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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Materials Data Science Ontology(MDS-Onto): Unifying Domain Knowledge in Materials and Applied Data Science

Ontologies have gained popularity in the scientific community as a way to standardize terminologies in organizations’ data. Although certain cohorts have created frameworks with rules and guidelines on creating ontologies, there exist significant variations in how Materials Science ontologies are currently developed. We seek to provide guidance in the form of a unified automated framework for developing interoperable and modular ontologies for Materials Data Science that simplifies the ontology terms matching by establishing a semantic bridge up to the Basic Formal Ontology(BFO). This framework provides key recommendations on how ontologies should be positioned within the semantic web, what knowledge representation language is recommended, and where ontologies should be published online to boost their findability and interoperability. Two fundamental components of the MDS-Onto framework are the bilingual package called FAIRmaterials for ontology creation and FAIRLinked, for FAIR data creation. To showcase the practical capabilities of FAIRmaterials, we present two exemplar domain ontologies of MDS-Onto: Synchrotron X-Ray Diffraction and Photovoltaics.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

FAIRLinked: Data FAIRification Tools for Materials Data Science

FAIRLinked is a software package created to support the FAIRification of materials science data, ensuring proper alignment with FAIR principles: Findable, Accessible, Interoperable, and Reusable. It is built to be compatible with MDS-Onto, an ontology designed to capture the semantics of various types of materials data, enabling integration and sharing across different research workflows. The package is subdivided into three subpackages: InterfaceMDS, RDFTableConversion, and QBWorkflow. The first subpackage, InterfaceMDS allows users to search for terms using either string search or various filters, explore different domains and subdomains, and add terms to MDS-Onto. RDFTableConversion is used for serialization and deserialization of data from CSV into JSONLDs and vice versa in a way that captures the semantics of the data using MDS-Onto. Lastly, QBWorkflow is a serialization and deserialization workflow that incorporates RDF Data Cube vocabulary, useful for working with multidimensional datasets. By offering these packages, FAIRLinked lowers the barrier of creating FAIR, machine-actionable data for researchers in the materials science community.

FAIR↗
DOE OSTI2021

Burning Plasma Science: Long Pulse-Materials and Fusion Nuclear Science (Final Project Report)

The series of International Conferences on Fusion Reactor Materials (ICFRM) has been established nearly forty years ago as the major international forum for exchanging and archiving a growing knowledge based on the development of materials for both near and long-term fusion energy systems. The critical role of materials science and engineering in overcoming the technological challenges to making fusion energy a practical reality has been an overarching theme of invited and contributed oral talks as well as the poster session presentations. ICFRM is the premier international forum connecting specialists in the area of development of materials for both near and longer-term fusion energy systems. A central theme is the sharing of new insights, cutting-edge research, and technology in materials science pertaining to the realization of fusion energy. The conference consists of daily overview plenary lectures, invited and contributed oral presentations, and a series of technical poster sessions. In addition to hosted satellite meetings, pre-conference technical tutorials, and stimulating discussions. The 19th International Conference on Fusion Reactor Materials (ICFRM-19) was held in La Jolla, California, U.S.A. during the period October 27 through November 1, 2019. We describe here the conference background, topics, structure, program, schedule and timeline, publications, venue, satellite meetings, short courses and seminars, and finally the budget and expenses.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A General Materials Data Science Framework for Quantitative 2D Analysis of Particle Growth from Image Sequences

Abstract Phase transformations are a challenging problem in materials science, which lead to changes in properties and may impact performance of material systems in various applications. We introduce a general framework for the analysis of particle growth kinetics by utilizing concepts from machine learning and graph theory. As a model system, we use image sequences of atomic force microscopy showing the crystallization of an amorphous fluoroelastomer film. To identify crystalline particles in an amorphous matrix and track the temporal evolution of the particle dispersion, we have developed quantitative methods of 2D analysis. 700 image sequences were analyzed using a neural network architecture, achieving 0.97 pixel-wise classification accuracy as a measure of the correctly classified pixels. The growth kinetics of isolated and impinged particles were tracked throughout time using these image sequences. The relationship between image sequences and spatiotemporal graph representations was explored to identify the proximity of crystallites from each other. The framework enables the analysis of all image sequences without the requirement of sampling for specific particles or timesteps for various materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Accelerated Creep Testing of Inconel 718 Using the Stepped Isostress Method (SSM)

This paper demonstrates the stepped isostress method (SSM); an accelerated creep test (ACT) for the rapid assessment of metallic materials. The SSM test is based on the time-temperature-stress superposition principle (TTSSP) where temperature and/or stress are step increased to accelerate the time-to-rupture. The SSM test has proven successful for the ACT of polymers and polymeric composites but has yet to be proven for metallic materials. In this study, new test matrix design rules for the SSM of metals are established based on deformation mechanism, time-temperature transformation, and time-temperature precipitation maps. A test matrix of SSM and conventional creep tests (CCTs) are executed for alloy Inconel 718 at 750°C (1382°F) with stress levels ranging from 100 to 350 MPa. Validation CCT data is gathered from the Japan National Institute of Material Science (NIMS). Material constants for the Sine-hyperbolic (Sinh) constitutive model are calibrated using the SSM data and employed to predict the conventional creep response. Here, when blindly compared to the CCT data, the SSM calibrated Sinh model can accurately predict the conventional creep response across logarithmic decades and thus accelerate the capture of conventional creep data. Fractography indicates ductile fracture by transgranular microvoid coalescence (TMVC) with the same fracture mode observed in both SSM and CCT specimens. Creep cavitation is indicated by the population, smoothness, and size of microvoids.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Glassy interphases reinforce elastomeric nanocomposites by enhancing percolation-driven volume expansion under strain

For nearly a century, introduction of nanoparticles to elastomers has yielded extraordinarily tough nanocomposites that are critical to technologies from actuators to tires. The mechanisms by which this reinforcement occurs have nevertheless remained a central open question in material science. One widely debated hypothesis posits that strong interactions between polymer and particles induce "glassy bridges" that cement particles into a cohesive percolating network that resists elongation. Here, molecular dynamics simulations show that glassy particle shells do not primarily provide elongational cohesion. Instead, they amplify an underlying mechanism wherein competition between filler and elastomer networks causes the elastomer's volume to increase on deformation. This induces contributions from the elastomer's bulk modulus, which is of order 1000 times larger than its Young's modulus. These findings establish a unified understanding of low-strain reinforcement in filled elastomers as emanating from volumetric competition between coexisting particulate and elastomeric networks. This reframes and unifies our understanding of low-strain reinforcement, provides a clear-cut diagnostic for the presence of glassy bridging, and offers a new design principle for tough elastomeric nanocomposites.

Computational Physics (physics.comp-ph)↗
DOE OSTI2024

Trust Not Verify? The Critical Need for Data Curation Standards in Materials Informatics

The importance of data curation has been recognized in multiple areas of research; however, the discussion of this important issue is only beginning to emerge in materials science. In this Perspective, we highlight the benefits of using the standardized data curation protocols in materials science and discuss current gaps in accurate and reproducible data reporting using case studies drawn from high-impact materials science papers and well-known databases such as the Crystallography Open Database (COD) and the Cambridge Structural Database (CSD). We argue that both experimental and computational materials scientists need to embrace a culture of rigorous data curation as part of modern research data management. We propose a sample data curation pipeline for materials chemistry and illustrate its use by creating two new materials chemistry databases. Here, we hope that this perspective will serve to catalyze further discussion and promote the continuous development of rigorous data curation practices within the materials science research community. We posit that adherence to best practices of data curation will promote and enhance the reliability, reproducibility, and integrity of materials research and enable the development of reliable AI and machine learning models that critically depend on the use of quality data.

Chemical structure↗
DOE OSTI2024

Materials data science using CRADLE: A distributed, data-centric approach

Abstract There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE’s capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed. Graphical abstract

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Machine learning in materials research: Developments over the last decade and challenges for the future

The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials science methods, and ML methods) used within papers that apply ML to materials science. The analysis demonstrates that despite the growth of deep learning techniques, the use of classical machine learning is still dominant as a whole. It also demonstrates how new research can effectively build upon past research, particular in the domain of ML models trained on density functional theory calculation data. Next, I present the progression of best scores as a function of time on the matbench materials science benchmark for formation enthalpy prediction. In particular, a dramatic improvement of 7 times reduction in error is obtained when progressing from feature-based methods that use conventional ML (random forest, support vector regression, etc.) to the use of graph neural network techniques. Finally, I provide views on future challenges and opportunities, focusing on data size and complexity, extrapolation, interpretation, access, and relevance.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Materials for Small Nuclear Reactors and Micro Reactors, Including Space Reactors

Small nuclear reactors, including small modular reactors (SMRs) or reactors for space applications, rely on different materials than those typically applied in large-scale nuclear power plants. Examples include molten salts as cooling medium or fuel carrier, metal hydrides as high-temperature moderators, and fuels allowing for higher burnup. All of these also require novel structural materials, for which material interactions have to be understood. Fissionable and fissile materials, such as uranium or plutonium, are rarely considered in materials design other than for nuclear fuels. Similarly, the aspects of radiation damage, occurring during irradiation when a reactor operates, are unique to nuclear materials research. The handling of these materials puts further limitations on the materials science conducted for nuclear materials. All of these issues move research for these materials off the “main stream” of materials science, and cause it to be more easily conducted at national laboratories. However, nuclear reactors offer unique opportunities for carbon-neutral energy generation and have great potential to address if not solve problems arising from global warming. This special topic, sponsored by the TMS Nuclear Materials Committee, focuses on materials research for small nuclear reactors, both experimental and simulation/modeling.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Exploration of Novel Neuromorphic Methodologies for Materials Applications

Many of today's most interesting questions involve understanding and interpreting complex relationships within graph-based structures. For instance, in materials science, predicting material properties often relies on analyzing the intricate network of atomic interactions. Graph neural networks (GNNs) have emerged as a popular approach for these tasks; however, they suffer from limitations such as inefficient hardware utilization and over-smoothing. Recent advancements in neuromorphic computing offer promising solutions to these challenges. In this work, we evaluate two such neuromorphic strategies known as reservoir computing and hyperdimensional computing. We compare the performance of both approaches for bandgap classification and regression using a subset of the Materials Project dataset. Our results indicate recent advances in hyperdimensional computing can be applied effectively to better represent molecular graphs.

Gobin, Derek [George Mason University, Virginia]↗
DOE OSTI2021

HSLA-100 Forging Contract Vendor Post-Award Meeting [Slides]

LANL utilizes thick-walled, spherical vessels for numerous basic science, materials, and system performance experiments. These pressure retaining vessels are designed and fabricated to meet the intent of ASM E B&PV Section VIII, Division 3 and Code Case 2564. LANL has successfully contracted past production of component parts (nozzle and cover forgings, plate production and head forming) and overall vessel construction (welding, inspection, hydrostatic testing) utilizing HSLA-100 steel. As current vessel inventories approach design lifetimes, we are once again embarking upon a series of fabrication contracts to produce replacement components and vessel assemblies to ensure continued experiment cadence at LANL. We anticipate releasing additional future vessel production contracts as experimental demands at LANL continue to increase

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fusion Materials Research at Oak Ridge National Laboratory in FY22

The materials science challenge of providing a suite of suitable materials to satisfy the technology to achieve fusion energy is addressed in this ORNL program. The inability of currently available materials and components to withstand the harsh fusion nuclear environment requires development of new materials, and an understanding of their response to the fusion environment. The overarching goal of the ORNL fusion materials program is to provide the applied materials science support and materials understanding to underpin the ongoing DOE Office of Science - Fusion Energy Sciences program, in parallel with developing the materials for fusion power systems. In this effort the program continues to be integrated both with the larger U.S. and international fusion materials communities and with the U.S. and international fusion design and technology communities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Fusion Materials Research at Oak Ridge National Laboratory in Fiscal Year 2023

The materials science challenge of providing a suite of suitable materials to satisfy the technology to achieve fusion energy is addressed in this ORNL program. The inability of currently available materials and components to withstand the harsh fusion nuclear environment requires development of new materials, and an understanding of their response to the fusion environment. The overarching goal of the ORNL Fusion Materials program is to provide the applied materials science support and materials understanding to underpin the ongoing DOE Office of Science—Fusion Energy Sciences program, in parallel with developing the materials for fusion power systems. In this effort the program continues to be integrated both with the larger U.S. and international fusion materials communities and with the U.S. and international fusion design and technology communities. The excitement of this program comes from the priorities given to this subject in the two recent fusion reviews, by the FESAC and NAS committees. An important element of those recommendations is the support for pivoting the national R&D emphasis to the Fusion Materials and Technologies (FM&T), the long-advocated Fusion Prototypic Neutron Source, and for the Fusion Pilot Plant study that will help focus program direction and efforts. Furthermore, the surge of venture capital investment into the private fusion industry start-ups over the last few years is anticipated to help accelerate all aspects of the fusion energy development. This twelfth annual report of the ORNL (Oak Ridge National Laboratory) Fusion Reactor Materials Program summarizes the accomplishments in Fiscal Year 2023 (FY2023). The year was the first to return to full post-COVID-restriction operations, with students and international assignees no longer impacted by COVID restrictions, as in FY20-21-22. Following the pattern of planning used in this program, work for the year FY2023 focused on having the data and productivity to support a strong presence at the International Conference on Fusion Reactor Materials (ICFRM) 21, organized by Spain and occurred in October 2023. Twenty-nine ORNL-led abstracts were submitted, with all accepted. Four were invited presentations, nine contributed oral, fourteen posters, and two withdrawn due to unforeseen circumstances. Additionally, nine external abstracts with ORNL contributing authors were presented. These will be reported in the FY24 report next year.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments

The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. Our ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.

artificial intelligence↗
DOE OSTI2022

Multimodal X-ray nano-spectromicroscopy analysis of chemically heterogeneous systems

Abstract Understanding the nanoscale chemical speciation of heterogeneous systems in their native environment is critical for several disciplines such as life and environmental sciences, biogeochemistry, and materials science. Synchrotron-based X-ray spectromicroscopy tools are widely used to understand the chemistry and morphology of complex material systems owing to their high penetration depth and sensitivity. The multidimensional (4D+) structure of spectromicroscopy data poses visualization and data-reduction challenges. This paper reports the strategies for the visualization and analysis of spectromicroscopy data. We created a new graphical user interface and data analysis platform named XMIDAS (X-ray multimodal image data analysis software) to visualize spectromicroscopy data from both image and spectrum representations. The interactive data analysis toolkit combined conventional analysis methods with well-established machine learning classification algorithms (e.g. nonnegative matrix factorization) for data reduction. The data visualization and analysis methodologies were then defined and optimized using a model particle aggregate with known chemical composition. Nanoprobe-based X-ray fluorescence (nano-XRF) and X-ray absorption near edge structure (nano-XANES) spectromicroscopy techniques were used to probe elemental and chemical state information of the aggregate sample. We illustrated the complete chemical speciation methodology of the model particle by using XMIDAS. Next, we demonstrated the application of this approach in detecting and characterizing nanoparticles associated with alveolar macrophages. Our multimodal approach combining nano-XRF, nano-XANES, and differential phase-contrast imaging efficiently visualizes the chemistry of localized nanostructure with the morphology. We believe that the optimized data-reduction strategies and tool development will facilitate the analysis of complex biological and environmental samples using X-ray spectromicroscopy techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Language models for materials discovery and sustainability: Progress, challenges, and opportunities

Significant advancements have been made in one of the most critical branches of artificial intelligence: natural language processing (NLP). These advancements are exemplified by the remarkable success of OpenAI’s GPT-3.5/4 and the recent release of GPT-4.5, which have sparked a global surge of interest akin to an NLP gold rush. Here, in this article, we offer our perspective on the development and application of NLP and large language models (LLMs) in materials science. We begin by presenting an overview of recent advancements in NLP within the broader scientific landscape, with a particular focus on their relevance to materials science. Next, we examine how NLP can facilitate the understanding and design of novel materials and its potential integration with other methodologies. To highlight key challenges and opportunities, we delve into three specific topics: (i) the limitations of LLMs and their implications for materials science applications, (ii) the creation of a fully automated materials discovery pipeline, and (iii) the potential of GPT-like tools to synthesize existing knowledge and aid in the design of sustainable materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

DOE ART Graphite R&D Program

Graphite overview: Material science, supply chain facts, nuclear material science, licensing thoughts

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗