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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A market-oriented database design for critical material research

Material databases are important tools to provide and store information from material research. Rising concerns about supply-chain risks to raw materials presents a need to incorporate raw-material market and end-use application data, beyond basic chemical and physical properties, into a material database. One key challenge for researchers working on critical materials is information scarcity and inconsistency. This paper introduces, as a result of a two-year project, a critical-material commodity database (CMCD) incorporated with a low-code web-based platform that allows easy access for users and simple updates for the authors. The main goal of this project was to educate material scientists on the applications having the most impact on the supply chain and current industrial specifications/markets for each application. The objective was to provide material researchers with harmonized information so that they could gain a better understanding of the market, focus their technologies on an application with a high potential for commercialization, and better contribute to supply-chain risk reduction. While the goal was met with high receptivity, several limitations stemmed from query design, distribution platform, and quality of data source. To overcome some of these limitations and expand on CMCD's potential, we are building a public webpage with an improved interface, better data organization, and higher extensibility.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Developing and Evaluating Energy Justice Metrics for Early-Stage Materials Research

Materials science is a central component of early-stage research and development of virtually all clean energy technologies. But as much as material breakthroughs often hold the key to high efficiencies, long lifetimes, and high stability in eventual devices, early-stage choices about material types, structures, and processing can also serve to lock in long-term social and equity impacts of deployed energy technologies. Thus, to achieve a just and sustainable energy transition, tools to assess the energy justice impacts of early-stage materials research are critical. Here, we discuss development of the Justice Underpinning Science and Technology Research (JUST-R) metrics framework - a suite of metrics targeted at early-stage researchers to assess energy justice considerations in their work. The framework is evaluated for its appeal to researchers and effectiveness at promoting integration of energy justice into research through case studies, which reveal its ability to broaden researcher perspectives and key avenues for future improvement.

energy justice↗
DOE OSTI2022

Machine learning in nuclear materials research

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes with associated transmutations, high temperature and temperature gradients, mechanical stresses, and corrosive coolants. They also have a wide range of microstructural and chemical makeups, resulting in multifaceted and often out-of-equilibrium interactions. Machine learning (ML) is increasingly being used to tackle these complex time-dependent interactions and aid researchers in developing models and making predictions, sometimes with better accuracy than traditional modeling that focuses on one or two parameters at a time. Conventional practices of acquiring new experimental data in nuclear materials research are often slow and expensive, limiting the opportunity for data-centric ML, but new methods are changing that paradigm. Here we review high-throughput computational and experimental data approaches, especially robotic experimentation and active learning that is based on Gaussian process and Bayesian optimization. We show ML examples in structural materials (e.g., reactor pressure vessel (RPV) alloys and radiation detecting scintillating materials) and highlight new techniques of high-throughput sample preparation and characterizations, and automated radiation/environmental exposures and real-time online diagnostics. Herein, this review suggests that ML models of material constitutive relations in plasticity, damage, and even electronic and optical responses to radiation are likely to become powerful tools as they develop. Finally, we speculate on how the recent trends of using natural language processing (NLP) to aid the collection and analysis of literature data, interpretable artificial intelligence (AI), and the use of streamlined scripting, database, workflow management, and cloud computing platforms that will soon make the utilization of ML techniques as commonplace as the spreadsheet curve-fitting practices of today.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A new capability facilitating nuclear materials research: the Activated Materials Laboratory at the Advanced Photon Source

The Activated Materials Laboratory (AML), located in the Long Beamline Building (LBB) of the Advanced Photon Source (APS) of Argonne National Laboratory (ANL), serves as a centralized radiological facility for preparing radioactive samples for APS beamline experiments. The AML is equipped to receive shipments, handle open-form radioactive materials, encapsulate samples, and transport samples to-and-from beamline end-stations. The AML works closely with users and the APS radiological safety committee to make sure the safe conduct of experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Artificial intelligence for materials research at extremes

Abstract Materials development is slow and expensive, taking decades from inception to fielding. For materials research at extremes, the situation is even more demanding, as the desired property combinations such as strength and oxidation resistance can have complex interactions. Here, we explore the role of AI and autonomous experimentation (AE) in the process of understanding and developing materials for extreme and coupled environments. AI is important in understanding materials under extremes due to the highly demanding and unique cases these environments represent. Materials are pushed to their limits in ways that, for example, equilibrium phase diagrams cannot describe. Often, multiple physical phenomena compete to determine the material response. Further, validation is often difficult or impossible. AI can help bridge these gaps, providing heuristic but valuable links between materials properties and performance under extreme conditions. We explore the potential advantages of AE along with decision strategies. In particular, we consider the problem of deciding between low-fidelity, inexpensive experiments and high-fidelity, expensive experiments. The cost of experiments is described in terms of the speed and throughput of automated experiments, contrasted with the human resources needed to execute manual experiments. We also consider the cost and benefits of modeling and simulation to further materials understanding, along with characterization of materials under extreme environments in the AE loop. Graphical abstract AI sequential decision-making methods for materials research: Active learning, which focuses on exploration by sampling uncertain regions, Bayesian and bandit optimization as well as reinforcement learning (RL), which trades off exploration of uncertain regions with exploitation of optimum function value. Bayesian and bandit optimization focus on finding the optimal value of the function at each step or cumulatively over the entire steps, respectively, whereas RL considers cumulative value of the labeling function, where the latter can change depending on the state of the system (blue, orange, or green).

36 MATERIALS SCIENCE↗
DOE OSTI2024

Are We on Track for 2050? A Materials Research & Sustainability Perspective

In commemoration of the Materials Research Society (MRS)'s 50th anniversary, the 2050 panel hosted a discussion to reflect on the past, present, and future of sustainability and the role of materials research and development. Three panelists discussed their views, based on their expertise, about future challenges and lessons from the past. Sustainable development is a broad topic; therefore, the discussion centered on their experience as material researchers and their efforts for a better and greener future. This work is developed in collaboration with the co-authors team, highlighting the need for accelerating research and development efforts, especially in materials science and applications, fostering interdisciplinary partnerships, and mobilizing collective action to address the complex and interconnected sustainability challenges that humanity is currently facing.

ENERGY PLANNING, POLICY, AND ECONOMY,ENVIRONMENTAL↗
DOE OSTI2020

Partial support of the Condensed Matter and Materials Research Committee. Final progress report

The Condensed Matter and Materials Research Committee (CMMRC) is one of five standing committees of the National Academies’ Board on Physics and Astronomy (BPA) and is charged with assisting the BPA in achieving its goals—monitoring the health of physics and astronomy, identifying important new developments at the scientific forefronts, fostering interactions with other fields, strengthening connections to technology, facilitating effective service to the nation, and enhancing education in physics. CMMRC carries out its functions in several ways – providing expert advice to the BPA in monitoring the health of condensed matter and materials research and providing perspective on issues that arise in those fields; assisting other National Academies’ boards in developing proposals and initiating studies that impact these fields; providing long-term stewardship of National Academies reports that it has played a role in initiating and that are of importance to the condensed matter and materials research communities, and providing a means for dialog with federal agencies on these and related fields.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Light Water Reactor Sustainability Program Materials Research Pathway Technical Program Plan

The Materials Research (MR) Pathway within the Light Water Reactor Sustainability (LWRS) Program is charged with performing the research and development (R&D) to develop the scientific basis for understanding and predicting long-term environmental degradation behavior of materials in nuclear reactors. Furthermore, it is essential to use the mechanistic understanding of degradation phenomena in materials to develop mitigation, repair, and new material alternatives for existing components. The work will provide data and methods to assess performance of systems, structures, and components (SSCs) essential to safe and sustained reactor operations. The R&D products developed from the LWRS Program will be used by utilities, industry groups, and regulators to inform operational and regulatory requirements for materials in reactor SSCs subjected to long-term operation conditions, providing key input to both regulators and industry. The intent of this research is to help in reducing the operating costs, which may be in the form of offset maintenance costs due to better predictive models for component lifetimes, improved analysis of materials through nondestructive evaluation, reduced costs for repairs, or extended performance of plants through the selection of improved replacement materials. To best achieve this, industry experience and guidance are important as is their role in coordinated or collaborative research projects. The objectives of this report are to describe the motivation and organization of the MR Pathway within the LWRS Program, provide details on the individual research tasks within the MR Pathway, describe the outcomes and deliverables of the MR Pathway, including recent technical highlights and progress, and list the requirements for performing the research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Light Water Reactor Sustainability: Program Materials Research Pathway (FY22 Technical Program Plan)

The Materials Research (MR) Pathway within the Light Water Reactor Sustainability (LWRS) program is charged with performing R&D to develop the scientific basis for understanding and predicting the long-term environmental degradation behavior of materials in nuclear reactors. Furthermore, the mechanistic understanding of degradation phenomena in materials must be leveraged to develop mitigation and repair strategies and new material alternatives for existing components. This research will provide data and methods to assess the performance of systems, structures, and components essential to safe and sustained reactor operations. The R&D products developed from the LWRS program will be used by stakeholders—including utilities, industry groups, and regulators—to inform operational and regulatory requirements for materials in reactor systems, structures, and components subjected to long-term operation conditions, providing key inputs to regulators and industry. Therefore, the intent of this research is to provide options to reduce the operating costs, which may be in the form of offset maintenance costs due to better predictive models for component lifetimes, improved analyses of materials through nondestructive evaluation, reduced costs for repairs, or extended performance of plants through the selection of improved replacement materials. To provide the best options, industry experience and guidance are important because of their role in coordinated or collaborative research projects. The objectives of this report are to describe the motivation and organization of the MR Pathway within the LWRS program; provide details on the individual research tasks within the MR Pathway; describe the outcomes and deliverables of the MR Pathway, including recent technical highlights and progress; and describe the requirements for performing this critically important research.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Towards physics-informed explainable machine learning and causal models for materials research

From emergent material descriptions to estimation of properties stemming from structures to optimization of process parameters for achieving best performance – all key facets of materials science and related fields have experienced tremendous growth with the introduction of data-driven models. This gradual progression goes at par with developments of machine learning workflows, from purely data-driven shallow models to those that are well-capable in encoding more complex graphs, symbolic representations, invariances, and positional embeddings. Furthermore, this perspective aims at summarizing strategic aspects of such transitions while providing insights into the requirements of bringing in explainable, interpretable predictive models, and causal learning to aid in materials design and discovery. Although the focus remains on a variety of functional materials by providing a handful of case studies, the applications of such integrated methodologies are universal to facilitate fundamental understandings of materials physics while enabling autonomous experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2020

University Partnership Program for Scintillator Materials Research

The Scintillation Detection Development (SDD) group of the Particle Physics Division (PPD) at Fermi National Accelerator Laboratory (Fermilab) conducts research and development work in the field of materials that exhibit scintillation properties for use in particle detection and identification in nuclear and high energy physics experiments and applications. SDD has established a University Partnership Program for Scintillator Materials Research (Program), to facilitate collaboration with faculty and students from local universities. The collaboration between the SDD group and Dominican University will address the development of new plastic scintillating materials in two ways: 3.1. Synthesis of new organic fluorescent compounds to test with plastics commonly used in scintillation applications 3.2. Preparation and testing of commercially available plastics known for their resilience to radiation and rarely used in scintillation applications

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Applying energy justice metrics to photovoltaic materials research

Abstract Achieving the energy transition sustainably requires addressing how new technologies may impact justice in the energy system. The Justice Underpinning Science and Technology Research (JUST-R) metrics framework was recently proposed to aid researchers in considering justice in early-stage research on energy technologies; however, case study evaluations of the framework revealed a desire from researchers to see metrics specialized to particular fields of study. Here, we refine metrics from the JUST-R framework to enhance its applicability to photovoltaic (PV) materials research. Metrics are reorganized to align with aspects of the research process (e.g., research team or source materials). For most metrics, baseline values are suggested to enable researchers to compare their project to competing technologies or standards at their institutions. These refinements are integrated into a tool to facilitate easier understanding and evaluation of justice considerations in early-stage PV research, which can serve as a template for evaluating other energy technologies. Graphical abstract

14 SOLAR ENERGY↗
DOE OSTI2026

Evaluating Technology Adoption Risks in Early-Stage Materials Research

Development of new technologies often begins with fundamental materials science research. Decisions at this stage can shape factors related to the eventual adoption readiness of the technology, such as process scalability or materials availability. Here we present the early-Stage Technology Evaluation for Adoption Risks (STEAR) framework as a method for qualitatively assessing metrics spanning four categories of adoption risks: value proposition, market acceptance, resource maturity, and license to operate. We conduct a case study applying STEAR to different methanol production processes at a range of technology readiness levels and demonstrate how the assessment identifies key challenges related to adoption readiness. Finally, we discuss efforts to expand the applicability and utility of STEAR, including focus group feedback and complementary quantitative analysis methods.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Ultrasonic Resonance Techniques for Materials Research

Mechanical resonances are directly related to the physical behavior of a system at the bulk and microscopic levels. In materials science, resonant ultrasound spectroscopy (RUS) has long been a preferred nondestructive method to study mechanical resonances of solids and precisely measure quantitative material properties, namely elasticity. In recent years, advances in computational power and hardware have enabled RUS to be relevant for an increasing range of applications, such as advanced manufacturing. An extension of this technique, nonlinear RUS (NRUS), has been demonstrated to provide unmatched sensitivity to early-stage damage. NRUS was originally developed to probe geologic materials but has become a vital tool in nondestructive evaluation and materials research, offering a powerful means of quantifying and characterizing microstructural nonlinearity in a broad range of materials. This review summarizes recent developments and growth opportunities in RUS and NRUS techniques, modeling, and applications across a wide range of material systems including metals, composites, geomaterials, and explosives.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Materials Research for Battery Recycling

This presentation for the summer 2024 class of Energy Execs offers a summary of materials research work for battery recycling conducted at NREL.

battery recycling↗
DOE OSTI2025

ALchemist (Active Learning Toolkit for Chemical and Materials Research) [SWR-25-102]

ALchemist is a modular Python toolkit that brings active learning and Bayesian optimization to experimental design in chemical and materials research. It is designed for scientists and engineers who want to efficiently explore or optimize high-dimensional variable spaces—without writing code—using an intuitive graphical interface.

Coatney, Caleb [National Renewable Energy Laborato↗
DOE OSTI2022

Capability Needs for Irradiated and Radioactive Materials Research (Ad hoc Committee Summary Report)

It is essential for the U.S. nuclear energy community to have access to world-leading equipment suitable for conducting research on both nuclear fuels and structural materials, including neutron irradiated–and therefore activated–materials. To address future research infrastructure requirements to support the DOE-NE mission, the Office of Reactor Fleet and Advanced Reactor Deployment established an ad hoc committee to gather information on potential capability gaps for radioactive materials and radiation effects research. This document summarizes the discussions of the committee addressing the high-level challenges in irradiated and radioactive materials research and the capabilities needed to address these challenges. After considering the various needs, the committee agreed on the four top-level targets and priority capability gaps.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗