneously.

08 HYDROGEN↗

DOE OSTI2022

Computing Thermodynamic Properties of Fluids Augmented by Nanoconfinement: Application to Pressurized Methane

Nanoconfined fluids exhibit remarkably different thermodynamic behavior compared to the bulk phase. These confinement effects render predictions of thermodynamic quantities of nanoconfined fluids challenging. In particular, confinement creates a spatially varying density profile near the wall that is primarily responsible for adsorption and capillary condensation behavior. Significant fluctuations in thermodynamic quantities, inherent in such nanoscale systems, coupled to strong fluid–wall interactions give rise to this near-wall density profile. Empirical models have been proposed to explain and model these effects, yet no first-principles based formulation has been developed. We present a statistical mechanics framework that embeds such a coupling to describe the effect of the fluid–wall interaction in amplifying the near-wall density behavior for compressible gases at elevated pressures such as pressurized methane in confinement. We show that the proposed theory predicts accurately the adsorbed layer thickness as obtained with small-angle neutron scattering measurements. Furthermore, the predictions of density under confinement from the proposed theory are shown to be in excellent agreement with available experimental and atomistic simulations data for a range of temperatures for nanoconfined methane. While the framework is presented for evaluating the near-wall density, owing to its rigorous foundation in statistical mechanics, the proposed theory can also be generalized for predicting phase-transition and nonequilibrium transport of nanoconfined fluids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Low temperature CVD growth of WSe 2 enabled by moisture-assisted defects in the precursor powder

Two-dimensional transition metal dichalcogenides (TMDs) have been proposed for a wide variety of applications, such as neuromorphic computing, flexible field effect transistors, photonics, and solar cells, among others. However, for most of these applications to be feasible, it is necessary to integrate these materials with the current existing silicon technology. Although chemical vapor deposition is a promising method for the growth of high-quality and large-area TMD crystals, the high temperatures necessary for the growth make this technique incompatible with the processes used in the semiconductor industry. Herein, we demonstrate the possibility of low-temperature growth of TMDs, using tungsten selenide (WSe 2 ) as a model, by simply using moisture-assisted defective tungsten oxide (WO 3 ) precursor powders during the growth of these materials. Density functional theory calculations reveal the mechanism by which moisture promotes the defect formation on the precursor crystal structure and how it dictates the reduction of the temperature of the growth. The results were compared with the standard growth at high temperatures and with a precursor mixture with alkali salts to show the high quality of the WSe 2 grown at temperatures as low as 550 °C. To conclude, the work improves the understanding of nucleation and growth mechanisms of WSe 2 at low temperatures and provides a useful strategy for the growth of TMDs at temperatures required for the back-end-of-line compatibility with current silicon technology.

2D materials↗

DOE OSTI2018

Computational investigation of inverse Heusler compounds for spintronics applications

In this work, first-principles calculations of the electronic structure, magnetism, and structural stability of inverse Heusler compounds with the chemical formula X 2 YZ are presented and discussed with a goal of identifying compounds of interest for spintronics. Compounds for which the number of electrons per atom for Y exceed that for X and for which X and Y are each one of the 3d elements, Sc-Zn, and Z is one of the group IIIA-VA elements: Al, Ga, In, Si, Ge, Sn, P, As, or Sb were considered. The formation energy per atom of each compound was calculated. By comparing our calculated formation energies to those calculated for phases in the inorganic crystal structure database of observed phases, we estimate that inverse Heuslers with formation energies within 0.052 eV/atom of the calculated convex hull are reasonably likely to be synthesizable in equilibrium. The observed trends in the formation energy and relative structural stability as the X, Y, and Z elements vary are described. In addition to the Slater-Pauling gap after 12 states per formula unit in one of the spin channels, inverse Heusler phases often have gaps after 9 states or 14 states. We describe the origin and occurrence of these gaps. We identify 14 inverse Heusler semiconductors, 51 half-metals, and 50 near-half-metals with negative formation energy. Additonally, our calculations predict 4 half-metals and 6 near-half-metals to lie close to the respective convex hull of stable phases, and thus may be experimentally realized under suitable synthesis conditions, resulting in potential candidates for future spintronics applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2014

ARCHER{sub RT} – A GPU-based and photon-electron coupled Monte Carlo dose computing engine for radiation therapy: Software development and application to helical tomotherapy

Purpose: Using the graphical processing units (GPU) hardware technology, an extremely fast Monte Carlo (MC) code ARCHER{sub RT} is developed for radiation dose calculations in radiation therapy. This paper describes the detailed software development and testing for three clinical TomoTherapy® cases: the prostate, lung, and head and neck. Methods: To obtain clinically relevant dose distributions, phase space files (PSFs) created from optimized radiation therapy treatment plan fluence maps were used as the input to ARCHER{sub RT}. Patient-specific phantoms were constructed from patient CT images. Batch simulations were employed to facilitate the time-consuming task of loading large PSFs, and to improve the estimation of statistical uncertainty. Furthermore, two different Woodcock tracking algorithms were implemented and their relative performance was compared. The dose curves of an Elekta accelerator PSF incident on a homogeneous water phantom were benchmarked against DOSXYZnrc. For each of the treatment cases, dose volume histograms and isodose maps were produced from ARCHER{sub RT} and the general-purpose code, GEANT4. The gamma index analysis was performed to evaluate the similarity of voxel doses obtained from these two codes. The hardware accelerators used in this study are one NVIDIA K20 GPU, one NVIDIA K40 GPU, and six NVIDIA M2090 GPUs. In addition, to make a fairer comparison of the CPU and GPU performance, a multithreaded CPU code was developed using OpenMP and tested on an Intel E5-2620 CPU. Results: For the water phantom, the depth dose curve and dose profiles from ARCHER{sub RT} agree well with DOSXYZnrc. For clinical cases, results from ARCHER{sub RT} are compared with those from GEANT4 and good agreement is observed. Gamma index test is performed for voxels whose dose is greater than 10% of maximum dose. For 2%/2mm criteria, the passing rates for the prostate, lung case, and head and neck cases are 99.7%, 98.5%, and 97.2%, respectively. Due to specific architecture of GPU, modified Woodcock tracking algorithm performed inferior to the original one. ARCHER{sub RT} achieves a fast speed for PSF-based dose calculations. With a single M2090 card, the simulations cost about 60, 50, 80 s for three cases, respectively, with the 1% statistical error in the PTV. Using the latest K40 card, the simulations are 1.7–1.8 times faster. More impressively, six M2090 cards could finish the simulations in 8.9–13.4 s. For comparison, the same simulations on Intel E5-2620 (12 hyperthreading) cost about 500–800 s. Conclusions: ARCHER{sub RT} was developed successfully to perform fast and accurate MC dose calculation for radiotherapy using PSFs and patient CT phantoms.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

High-performance finite elements with MFEM

The MFEM (Modular Finite Element Methods) library is a high-performance C++ library for finite element discretizations. MFEM supports numerous types of finite element methods and is the discretization engine powering many computational physics and engineering applications across a number of domains. Furthermore, this paper describes some of the recent research and development in MFEM, focusing on performance portability across leadership-class supercomputing facilities, including exascale supercomputers, as well as new capabilities and functionality, enabling a wider range of applications. Much of this work was undertaken as part of the Department of Energy’s Exascale Computing Project (ECP) in collaboration with the Center for Efficient Exascale Discretizations (CEED).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Data Driven Smart Proxy for CFD: Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics (Part 1)

Simulation technologies can reduce the time and cost of the development and deployment of advanced technologies and allow rapid scale-up of these technologies for fossil fuel based energy systems. However, to ensure their usefulness in practice, the credibility of the simulations needs to be established with Uncertainty Quantification (UQ) methods. National Energy Technology Laboratory (NETL) has been applying non-intrusive UQ methodologies to categorize and quantify uncertainties in CFD simulations of gas-solid multiphase flows. To reduce the computational cost associated with gas-solid flow simulations required for UQ analysis, techniques commonly used in the area of Artificial Intelligence (AI) and Data Mining (DM) are used to construct smart proxy models, which can reduce the computational cost of conducting large number of multiphase CFD simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Report Two: Model Building at the Cell Level

To ensure the usefulness of simulation technologies in practice, their credibility needs to be established with Uncertainty Quantification (UQ) methods. In this project, smart proxy is introduced to significantly reduce the computational cost of conducting large number of multiphase CFD simulations, which is typically required for non-intrusive UQ analysis. Smart proxy for CFD models are developed using pattern recognition capabilities of Artificial Intelligence (AI) and Data Mining (DM) technologies. Several CFD simulation runs with different inlet air velocities for a rectangular fluidized bed are used to create a smart CFD proxy that is capable of replicating the CFD results for the entire geometry and inlet velocity range. The smart CFD proxy is validated with blind CFD runs (CFD runs that have not played any role during the development of the smart CFD proxy). The developed and validated smart CFD proxy generates its results in seconds with reasonable error (less than 10%). Upon completion of this project, UQ studies that rely on hundreds or thousands of smart CFD proxy runs can be accomplished in minutes. Following figure demonstrates a validation example (blind CFD run) showing the results from the MFiX simulation and the smart CFD proxy for pressure distribution across a fluidized bed at a given time-step (the layer number corresponds to the vertical location in the bed).

97 MATHEMATICS AND COMPUTING↗