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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Anomaly Detection in Materials Digital Twins with Multiscale ICME for Additive Manufacturing

Detecting anomaly in fatigue and fracture experimental materials science is an interesting yet challenging topic. The reasons are threefold. First, the anomalous microstructure feature that gives rise to structural failure is small, sometimes in the order of 10 -7 of the interrogated volume. This, in turn, results in a highly imbalanced classification problem in machine learning (ML). Second, the consequence is high, in the sense that the test specimen is destructed in such case. Third, the convolution between microstructure stochasticity and the small probability of void nucleation, growth, and coalescence makes failure and fracture a hard-to-predict and challenging problem in materials science due to its irreproducibility, even experimentally. In this paper, we developed a materials digital twin and applied anomaly detection methods to detect voids and anomaly in additive manufacturing (AM). The materials digital twin is driven by two integrated computational materials engineering (ICME) models, which are kinetic Monte Carlo (kMC) and crystal plasticity finite element method (CPFEM). In conclusion, we demonstrated that by using anomaly detection, it is possible to detect voids and other defects in materials digital twin, which paves way for future research in integrating materials digital twin with its physical counterpart.

ICME↗
DOE OSTI2017

Spherical Region Queries on Multicore Architectures

In this short paper, we report the performance of multiple thread-parallel algorithms for spherical region queries on multicore architectures motivated by a challenging data analytics application in materials science. Performances of two tree-based algorithms and a naive algorithm are compared to identify the length scales at which these approaches perform optimally. The optimal algorithm is then used to scale the driving materials science application, which is shown to deliver over 17X speedup using 32 OpenMP threads on data sets containing many millions of atoms.

Lu, Hao↗
NASA NTRS1974

CVT/GPL phase 2 integrated testing

Experiments representing earth observations, space physics, and material sciences disciplines were installed in the General Purpose Laboratory (GPL). The experiments and the GPL are described. The experiments interfaces the GPL and GPL support systems are assessed. The experiments were cloud physics, ionospheric disturbances, material sciences, high energy astronomy, and superfluid helium.

Shurney, R. E.↗
DOE OSTI2015

MST e-News December 2015

This is the December 2015 issue of the MST e-News newsletter for the Materials Science and Technology Division.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Single crystal growth and characterization of topological semimetal ZrSnTe

High quality single crystals are critical for experimental materials science research. ZrSnTe represents such an example. This material belongs to the ZrSiS-type topological material family, which is so far the only one in this material family possessing Fermi surface formed by Dirac bands generated by a Sn-square net. Experimental study on ZrSnTe is limited due to the difficulty in single crystal growth. Here, in this work, we report the single crystal growth for ZrSnTe using a solid-state reaction method with Sn as a flux. The roles of various growth parameters such as the molar ratio of starting materials, growth temperature and cooling rate in obtaining sizeable single crystals were investigated. The quality of the obtained single crystals was checked by elemental, structural and electronic characterizations. Our study on the growth method for ZrSnTe would enable the future study on this less explored topological semimetal.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1988

Analysis of low gravity tolerance of model experiments for space station: Preliminary results for directional solidification

It has become clear from measurements of the acceleration environment in the Spacelab that the residual gravity levels on board a spacecraft in low Earth orbit can be significant and should be of concern to experimenters who wish to take advantage of the low gravity conditions on future Spacelab missions and on board the Space Station. The basic goals are to better understand the low gravity tolerance of three classes of materials science experiments: crystal growth from a melt, a vapor, and a solution. The results of the research will provide guidance toward the determination of the sensitivity of the low gravity environment, the design of the laboratory facilites, and the timelining of materials science experiments. To data, analyses of the effects of microgravity environment were, with a few exceptions, restricted to order of magnitude estimates. Preliminary results obtained from numerical models of the effects of residual steady and time dependent acceleration are reported on: heat, mass, and momentum transport during the growth of a dilute alloy by the Bridgman-Stockbarger technique, and the response of a simple fluid physics experiment involving buoyant convection in a square cavity.

Alexander, J. Iwan D.↗
DOE OSTI2017

A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials

Since the beginning of the industrial age, material performance and design have been in the midst of innovation of many disruptive technologies. Today's electronics, space, medical, transportation, and other industries are enriched by development, design and deployment of composite, heterogeneous and multifunctional materials. As a result, materials innovation is now considerably outpaced by other aspects from component design to product cycle. In this article, we review predictive nonlinear theories for multiscale modeling of heterogeneous materials. Deeper attention is given to multiscale modeling in space and to computational homogenization in addressing challenging materials science questions. Moreover, we discuss a state-of-the-art platform in predictive image-based, multiscale modeling with co-designed simulations and experiments that executes on the world's largest supercomputers. Such a modeling framework consists of experimental tools, computational methods, and digital data strategies. Once fully completed, this collaborative and interdisciplinary framework can be the basis of Virtual Materials Testing standards and aids in the development of new material formulations. Moreover, it will decrease the time to market of innovative products.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms

This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative models generate synthetic data resembling real data, enabling deep learning model training without extensive labeled datasets. They prove valuable in creating virtual libraries of molecules for material science and facilitating drug discovery by generating molecules with specific properties. While generative adversarial networks (GANs) are explored for these purposes, mode collapse restricts their efficacy, limiting novel structure variability. To address this, we introduce a masked language model (LM) inspired by natural language processing. Although LMs alone can have inherent limitations, we propose a hybrid architecture combining LMs and GANs to efficiently generate new molecules, demonstrating superior performance over standalone masked LMs, particularly for smaller population sizes. This hybrid LM-GAN architecture enhances efficiency in optimizing properties and generating novel samples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Recent advances in the global rare-earth supply chain

The current global rare-earth element (REE) supply chain is highly imbalanced and tightly controlled by just a few countries. Such an imbalance of the critical metals supply chain poses a significant challenge to the energy-transition strategies and the national security of many countries. As such, this issue of MRS Bulletin delves into the materials science aspects of the REE supply chain, including fundamental REE mineralogy, REE separation and extraction, REE mining economics, the environmental impacts of REE mining and processing, and circular economy potential for REEs. This issue of MRS Bulletin is meant to inform the materials science community of some of the constraints on REE production from the mining of ore deposits, through processing technologies, and then finally, the possibility of recycling.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

ChemGraph as an agentic framework for computational chemistry workflows

Atomistic simulations are essential in chemistry and materials science but remain challenging to run due to the expert knowledge required for the setup, execution, and validation stages of these calculations. We present ChemGraph, an agentic framework powered by artificial intelligence and state-of-the-art simulation tools to streamline and automate computational chemistry and materials science workflows. ChemGraph leverages graph neural network-based foundation models for accurate yet computationally efficient calculations and large language models (LLMs) for natural language understanding, task planning, and scientific reasoning to provide an intuitive and interactive interface. We evaluate ChemGraph across 13 benchmark tasks and demonstrate that smaller LLMs (GPT-4o-mini, Claude-3.5-haiku, Qwen-2.5-14B) perform well on simple workflows, while more complex tasks benefit from using larger models. Importantly, we show that decomposing complex tasks into smaller subtasks through a multi-agent framework enables GPT-4o to reach perfect accuracy and smaller LLMs to match or exceed single-agent GPT-4o's performance in these benchmarks.

Computational chemistry↗
DOE OSTI2016

Soft Landing of Complex Ions for Studies in Catalysis and Energy Storage

Immobilization of complex molecules and clusters on supports plays an important role in a variety of disciplines including materials science, catalysis and biochemistry. In particular, deposition of clusters on surfaces has attracted considerable attention due to their non-scalable, highly size-dependent properties. The ability to precisely control the composition and morphology of clusters and small nanoparticles on surfaces is crucial for the development of next generation materials with rationally tailored properties. Soft- and reactive landing of ions onto solid or liquid surfaces introduces unprecedented selectivity into surface modification by completely eliminating the effect of solvent and sample contamination on the quality of the film. The ability to select the mass-to-charge ratio of the precursor ion, its kinetic energy and charge state along with precise control of the size, shape and position of the ion beam on the deposition target makes soft-landing an attractive approach for surface modification. High-purity uniform thin films on surfaces generated using mass-selected ion deposition facilitate understanding of critical interfacial phenomena relevant to catalysis, energy generation and storage, and materials science. Our efforts have been directed toward understanding charge retention by soft-landed metal and metal-oxide cluster ions, which may affect both their structure and reactivity. Specifically, we have examined the effect of the surface on charge retention by both positively and negatively charged cluster ions. We found that the electronic properties of the surface play an important role in charge retention by cluster cations. Meanwhile, the electron binding energy is a key factor determining charge retention by cluster anions. These findings provide the scientific foundation for the rational design of interfaces for advanced catalysts and energy storage devices. Further optimization of electrode-electrolyte interfaces for applications in energy storage and electrocatalysis may be achieved by understanding and controlling the properties of soft-landed cluster ions.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2021

Advances in bioelectronics: Materials, devices, and translational applications

Modern electronics and materials science are bringing revolutionary advances to biointerface design and are shattering the limits of what is possible in the areas of biomedical diagnostics and sensing, neuroscience, and prosthetics. Furthermore, this special issue highlights the rapid progress in all areas of bioelectronics and includes contributions that explore the interfaces between electronics, materials science, and other engineering disciplines, as well as biochemistry, biophysics, and general biology.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2017

Liquid Metals as Plasma-facing Materials for Fusion Energy Systems: From Atoms to Tokamaks

The objective of our studies was to advance our fundamental understanding of liquid metals as plasma-facing materials for fusion energy systems, with a broad scope: from atoms to tokamaks. The flow of liquid metals offers solutions to significant problems of the plasma-facing materials for fusion energy systems. Candidate metals include lithium, tin, gallium, and their eutectic combinations. However, such liquid metal solutions can only be designed efficiently if a range of scientific and engineering issues are resolved that require advances in fundamental fluid dynamics, materials science and surface science. In our research we investigated a range of significant and timely problems relevant to current and proposed engineering designs for fusion reactors, including high-heat flux configurations that are being considered by leading fusion energy groups world-wide. Using experimental and theoretical tools spanning atomistic to continuum descriptions of liquid metals, and bridging surface chemistry, wetting/dewetting and flow, our research has advanced the science and engineering of fusion energy materials and systems. Specifically, we developed a combined experimental and theoretical program to investigate flows of liquid metals in fusion-relevant geometries, including equilibrium and stability of thin-film flows, e.g. wetting and dewetting, effects of electromagnetic and thermocapillary fields on liquid metal thin-film flows, and how chemical interactions and the properties of the surface are influenced by impurities and in turn affect the surface wetting characteristics, the surface tension, and its gradients. Because high-heat flux configurations produce evaporation and sputtering, which forces rearrangement of the liquid, and any dewetting exposes the substrate to damage from the plasma, our studies addressed such evaporatively driven liquid flows and measured and simulated properties of the different bulk phases and material interfaces. The range of our studies included (i) quantum mechanical calculations that allow inclusion of many thousands of atoms for the characterization of the interface of liquid metals exposed to continuous bombardment by deuterium and tritium as expected in fusion, (ii) molecular dynamics studies of the phase behavior of liquid metals, which (a) utilize thermodynamic properties computed using our quantum mechanical calculations and (b) establish material and wetting properties of the liquid metals, including relevant eutectics, (iii) experimental investigations of the surface science of liquid metals, interacting both with the solid substrate as well as gaseous species, and (iv) fluid dynamical studies that incorporate the material and surface science results of (ii) and (iii) in order to characterize flow in capillary porous materials and the thin-film flow along curved boundaries, both of which are potentially major components of plasma-facing materials. The outcome of these integrated studies was new understanding that enables developing design rules useful for future developments of the plasma-facing components critical to the success of fusion energy systems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Enhancement of the Magnetoresistance in the Mobility‐Engineered Compensated Metal Pt 5 P 2

Abstract The magnetoresistance (MR) in nonmagnetic materials continues to be a fertile research area in materials science. The search for giant, positive MR has been limited to a rather small window of materials such as high‐mobility semimetals in single‐crystalline form. Here, the observation of a very large positive MR in metallic Pt 5 P 2 in polycrystalline form is reported. The observations reveal that improvement of the crystallinity results in a significant enhancement of the positive MR, exceeding 10 000% at 9 T, comparable to high‐mobility semimetals. Based on first‐principles calculations combined with magnetotransport and thermoelectric measurements, the Fermi surface of Pt 5 P 2 is found to consist of a collection of multiple electron and hole pockets compensating one another, along with a characteristic pocket continuously connected to the adjacent Brillouin zone, together with possible topologically protected band crossings. This work extends the landscape of high MR candidate materials to polycrystalline metals, which demonstrates the importance of crystallinity and purity of the samples for the optimization of the MR.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model

Abstract The design of materials structure for optimizing functional properties and potentially, the discovery of novel behaviors is a keystone problem in materials science. In many cases microstructural models underpinning materials functionality are available and well understood. However, optimization of average properties via microstructural engineering often leads to combinatorically intractable problems. Here, we explore the use of the reinforcement learning (RL) for microstructure optimization targeting the discovery of the physical mechanisms behind enhanced functionalities. We illustrate that RL can provide insights into the mechanisms driving properties of interest in a 2D discrete Landau ferroelectrics simulator. Intriguingly, we find that non-trivial phenomena emerge if the rewards are assigned to favor physically impossible tasks, which we illustrate through rewarding RL agents to rotate polarization vectors to energetically unfavorable positions. We further find that strategies to induce polarization curl can be non-intuitive, based on analysis of learned agent policies. This study suggests that RL is a promising machine learning method for material design optimization tasks, and for better understanding the dynamics of microstructural simulations.

36 MATERIALS SCIENCE↗