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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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140 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Modernizing the Nuclear Industry and New Ways of Working

Nuclear energy is recognized as the most feasible energy source towards achieving nation-wide net-zero goals (COP 208, MIT). Additionally, the demand for reliable, consistent energy supply is soaring with the construction of energy hungry data centers and AI tools. This means the construction of new nuclear and the continuation of current nuclear are a high national priority. However, this leaves the nuclear industry with a workforce challenge. The rising demand for nuclear power is threatened by an underpopulated workforce. There exist a few contributors to the decreased workforce such as skewed workforce demographics resulting in large-scale workforce retirement. Also, some plants have observed difficulty hiring and retaining skill sets that typically comprise the nuclear power workforce – specifically skilled craftsman. Those who are graduating with desirable skills are seeking employment in sectors that are more modern and culturally more aligned with younger generational values. Lastly, within the nuclear sector, skilled employees will be a competitive commodity as advanced plants come online offering work environments that incorporate modern technologies, skill sets, and opportunities for career advancement. These factors emphasize the need for legacy plants to modernize with a focus on the workplace environment and culture that meets younger generations’ skills, cultural expectations, and desire for advancement opportunities. Nuclear plants are actively engaged in deploying advancements that improve the management of systems, structures, and components as well as process improvements and technologies that can reduce the costs of operation and maintenance. However, the advancements must also be analyzed, reviewed and deployed in a manner that considers the impact on how people within the organization collaborate, communicate, make decisions, and solve problems. Neglecting to consider the cultural impact of modernization risks poor adoption, unrealized opportunities, or insignificant change toward helping attract new employees. The traditional way of working is not necessarily the way new generations want to engage with their employer. For instance, in a survey performed by North American Young Generation in Nuclear the top three reasons for younger nuclear employees to seek other job opportunities was seeking better work-life balance, a lack of advancement opportunities, and work culture and leadership style differences (Smyth et al. 2022). Integrated Operations for Nuclear (ION) is an approach for the nuclear industry to create long-term strategic modernization plans and analyze each advancement for the impact on people and processes and measure how those impacts flow up to support high-level, long-term plant goals and requirements. One principle of ION is to replace the current labor-centric operation style in legacy nuclear plants with data-centric collaborative operation styles. This paper will explain how transitioning operations to leverage centralized skills and responsibility, multi-skilled teams, and collaborative decision making can flatten the hierarchical structure in plants, enable greater individual efficacy while also offering more experiences and advancement opportunities to staff. Adopting the ION way of working shifts the industry mind-set towards more networked, collaborative, and modern way of working that will attract skilled, next-generation workers to operate the legacy nuclear fleet.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Data Analytics Methods to Measure Plant Outage Resilience

Every 18 or 24 months nuclear power plants (depending on plant configuration, pressurized or boiling water reactor respectively) undergo a period of outage where the plant is taken offline and a large number of maintenance and surveillance activities (that cannot be performed while plant is running) are performed in typically 2–3 weeks. Planning of a plant outage is very challenging since all the activities are required to be performed in the shortest amount of time given available resources (typically contractor crews hired for the duration of the outage). Consequently, plant outages can be costly due the actual loss of power generation and crew costs and, because of it, there is a need to maximize resource usage in the outage planning phase and reduce the risk of outage delays. This paper is addressing these needs by providing a set of analytical methods designed to analyze plant outage schedule and identify critical elements based on available resources (time and crews). These methods are based on natural language processing and optimization algorithms. In this respect, two classes of methods have been developed: one that focuses on the time resource and how variability in the time to complete outage tasks may impact outage delays, and one that minimizes the risk of outage delays by integrating available resources to assess when daily activities should be performed.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Fermilab VALOR Program

The Veteran Applied Laboratory Occupational Retraining (VALOR) Program is an expansion of Fermilab’s established VetTech internship program originally initiated in 2016. The enhanced program seeks to recruit, hire, train, and retain veterans and transitioning service members by providing multiple entry points for full time STEM-based careers at Fermilab. VALOR is comprised of four paid internships/apprenticeships supplemented by professional development opportunities.

Fermilab, Fermilab↗
DOE OSTI2022

Creating superconductivity in WB 2 through pressure-induced metastable planar defects

High-pressure electrical resistivity measurements reveal that the mechanical deformation of ultra-hard WB 2 during compression induces superconductivity above 50 GPa with a maximum superconducting critical temperature, T c of 17 K at 91 GPa. Upon further compression up to 187 GPa, the T c gradually decreases. Theoretical calculations show that electron-phonon mediated superconductivity originates from the formation of metastable stacking faults and twin boundaries that exhibit a local structure resembling MgB 2 (hP3, space group 191, prototype AlB 2 ). Synchrotron x-ray diffraction measurements up to 145 GPa show that the ambient pressure hP12 structure (space group 194, prototype WB 2 ) continues to persist to this pressure, consistent with the formation of the planar defects above 50 GPa. The abrupt appearance of superconductivity under pressure does not coincide with a structural transition but instead with the formation and percolation of mechanically-induced stacking faults and twin boundaries. The results identify an alternate route for designing superconducting materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Machine learning of superconducting critical temperature from Eliashberg theory

Abstract The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional superconductors, including the retardation of the interaction and the Coulomb pseudopotential, to predict the critical temperature T c . McMillan, Allen, and Dynes derived approximate closed-form expressions for the critical temperature within this theory, which depends on the electron–phonon spectral function α 2 F ( ω ). Here we show that modern machine-learning techniques can substantially improve these formulae, accounting for more general shapes of the α 2 F function. Using symbolic regression and the SISSO framework, together with a database of artificially generated α 2 F functions and numerical solutions of the Eliashberg equations, we derive a formula for T c that performs as well as Allen–Dynes for low- T c superconductors and substantially better for higher- T c ones. This corrects the systematic underestimation of T c while reproducing the physical constraints originally outlined by Allen and Dynes. This equation should replace the Allen–Dynes formula for the prediction of higher-temperature superconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Developing a complete AI-accelerated workflow for superconductor discovery

The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based Allen-Dynes calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI-accelerated discovery pipeline that incorporates elemental-substitution strategies and machine-learned interatomic potentials, our workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed T c > 5 K. We report the successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery.

Gibson, Jason B. [Quantum Formatics, Cambridge, MA↗
DOE OSTI2021

A15 Nb 3 Si: a ‘high’ T c superconductor synthesized at a pressure of one megabar and metastable at ambient conditions

A15 Nb 3 Si is, until now, the only 'high' temperature superconductor produced at high pressure (~110 GPa) that has been successfully brought back to room pressure conditions in a metastable condition. Based on the current great interest in trying to create metastable-at-room-pressure high temperature superconductors produced at high pressure, we have restudied explosively compressed A15 Nb 3 Si and its production from tetragonal Nb 3 Si. First, diamond anvil cell pressure measurements up to 88 GPa were performed on explosively compressed A15 Nb 3 Si material to trace T c as a function of pressure. T c is suppressed to ~5.2 K at 88 GPa. Then, using these T c (P) data for A15 Nb 3 Si, pressures up to 92 GPa were applied at room temperature (which increased to 120 GPa at 5 K) on tetragonal Nb 3 Si. Measurements of the resistivity gave no indication of any A15 structure production, i.e. no indications of the superconductivity characteristic of A15 Nb 3 Si. This is in contrast to the explosive compression (up to P ~ 110 GPa) of tetragonal Nb 3 Si, which produced 50%–70% A15 material, T c = 18 K at ambient pressure, in a 1981 Los Alamos National Laboratory experiment. This implies that the accompanying high temperature (1000 °C) caused by explosive compression is necessary to successfully drive the reaction kinetics of the tetragonal → A15 Nb 3 Si structural transformation. Our theoretical calculations show that A15 Nb 3 Si has an enthalpy vs the tetragonal structure that is 70 meV atom –1 smaller at 100 GPa, while at ambient pressure the tetragonal phase enthalpy is lower than that of the A15 phase by 90 meV atom –1 . Furthermore, the fact that 'annealing' the A15 explosively compressed material at room temperature for 39 years has no effect shows that slow kinetics can stabilize high pressure metastable phases at ambient conditions over long times even for large driving forces of 90 meV atom –1 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

The 2021 room-temperature superconductivity roadmap

Designing materials with advanced functionalities is the main focus of contemporary solid-state physics and chemistry. Research efforts worldwide are funneled into a few high-end goals, one of the oldest, and most fascinating of which is the search for an ambient temperature superconductor (A-SC). The reason is clear: superconductivity at ambient conditions implies being able to handle, measure and access a single, coherent, macroscopic quantum mechanical state without the limitations associated with cryogenics and pressurization. This would not only open exciting avenues for fundamental research, but also pave the road for a wide range of technological applications, affecting strategic areas such as energy conservation and climate change. In this roadmap we have collected contributions from many of the main actors working on superconductivity, and asked them to share their personal viewpoint on the field. The hope is that this article will serve not only as an instantaneous picture of the status of research, but also as a true roadmap defining the main long-term theoretical and experimental challenges that lie ahead. Interestingly, although the current research in superconductor design is dominated by conventional (phonon-mediated) superconductors, there seems to be a widespread consensus that achieving A-SC may require different pairing mechanisms.

"Toward hot superconductivity"↗
DOE OSTI2020

Remarkable low-energy properties of the pseudogapped semimetal Be 5 Pt

We report measurements and calculations on the properties of the intermetallic compound Be 5 Pt. High-quality polycrystalline samples show a nearly constant temperature dependence of the electrical resistivity over a wide temperature range. On the other hand, relativistic electronic structure calculations indicate the existence of a narrow pseudogap in the density of states arising from accidental approximate Dirac cones extremely close to the Fermi level. A small true gap of order ~ 3 meV is present at the Fermi level, yet the measured resistivity is nearly constant from low to room temperature. We argue that this unexpected behavior can be understood by a cancellation of the energy dependence of density of states and relaxation time due to disorder, and discuss a model for electronic transport. With applied pressure, the resistivity becomes semiconducting, consistent with theoretical calculations that show that the band gap increases with applied pressure. We further discuss the role of Be inclusions in the samples.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High-pressure study of the low- Z rich superconductor Be 22 Re

With T c ~ 9.6K, Be 22 Re exhibits one of the highest critical temperatures among Be-rich compounds.We have carried out a series of high-pressure electrical resistivity measurements on this compound to 30 GPa. The data show that the critical temperature T c is suppressed gradually at a rate of dT c /dP = –0.05K/GPa. Using density functional theory (DFT) calculations of the electronic and phonon density of states (DOS) and the measured critical temperature, we estimate that the rapid increase in lattice stiffening in Be 22 Re overwhelms a moderate increase in the electron-ion interaction with pressure, resulting in the decrease in T c . Furthermore, high-pressure x-ray diffraction measurements show that the ambient pressure crystal structure of Be 22 Re persists to at least 154 GPa.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

High critical field superconductivity at ambient pressure in MoB 2 stabilized in the P6/mmm structure via Nb substitution

Recently it was discovered that, under elevated pressures, MoB 2 exhibits superconductivity at a critical temperature T c as high as 32 K. The superconductivity appears to develop following a pressure-induced structural transition from the ambient pressure $\text{R}\bar{3}$⁢m structure to an MgB 2 -like P6/mmm structure. This suggests that remarkably high T c values among diborides are not restricted to MgB 2 as previously appeared to be the case, and that similarly high T c values may occur in other diborides if they can be coerced into the MgB 2 structure. In this paper, we show that density functional theory calculations indicate that phonon free energy stabilizes the P6/mmm structure over the $\text{R}\bar{3}$⁢m at high temperatures across the Nb 1–x ⁢Mo x B 2 series. X-ray diffraction confirms that the synthesized Nb-substituted MoB 2 adopts the MgB 2 crystal structure. Finally, high magnetic field electrical resistivity measurements and specific heat measurements demonstrate that Nb x ⁢Mo 1–x⁢ B 2 exhibits superconductivity with T c as high as 8 K and critical fields approaching 6 T.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

When more data hurts: Optimizing data coverage while mitigating diversity-induced underfitting in an ultrafast machine-learned potential

Machine-learned interatomic potentials (MLIPs) are becoming an essential tool in materials modeling. However, optimizing the generation of training data used to parametrize the MLIPs remains a significant challenge. This is because MLIPs can fail when encountering local environments too different from those present in the training data. The difficulty of determining a priori the environments that will be encountered during molecular dynamics simulation necessitates diverse, high-quality training data. Here, this study investigates how training data diversity affects the performance of MLIPs using the Ultra-Fast force field (UF 3 ) to model amorphous silicon nitride. We employ expert and autonomously generated data to create the training data and fit four force field variants to subsets of the data. Our findings reveal a critical balance in training data diversity: insufficient diversity hinders generalization, while excessive diversity can exceed the MLIP's learning capacity, reducing simulation accuracy. Specifically, we found that the UF 3 variant trained on a subset of the training data, in which nitrogen-rich structures were removed, offered vastly better prediction and simulation accuracy than any other variant. By comparing these UF 3 variants, we highlight the nuanced requirements for creating accurate MLIPs, emphasizing the importance of application-specific training data to achieve optimal performance in modeling complex material behaviors.

ab initio molecular dynamics↗
DOE OSTI2022

U.S. Hydropower Workforce: Challenges and Opportunities

Over the past four years, the National Renewable Energy Laboratory (NREL) has engaged with the hydropower industry, academia, and students to understand the perspectives and challenges of the U.S. hydropower workforce. This engagement and research were sponsored by the U.S. Department of Energy's (DOE's) Water Power Technologies Office as part of an NREL-led project—Water Power Science, Technology, Engineering, and Mathematics (STEM) to Workforce—which has the goal of developing tools and programs to strengthen the water power workforce pipeline. This report shares findings from that research and provides an update on U.S. hydropower workforce trends as a follow-on to NREL's 2019 report, Workforce Development for U.S. Hydropower: Key Trends and Findings (Keyser and Tegen 2019). Recent data from NREL, the Hydropower Foundation's survey efforts, and the hydropower industry are presented.

13 HYDRO ENERGY↗
DOE OSTI2025

Unraveling fundamental mechanisms of silicon nitride crystallization in microelectronics manufacturing

This research project investigates the fundamental mechanisms of silicon nitride (SiN) crystallization, aiming to enhance the understanding of this critical material in microelectronics manufacturing. Through a collaborative effort between Sandia National Laboratories, the University of Tennessee, and the University of Florida, we developed a comprehensive framework that integrates experimental techniques, atomistic modeling, meso-scale simulations, and an integrated multi-scale model to capture this physical phenomenon on multiple time and length scales . The project developed a new machine learning based atomistic potential and utilized advanced phase field modeling to capture the complexities of polycrystalline growth and the influence of mechanical stresses on crystallization dynamics. By employing a grain tracker algorithm, the meso-scale model effectively identified and tracked individual crystal grains, enabling the simulation of anisotropic growth behaviors reflective of SiN’s physical properties. The integration of atomistic simulations with meso-scale modeling created a powerful multi-scale framework that validated atomistic inputs and enhanced predictive accuracy for crystallization dynamics at larger scales, validated experimentally. This adaptable modeling capability not only accelerates development times by informing manufacturing processes but also serves as a valuable starting point for understanding crystallization physics in similar materials. The insights gained from this research unlock new opportunities for the development of advanced materials tailored for future microelectronics and photonics applications. Overall, this project represents a significant advancement in understanding of fundamental physics of SiN and establishes a foundation for future research in material science, bridging the gap between atomic-level phenomena and macroscopic material behaviors for practical applications.

36 MATERIALS SCIENCE↗