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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Microgravity strategic plan, 1990

The mission of the NASA Microgravity program is to utilize the unique characteristics of the space environment, primarily the near absence of gravity, to understand the role of gravity in materials processing, and to demonstrate the feasibility of space production of improved materials that have high technological, and possible commercial, utility. The following five goals for the Microgravity Program are discussed: (1) Develop a comprehensive research program in fundamental sciences, materials science, and biotechnology for the purpose of attaining a structured understanding of gravity dependent physical phenomena in both Earth and non-Earth environments; (2) Foster the growth of interdisciplinary research community to conduct research in the space environment; (3) Encourage international cooperation for the purpose of conducting research in the space environment; (4) Utilize a permanently manned, multi-facility national microgravity laboratory in low-Earth orbit to provide a long-duration, stable microgravity environment; (5) Promote industrial applications of space research for the development of new, commercially viable products, services, and markets resulting from research in the space environment.

Source record↗
DOE OSTI2024

Modern chemical graph theory

Abstract Graph theory has a long history in chemistry. Yet as the breadth and variety of chemical data is rapidly changing, so too do graph encoding methods and analyses that yield qualitative and quantitative insights. Using illustrative cases within a basic mathematical framework, we showcase modern chemical graph theory's utility in Chemists' analysis and model development toolkit. The encoding of both experimental and simulation data is discussed at various levels of granularity of information. This is followed by a discussion of the two major classes of graph theoretical analyses: identifying connectivity patterns and partitioning methods. Measures, metrics, descriptors, and topological indices are then introduced with an emphasis upon enhancing interpretability and incorporation into physical models. Challenging data cases are described that include strategies for studying time dependence. Throughout, we incorporate recent advancements in computer science and applied mathematics that are propelling chemical graph theory into new domains of chemical study. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Materials Science Structure and Mechanism > Molecular Structures

Leite, Leonardo S. G.↗
NASA NTRS2003

Space Station Science Supported by Marshall Space Flight Center

The science program at Marshall Space Flight Center will be reviewed in the context of the overall NASA science program. An overview will be given on how Marshall science supports the International Space Station research program. The Microgravity research capabilities at Marshall's Biological and Physical Space Research Laboratory will be reviewed. The environment in orbit provides a unique opportunity to study Materials Science and Biotechnology in the absence of sedimentation and convection. There are a number of peer-selected investigations that have been selected to fly on the Space Station that have been conceived and are led by Marshall civil service and contractor scientists. In addition to Microgravity research the Station will enable research in New Initiative Research Areas that focus on enabling humans to live, work, and explore the solar system safely. The specific scientific instruments that have been developed for Materials Science and Biotechnology Research on the International Space Station will be discussed.

Whitaker, Ann F.↗
DOE OSTI2022

NNSA Advanced Photon Source Sector Opportunities Workshop (Workshop Summary)

An NNSA-Argonne National Laboratory workshop to define NNSA and external partner mission needs and opportunities at the Advanced Photon Source was held February 15-17, 2022. This workshop summary provides a fresh look at NNSA’s evolving mission needs, partner agency and university interests, x-ray photon characteristics, and technical requirements to meet those needs. The workshop was organized according to four breakout groups: Coupling dynamic drivers to a synchrotron, High explosives and chemical sciences, Process-structure-performance: X-ray experiments to understand materials behaviors, and Process-structure-performance: manufacturing and aging. This summary report describes the workshop outcomes, and the preliminary conceptual design for a dedicated Defense Materials Science Sector (DMSS) at the Advanced Photon Source.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Immersive Particle Advection: Through the Scales of Renewable Energy: Preprint

We describe the benefits of immersive flow analysis for three large-scale computational science studies in the field of renewable energy. The studies encompass a range of scales, spanning from the large atmospheric scale of a wind farm to the human scale of an electric vehicle cabin down to the microscopic scale of battery material science. In these studies, users explored the flow patterns and dynamics through immersive particle advection. The integration of high-performance computing with immersive analysis provided a deeper understanding of these systems, helping develop more effective solutions for a sustainable energy future.

computational fluid dynamics↗
NASA NTRS1976

A research project to develop and evaluate a technical education component on materials technology for orientation to space-age technology

A project was initiated to develop, implement, and evaluate a prototype component for self-pacing, individualized instruction on basic materials science. Results of this project indicate that systematically developed, self-paced instruction provides an effective means for orienting nontraditional college students and secondary students, especially minorities, to both engineering technology and basic materials science. In addition, students using such a system gain greater chances for mastering subject matter than with conventional modes of instruction.

Jacobs, J. A.↗
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↗
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 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 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 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 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↗