Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “material science”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Orchestration of materials science workflows for heterogeneous resources at large scale

In the era of big data, materials science workflows need to handle large-scale data distribution, storage, and computation. Any of these areas can become a performance bottleneck. We present a framework for analyzing internal material structures (e.g., cracks) to mitigate these bottlenecks. We demonstrate the effectiveness of our framework for a workflow performing synchrotron X-ray computed tomography reconstruction and segmentation of a silica-based structure. Our framework provides a cloud-based, cutting-edge solution to challenges such as growing intermediate and output data and heavy resource demands during image reconstruction and segmentation. Specifically, our framework efficiently manages data storage, scaling up compute resources on the cloud. The multi-layer software structure of our framework includes three layers. A top layer uses Jupyter notebooks and serves as the user interface. A middle layer uses Ansible for resource deployment and managing the execution environment. A low layer is dedicated to resource management and provides resource management and job scheduling on heterogeneous nodes (i.e., GPU and CPU). At the core of this layer, Kubernetes supports resource management, and Dask enables large-scale job scheduling for heterogeneous resources. The broader impact of our work is four-fold: through our framework, we hide the complexity of the cloud’s software stack to the user who otherwise is required to have expertise in cloud technologies; we manage job scheduling efficiently and in a scalable manner; we enable resource elasticity and workflow orchestration at a large scale; and we facilitate moving the study of nonporous structures, which has wide applications in engineering and scientific fields, to the cloud. While we demonstrate the capability of our framework for a specific materials science application, it can be adapted for other applications and domains because of its modular, multi-layer architecture.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry

Graph deep learning models, which incorporate a natural inductive bias for atomic structures, are of immense interest in materials science and chemistry. Here, we introduce the Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. Built on top of the popular Deep Graph Library (DGL) and Python Materials Genomics (Pymatgen) packages, MatGL is designed to be an extensible “batteries-included” library for developing advanced model architectures for materials property predictions and interatomic potentials. At present, MatGL has efficient implementations for both invariant and equivariant graph deep learning models, including the Materials 3-body Graph Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph Network (CHGNet), TensorNet and SO3Net architectures. MatGL also provides several pre-trained foundation potentials (FPs) with coverage of the entire periodic table, and property prediction models for out-of-box usage, benchmarking and fine-tuning. Finally, MatGL integrates with PyTorch Lightning to enable efficient model training.

chemistry↗
NASA NTRS2001

Computed Tomography Support for Microgravity Materials Science Experiments

The accurate measurement of density in both liquid and solid samples is of considerable interest to Principal Investigators with materials science experiments slated for the ISS. The work to be described is an innovative application of a conventional industrial nondestructive evaluation instrument. Traditional applications of industrial computed tomography (CT) rely on reconstructing cross sections of large structures to provide two-dimensional planar views which can identify defects such as porosity, or other material anomalies. This has been done on microgravity materials science experiments to check the integrity of ampoule-cartridge assemblies for safety purposes. With a substantially monoenergetic flux, as can be obtained with a radioactive cobalt source, there will be a direct correlation between absorption and density. Under such conditions it then becomes possible to make accurate measurements of density throughout a sample, and even when the sample itself is enclosed within a furnace and a safety required cartridge. Such a system has been installed at Kennedy Space Center (KSC) and is available to PIs to examine samples before and after flight. The CT system is being used to provide density information for two purposes. Firstly, the determination of density changes from liquid to solid is vital information to the PI for purposes of modeling the solidification behavior of his sample, and to engineers who have to design containment ampoules and must allow for shrinkage and other volume changes that may occur during processing. While such information can be obtained by pycnometric measurements, the possibility of using a furnace installed on the CT system enables one to examine potentially dangerous materials having high vapor pressures, while not needing visible access to the material. In addition, uniform temperature can readily be obtained, and the system can be controlled to ramp up, hold, and ramp down while collecting data over a wide range of parameters automatically. Results of initial tests on low melting point elements such as gallium, indium and tin will be presented, and the intent is to proceed to compounds such as InSb, HgCdTe and CdTe. Alloys such as Pb-Sb (PI - Poirier, U AZ) and Cu-Al (PI - Trivedi, Ames Lab.), which are the subjects of flight experiments, will also be examined. The second application is the conversion of measured density values directly to composition. This was successfully done with the mercury cadmium telluride alloys grown on the second and fourth United States Microgravity Payload (USMP-2 and USMP-4) missions by Lehoczky. CdTe values along the length of the boules were obtained at KSC prior to cutting the sample, and could have been obtained prior to its removal from the cartridge and ampoule. Examples of the data obtained will be shown. It is anticipated that several of the materials science PIs will avail themselves of the technique described, initially for determining densities prior to flight, and then to acquire early quantitative data on the compositional variation within their samples.

Gillies, Donald C.↗
DOE OSTI2023

Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

Alexeev, Yuri↗
NASA NTRS2004

Coarsening in Solid-Liquid Mixtures-2: A Materials Science Experiment for the ISS

A materials science experiment has been developed and readied for operation aboard the International Space Station (ISS). Components of this experiment are onboard ISS and area awaiting the flight of science samples. The goal of the experiment is to understand the dynamics of Ostwald ripening, also known as coarsening, a process that occurs in nearly any two-phase mixture found in nature. Attempts to obtain experimental data in ground-based laboratories are hindered due to the presence of gravity, which introduces material transport modes other than that of the coarsening phenomenon. This introduces adjustable parameters in the formulation of theory. The original Coarsening in Solid-Liquid Mixtures (CSLM) mission, which flew on the Space Shuttle in 1997, produced data from a coarsened eutectic alloy. Unfortunately, both the science matrix and the hardware, while nominally functional, did not account adequately for operations in microgravity. A significantly redesigned follow-on experiment, CSLM-2 has been developed to redress the inadequacies of the original experiment. This paper reviews the CSLM-2 project: its history, science goals, flight hardware implementation, and planned operations and analysis

Hickman, J. Mark↗
DOE OSTI2024

The Rise of Intelligent Materials Science: Unleashing the Power of Machine Intelligence in Characterization

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

autonomous↗
DOE OSTI2024

Roadmap on data-centric materials science

Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of artificial intelligence and its subset machine learning, has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.

36 MATERIALS SCIENCE↗
NASA NTRS1999

Carbon Nanotubes: Miracle of Materials Science?

Article to be sent to Advanced Materials and Processes, journal of ASM International, as attached. This is a news-type technical journal for a large organization of scientists, engineers, salesmen, and managers. The article is quite general, meant to be an introduction to the properties of nanotubes. This is a materials science organization, therefore the article is geared toward using nanotubes for materials uses. Pictures have not been included in this version.

Files, Bradley S.↗
NASA NTRS1995

Materials Science Research

Microgravity materials processing experiments provide an opportunity to perform scientific research in an environment which allows one to observe various phenomena without the masking effects of gravity-driven convective flows, buoyancy, or contaminating influences of walled containers. Even for the most experienced scientists, it is still difficult to predict beforehand, whether or not microgravity experimentation can be successfully performed in space and achieve solutions to problems which are not attainable in 1 g. Consequently, experimentation in ground based facilities which are capable of simulating, in somewhat lesser time frames and to a lesser degree of microgravity, provides a unique low-cost approach to determine the feasibility of continuing research in a particular experiment. The utilization of these facilities in developing the full requirements for a space experiment does present a very cost-effective approach to microgravity experimentation. The Drop Tube Facility at Marshall Space Flight Center (MSFC) provides an excellent test bed for containerless processing experiments such as described here. These facilities have demonstrated for a number of years the capability to develop insight into space experiments involving containerless processing, rapid solidification, and wetting phenomena through the use of lower-cost ground facilities. Once sufficient data has been obtained, then a space-based experiment can be better defined.

Workman, Gary L.↗
NASA NTRS2012

Overview of NASA's Microgravity Materials Science Program

The microgravity materials program was nearly eliminated in the middle of the aughts due to budget constraints. Hardware developments were eliminated. Some investigators with experiments that could be performed using ISS partner hardware received continued funding. Partnerships were established between US investigators and ESA science teams for several investigations. ESA conducted peer reviews on the proposals of various science teams as part of an ESA AO process. Assuming he or she was part of a science team that was selected by the ESA process, a US investigator would submit a proposal to NASA for grant funding to support their part of the science team effort. In a similar manner, a US materials investigator (Dr. Rohit Trivedi) is working as a part of a CNES selected science team. As funding began to increase another seven materials investigators were selected in 2010 through an NRA mechanism to perform research related to development of Materials Science Research Rack investigations. One of these has since been converted to a Glovebox investigation.

Downey, James Patton↗
NASA NTRS2004

Low Gravity Materials Science Research for Space Exploration

On January 14, 2004, the President of the United States announced a new vision for the United States civil space program. The Administrator of the National Aeronautics and Space Administration (NASA) has the responsibility to implement this new vision. The President also created a Presidential Commission 'to obtain recommendations concerning implementation of the new vision for space exploration.' The President's Commission recognized that achieving the exploration objectives would require significant technical innovation, research, and development in focal areas defined as 'enabling technologies.' Among the 17 enabling technologies identified for initial focus were advanced structures; advanced power and propulsion; closed-loop life support and habitability; extravehicular activity system; autonomous systems and robotics; scientific data collection and analysis; biomedical risk mitigation; and planetary in situ resource utilization. The Commission also recommended realignment of NASA Headquarters organizations to support the vision for space exploration. NASA has aggressively responded in its planning to support the vision for space exploration and with the current considerations of the findings and recommendations from the Presidential Commission. This presentation will examine the transformation and realignment activities to support the vision for space exploration that are underway in the microgravity materials science program. The heritage of the microgravity materials science program, in the context of residence within the organizational structure of the Office of Biological and Physical Research, and thematic and sub-discipline based research content areas, will be briefly examined as the starting point for the ongoing transformation. Overviews of future research directions will be presented and the status of organizational restructuring at NASA Headquarters, with respect to influences on the microgravity materials science program, will be discussed. Additional information is included in the original extended abstract.

Clinton, R. G., Jr.↗
DOE OSTI2025

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.

Computer Science↗
NASA NTRS1985

Material sciences experiments under microgravity conditions with MAUS

Project MAUS is a part of the German material sciences program and provides autonomous payloads for the Space Shuttle. These payloads are housed in canisters which are identical with those of NASA's Get-Away-Special program. The main components of the hardware are: a standard system consisting of power supply, experiment control, data acquisition and the experiment modules containing experiment specific hardware. So far, three MAUS modules with experiments from the area of material sciences have been flown as GAS payloads.

G.H. Otto↗
DOE OSTI2024

A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community

Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multimodal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. Furthermore, modern materials engineering requires the tuning of not only composition but also of phase and microstructure to elucidate processing–structure–property–performance relationships. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing (i) synthesis and processing conditions, (ii) characterization results, and (iii) property and performance measurements. In this work, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A multimodal large language model for materials science

Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics and beyond. Integrating material structure data with language-based information through multimodal large language models (LLMs) offers great potential to support these efforts by enhancing human–artificial intelligence interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multimodal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat uses a bridging module to effectively align a pretrained universal machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat greatly improves performance in material property prediction and human–artificial intelligence interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis.

Tang, Yingheng [Lawrence Berkeley National Laborat↗
NASA NTRS1988

Ground based materials science experiments

The facilities at the Microgravity Materials Science Laboratory (MMSL) at the Lewis Research Center, created to offer immediate and low-cost access to ground-based testing facilities for industrial, academic, and government researchers, are described. The equipment in the MMSL falls into three categories: (1) devices which emulate some aspect of low gravitational forces, (2) specialized capabilities for 1-g development and refinement of microgravity experiments, and (3) functional duplicates of flight hardware. Equipment diagrams are included.

Meyer, M. B.↗