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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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This work demonstrates the applicability of partially Bayesian neural networks (PBNNs) to active and transfer learning within materials science and chemistry domains, combining reliable uncertainty quantification (UQ) with NN flexibility.
Computer simulation plays a central role in modern-day materials science. The utility of a given computational approach depends largely on the balance it provides between accuracy and computational cost. Molecular crystals are a class of materials of great technological importance which are challenging for even the most sophisticated ab initio electronic structure theories to accurately describe. This is partly because they are held together by a balance of weak intermolecular forces but also because the primitive cells of molecular crystals are often substantially larger than those of atomic solids. Here, we demonstrate that diffusion quantum Monte Carlo (DMC) delivers subchemical accuracy for a diverse set of molecular crystals at a surprisingly moderate computational cost. As such, we anticipate that DMC can play an important role in understanding and predicting the properties of a large number of molecular crystals, including those built from relatively large molecules which are far beyond reach of other high-accuracy methods.
Nb 3 Sn offers significant potential to exceed the performance of niobium for superconducting radio frequency accelerator cavities. The most promising path toward deployment is by tin vapor diffusion coating of Nb cavity interiors via a two step nucleation-then-growth sequence. Reported here is a materials science study of the nucleation process. We manipulated the accessible range of process variables and determined the effect on composition and microstructure using an array of materials characterization tools. Broadly, nucleation deposits tin as a thin surface phase and, under some conditions, as near-micron sized particles as well, resembling Stranski–Krastanov growth. Conditions that impair nucleation promote the formation of defects, such as patches, in subsequent coating growth. Furthermore no significant effect on the subsequently grown coating was found for structures produced during nucleation.
Rapid progress in noisy intermediate-scale quantum (NISQ) computing technology has led to the development of novel resource-efficient hybrid quantum-classical algorithms, such as the variational quantum eigensolver (VQE), that can address open challenges in quantum chemistry, physics, and material science. Proof-of-principle quantum chemistry simulations for small molecules have been demonstrated on NISQ devices. While several approaches have been theoretically proposed for correlated materials, NISQ simulations of interacting periodic models on current quantum devices have not yet been demonstrated. Here, we develop a hybrid quantum-classical simulation framework for correlated electron systems based on the Gutzwiller variational embedding approach. We implement this framework on Rigetti quantum processing units (QPUs) and apply it to the periodic Anderson model, which describes a correlated heavy electron band hybridizing with noninteracting conduction electrons. Our simulation results quantitatively reproduce the known ground state quantum phase diagram including metallic, Kondo and Mott insulating phases. This is the first fully self-consistent hybrid quantum-classical simulation of an infinite correlated lattice model executed on QPUs, demonstrating that the Gutzwiller hybrid quantum-classical embedding framework is a powerful approach to simulate correlated materials on NISQ hardware. This benchmark study also puts forth a concrete pathway towards practical quantum advantage on NISQ devices.
On April 6-8, 2021, Sandia National Laboratories hosted a virtual workshop to explore the potential for developing AI-Enhanced Co-Design for Next-Generation Microelectronics (AICoM). The workshop brought together two themes. The first theme was articulated in the 2018 Department of Energy Office of Science (DOE SC) “Basic Research Needs for Microelectronics” (BRN) report, which called for a “fundamental rethinking” of the traditional design approach to microelectronics, in which subject matter experts (SMEs) in each microelectronics discipline (materials, devices, circuits, algorithms, etc.) work near-independently. Instead, the BRN called for a non-hierarchical, egalitarian vision of co-design, wherein “each scientific discipline informs and engages the others” in “parallel but intimately networked efforts to create radically new capabilities.” The second theme was the recognition of the continuing breakthroughs in artificial intelligence (AI) that are currently enhancing and accelerating the solution of traditional design problems in materials science, circuit design, and electronic design automation (EDA).
As a successful completion of this award, my group has demonstrated world-leading electron gain from diamond for use in a diamond-amplified photocathode. Also, using high-resolution photoemission measurements we were able to uncover exciting new physics of the electron emission mechanisms from hydrogen terminated diamond. Our work, through the continued support of HEP, has resulted in a greater understanding of the diamond material science, including current limits, charge transport modeling, and spatial uniformity.
The mission of the Nuclear Energy Enabling Technologies (NEET) program is to develop crosscutting technologies for nuclear energy applications. Advanced structural materials with superior performance at elevated temperatures are always desired for nuclear reactors, which can improve reactor economics, safety margins, and design flexibility. They benefit not only new reactors, including advanced light water reactors (LWRs) and fast reactors such as sodium-cooled fast reactor (SFR) that is primarily designed for management of high-level wastes, but also life extension of the existing fleet when component exchange is needed. Developing and utilizing the modern materials science tools (experimental, theoretical, and computational tools) is an important path to more efficient alloy development and process optimization. Ferritic-martensitic (FM) steels are important structural materials for nuclear reactors due to their advantages over other applicable materials like austenitic stainless steels, notably their resistance to void swelling, low thermal expansion coefficients, and higher thermal conductivity. However, traditional FM steels exhibit a noticeable yield strength reduction at elevated temperatures above ~500°C, which limits their applications in advanced nuclear reactors which target operating temperatures at 650°C or higher. Although oxide-dispersion-strengthened (ODS) ferritic steels have shown excellent high-temperature performance, their extremely high cost, limited size and fabricability of products, as well as the great difficulty with welding and joining, have limited or precluded their commercial applications. Zirconium has shown many benefits to Fe-base alloys such as grain refinement, improved phase stability, and reduced radiation-induced segregation. The ultimate goal of this project is, with the aid of computational modeling tools, to accelerate the development of a new generation of Zr-bearing ferritic alloys to be fabricated using conventional steelmaking practices, which have excellent radiation resistance and enhanced high-temperature creep performance greater than Grade 91.
This report describes literature regarding the degradation mechanisms associated with a poly(ester urethane) block copolymer, Estane® 5703 (Estane), used in conjunction with Nitroplasticizer (NP), and 1,3,5,7-tetranitro-1,3,5,7-tetrazocane, also known as high molecular weight explosive (HMX) to produce polymer bonded explosive PBX 9501. Two principal degradation mechanisms are reported: NO2 oxidative reaction with the urethane linkage resulting in crosslinking and chain scission events, and acid catalyzed hydrolysis of the ester linkage. This report details future work regarding this PBX support system, to be conducted in late 2017 and 2018 at Engineered Materials Group (MST-7), Materials Science and Technology Division, Los Alamos National Laboratory. This is the first of a series of three reports on the degradation processes and trends of the support materials of PBX 9501.
today, more than ever before, means must be found to apply the full power of modern materials science to the important problem of efficient and environmentally friendly energy conversion devices.
The question of material stability is of fundamental importance to any analysis of system properties in condensed matter physics and materials science. The ability to evaluate chemical stability, i.e., whether a stoichiometry will persist in some chemical environment, and structure selection, i.e. what crystal structure a stoichiometry will adopt, is critical to the prediction of materials synthesis, reactivity and properties. In this paper, we demonstrate that density functional theory, with the recently developed strongly constrained and appropriately normed (SCAN) functional, has advanced to a point where both facets of the stability problem can be reliably and efficiently predicted for main group compounds, while transition metal compounds are improved but remain a challenge. SCAN therefore offers a robust model for a significant portion of the periodic table, presenting an opportunity for the development of novel materials and the study of fine phase transformations even in largely unexplored systems with little to no experimental data.
Nuclear power today accounts for approximately 20% of the electrical supply for the US. It provides reliable energy that helps to stabilize the national grid. In contrast to current nuclear power facilities, future concepts operate at higher temperatures, are operationally more efficient, more fully utilize the energy stored within the fuel, and reduce the amount of waste produced. Recent advances in chemistry and materials sciences provide an unprecedented opportunity to develop the critical systems—the fuel, coolant, and structural materials—needed for deployment of advanced nuclear reactor designs. To address the challenge of stringent demands on material systems as their properties and performance continuously evolve over the operational lifetime, the frameworks to uncover the underlying processes that cause reduced performance have been developed over the last decade. There is now the prospect of designing materials that report upon their own damage, are self-healing, and leverage what would be considered deleterious chemical processes to increase their performance within advanced reactor designs. Success will provide an energy source needed to secure a stable and safe energy supply for the nation. To identify the key research directions required to discover the chemical processes and material degradation mechanisms that result in loss of performance of the fuel, coolant, and structural materials, nearly 150 theoreticians, computational experts, and experimentalists from industry, national laboratories, academia, and federal agencies participated in a workshop held in August 2017. Based on the discussions held at the workshop, five Priority Research Directions (PRDs) were identified. These PRDs defined the key scientific challenges that must be met as well as the opportunities that can be exploited to achieve a multi-scale spatial and temporal understanding of fundamental processes that govern the properties and performance of the different material systems required for advanced reactors.
High angle annular dark field scanning transmission electron microscopy images of MoWSSe (2D quaternary alloy system), by Jordan Hachtel (Center for Nanophase Materials Sciences, Oak Ridge National Laboratory). Comes with an ipython notebook for analysis purposes.
The Matter-Radiation Interactions in Extremes project will build the experimental facility for the time-dependent control of dynamic material performance. An x-ray free electron laser at up to 42-keV fundamental energy and with photon pulses down to sub-nanosecond spacing, MaRIE 1.0 is designed to meet the challenges of time-dependent mesoscale materials science. Those challenges will be outlined, the techniques of coherent diffractive imaging and dynamic polycrystalline diffraction described, and the resulting requirements defined for a coherent x-ray source. The talk concludes with the role of the MaRIE project and science in the future.
Uncertainty quantification (UQ) plays a major role in verification and validation for computational engineering models and simulations, and establishes trust in the predictive capability of computational models. In the materials science and engineering context, where the process-structure-property-performance linkage is well known to be the only road mapping from manufacturing to engineering performance, numerous integrated computational materials engineering (ICME) models have been developed across a wide spectrum of length-scales and time-scales to relieve the burden of resource-intensive experiments. Within the structure-property linkage, crystal plasticity finite element method (CPFEM) models have been widely used since they are one of a few ICME toolboxes that allows numerical predictions, providing the bridge from microstructure to materials properties and performances. Several constitutive models have been proposed in the last few decades to capture the mechanics and plasticity behavior of materials. While some UQ studies have been performed, the robustness and uncertainty of these constitutive models have not been rigorously established. In this work, we apply a stochastic collocation (SC) method, which is mathematically rigorous and has been widely used in the field of UQ, to quantify the uncertainty of three most commonly used constitutive models in CPFEM, namely phenomenological models (with and without twinning), and dislocation-density-based constitutive models, for three different types of crystal structures, namely face-centered cubic (fcc) copper (Cu), body-centered cubic (bcc) tungsten (W), and hexagonal close packing (hcp) magnesium (Mg). Our numerical results not only quantify the uncertainty of these constitutive models in stress-strain curve, but also analyze the global sensitivity of the underlying constitutive parameters with respect to the initial yield behavior, which may be helpful for robust constitutive model calibration works in the future.
The Manufacturing Science area of leadership spans the application of fundamental science research and development activities to understand the critical steps in manufacturing processes for the purposes of control and optimization. Although manufacturing is an applied area of research, fundamental science underpins our knowledge of both traditional and transformational manufacturing processes. For example, casting, welding, and many modern additive manufacturing processes that see wide use are broadly classified as solidification processes, with material transitioning from liquid to solid phases upon cooling from higher temperature. However, our understanding of the detailed physics involved in such processes is incomplete, and solidification remains a grand challenge for materials science. To enable adoption of transformative fabrication processes or to accelerate development of traditional ones, it is necessary to develop a detailed mechanistic understanding spanning multiple scales, starting with atomic building blocks (defects, interfaces, composition) and extending all the way up to macroscale performance in moderate and/or extreme environments.
Magnetic topological phases of quantum matter are an emerging frontier in physics and materials science, of which kagome magnets appear as a highly promising platform. Here, we explore magnetic correlations in the recently identified topological kagome system TbMn6 S n 6 using muon spin rotation, combined with local field analysis and neutron diffraction. Our studies identify an out-of-plane ferrimagnetic structure with slow magnetic fluctuations which exhibit a critical slowing down below $T^{*}_{C1}$ ≃ 120 K and finally freeze into static patches with ideal out-of-plane order below T C1 ≃ 20 K. We further show that hydrostatic pressure of 2.1 GPa stabilises the static out-of-plane topological ferrimagnetic ground state in the whole volume of the sample. Therefore the exciting perspective arises of a magnetically-induced topological system whose magnetism can be controlled through external parameters. The present results will stimulate theoretical investigations to obtain a microscopic understanding of the relation between the low-temperature volume-wise magnetic evolution of the static c-axis ferrimagnetic patches and the topological electronic properties in TbMn 6 Sn 6 .
Controlling and predicting the processing-structure-performance relationship in functional materials is a grand challenge in materials science, with important implications for a wide range of emerging applications; a high fidelity understanding of the performance impact of microstructures formed under synthesis conditions is required to develop advanced materials, such as solid-state fuel cells and electrolyzers. Using the ceramic BaZrO 3 as a case study, we directly simulate the synthesis and investigate how proton transport is dictated by microstructures. We develop a framework that couples density functional theory (DFT), machine-learning interatomic potential (MLIP) driven molecular dynamics, and grand canonical Monte Carlo to perform large-scale, microstructure-resolved, atomistic simulations of proton transport in experimentally representative polycrystalline structures. Our fully ab initio approach, using a MLIP as a proxy for DFT, allows us to quantify the competition between two distinct diffusion mechanisms: one associated with grain-boundary regions and another within grains. When the impacts of grain boundaries are taken into account, proton transport exhibits substantial deviation from the bulk oxide limit. This addresses long-standing discrepancies between theory and experiments. Our integrated approach provides atomistic insight into microstructure-dependent proton pathways in BaZrO 3 and establishes a general protocol for predicting processing-structure-performance relationships.
Computational analysis of cell walls Layers of intertwined fibers make up plant cell walls. The various types of fibers respond differently to deformation. Cellulose microfibrils, for example, can stretch or curve, changing their end-to-end length, and can also slide past each other, reorient relative directions, and bundle with neighboring microfibrils. Zhang et al. developed a computational model based on observations of onion skin epidermis that describes how these complex changes in space govern cell wall mechanics. The results inform ways to engineer multifunctional fibrous materials. Science , this issue p. 706