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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Manufacturing Li 2 TiO 3 -based tritium breeder materials by volume-controlled spark plasma sintering with an optimized microstructure

Multifunctional ceramic breeder materials are highly desirable for the deuterium-tritium fusion to achieve high efficiency in breeding tritium through neutron irradiation of lithium-containing blankets. Li 2 TiO 3 displays unique attributes as a potential ceramic breeder material. An optimized microstructure with three-dimensional interconnected pore structure is required for rapid transport of the tritium for effective fuel cycle, which however enviably results in the degradation of the thermal-mechanical properties of the breeding materials. In this work, nanocrystalline porous Li 2 TiO 3 ceramic pellets with controlled porosities of 14% and 20% are manufactured by volume-controlled spark plasma sintering. An optimized 3D interconnected pore structure is achieved consisting of both micro-sized pores and nano-sized pores embedded in nanocrystalline matrix, which could be beneficial to facilitate easy removal of bred T and He. Further, the 3D interconnected porous structure is well maintained upon isothermal annealing of the SPS-fabricated pellets at relevant operation temperature of the solid breeding materials. Single-phasic porous pellets also display enhanced thermal-mechanical properties, superior to current state-of-the-art materials which establish their potential as a promising tritium breeder material for nuclear fusion applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

An Accelerated Testing and Analysis Framework for Qualification of Battery Materials Part I: A Case Study with LFP Cathodes

The growing demand for batteries used within automotive, aviation, and grid applications has exacerbated the need to supplement critical battery material feedstocks, such as those for anode and cathode active materials. New or supplementary material sources, however, universally comprise unique properties that can affect the lifetime and performance of resultant batteries. Even minor differences between new sources and established supplies can delay qualification, making it difficult for new suppliers to commercialize and resulting in a less resilient supply chain. Accordingly, the influence of composition, microstructure, and morphology on electrochemical performance should be characterized quickly and accurately to accelerate commercialization of new sources. This work introduces a tiered framework to assess new material viability and understand the influence of physicochemical properties on battery performance. The Tier 1 testing described here is rapid and low-effort to recognize materials with fundamental flaws and potentially disqualify them. Later testing would require more effort but provide higher-fidelity information with a goal of application-based validation. A case study examining commercial sources of LiFePO4 (LFP) is presented, using Tier 1 of the protocol to identify rapid electrochemical and physicochemical signals that correlate with performance and provide early go/no-go decisions for LFP materials without requiring long-term cycling.

25 ENERGY STORAGE↗
DOE OSTI2026

Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges

Developing high-performance materials is critical for diverse energy applications to increase efficiency, improve sustainability and reduce costs. Classical computational methods have enabled important breakthroughs in energy materials development, but they face scaling and time-complexity limitations, particularly for high-dimensional or strongly correlated material systems. Quantum computing (QC) promises to offer a paradigm shift by exploiting quantum bits with their superposition and entanglement to address challenging problems intractable for classical approaches. This Perspective discusses the opportunities in leveraging QC to advance energy materials research and the challenges QC faces in solving complex and high-dimensional problems. We present cases on how QC, when combined with classical computing methods, can be used for the design and simulation of practical energy materials. We also outline the outlook for error-corrected, fault-tolerant QC capable of achieving predictive accuracy and quantum advantage for complex material systems.

Algorithms↗
DOE OSTI2025

All 2D Material Printed Diodes and Circuits on Paper for Sustainable Electronics

Sustainable electronics aim to reduce environmental impact by using ecofriendly materials, energy-efficient manufacturing, and recyclable components. However, existing approaches rely on complex, resource-intensive methods, rare metals, or nanomaterials with limited stability as well as plastic substrates, raising sustainability issues. Solution-processed two-dimensional (2D) materials offer a promising alternative: water-based and biocompatible conductive, semiconductive, and insulating 2D material inks can be produced with scalable techniques and are suitable for the fabrication of fully printed devices on low-cost and biodegradable paper substrates. However, 2D material only and fully printed diodes on paper have not yet been reported. Here, we demonstrate fully inkjet-printed 2D material-based diodes on paper using metal-insulator-semiconductor and metal-insulator-metal-semiconductor architectures. Water-based graphene and MoS2 inks, prepared by liquid-phase exfoliation, are used for the metallic and insulating films, while electrochemical exfoliation is used to produce the semiconducting MoS2 ink. The highest forward-to-reverse current ratio obtained is 330 (at ±2 V), while the forward current density is 1 mA/cm2 (at 1 V), making the diode performance comparable to the best solution-processed diodes reported so far. However, in contrast to previous works, fabrication occurs entirely at room temperature and ambient pressure, without using high-pressure sputtering, thermal evaporation, and any precious metal ink. The devices maintain stable performance under bending up to strain of 4% over 10,000 cycles. Finally, the diodes are successfully integrated with other 2D-material based electrical components to realize fully printed RC circuits, differentiators, integrators, and AC-to-DC converters onto paper, hence demonstrating the suitability of our approach for sustainable and disposable integrated circuits.

2D materials↗
DOE OSTI2024

Targeted materials discovery using Bayesian algorithm execution

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO 2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

42 ENGINEERING↗
DOE OSTI2025

High-throughput and data-driven search for stable optoelectronic AMSe 3 materials

The rapid advancement in emerging optoelectronic technologies demands highly efficient, affordable, and ecofriendly materials. In this context, ternary chalcogenides, especially ternary selenides, show early promise as a material class due to their stability and remarkable electronic, optical, and transport properties. In this work, we integrate first-principles-based high-throughput computations with machine learning (ML) techniques to predict the thermodynamic stability and optoelectronic properties of 920 valency-satisfied selenide compounds. Through investigating polymorphism, our study reveals the edge-sharing orthorhombic Pnma phase (NH 4 CdCl 3 -type) as the most stable structure for most ternary selenides. High-fidelity supervised ML models are trained and tested to accelerate stability and band gap predictions. These data-driven models pin down the most influential features that dominantly control key material characteristics. The multistep high-throughput computations identify the ternary selenides with optimal direct band gaps, light carrier masses, and strong optical absorption edges. The extensive materials screening considering phase stability, toxicity, and defect tolerance, finally identifies the seven most suitable candidates for photovoltaic applications. Two of these final compounds, SrZrSe 3 and SrHfSe 3 , have already been synthesized in a single-phase form, with the latter showing an optically suitable band gap, aligning well with our findings. The non-adiabatic molecular dynamics reveal sufficiently long photoexcited charge carrier lifetimes (on the order of nanoseconds) in some of these selected selenide materials, indicating their exciting characteristics. Overall, our study suggests a robust in silico framework that can be extended to screen large datasets of various material classes for identifying promising photoactive candidates.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models

Spectroscopy techniques such as x-ray absorption near edge structure (XANES) provide valuable insights into the atomic structures of materials, yet the inverse prediction of precise structures from spectroscopic data remains a formidable challenge. In this study, we introduce a framework that combines generative artificial intelligence models with XANES spectroscopy to predict three-dimensional atomic structures of disordered systems, using amorphous carbon (a-C) as a model system. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method, to predict 3D structures of disordered materials from a target property. For demonstration, we apply the model to identify the atomic structures of a-C as a representative material system from the target XANES spectra. We show that conditional generation guided by XANES spectra reproduces key features of the target structures. Furthermore, we show that our model can steer the generative process to tailor atomic arrangements for a specific XANES spectrum. Finally, our generative model exhibits a remarkable scale-agnostic property, thereby enabling generation of realistic, large-scale structures through learning from a small-scale dataset (i.e. with small unit cells). Our work represents a significant stride in bridging the gap between materials characterization and atomic structure determination; in addition, it can be leveraged for materials discovery in exploring various material properties as targeted.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility

One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision requires both providing detailed uncertainty quantification (model prediction errors and domain of applicability) and making models readily usable. At present, it is common practice in the community to assess ML model performance only in terms of prediction accuracy (e.g. mean absolute error), while neglecting detailed uncertainty quantification and robust model accessibility and usability. Here, we demonstrate a practical method for realizing both uncertainty and accessibility features with a large set of models. We develop random forest ML models for 33 materials properties spanning an array of data sources (computational and experimental) and property types (electrical, mechanical, thermodynamic, etc). All models have calibrated ensemble error bars to quantify prediction uncertainty and domain of applicability guidance enabled by kernel-density-estimate-based feature distance measures. All data and models are publicly hosted on the Garden-AI infrastructure, which provides an easy-to-use, persistent interface for model dissemination that permits models to be invoked with only a few lines of Python code. We demonstrate the power of this approach by using our models to conduct a fully ML-based materials discovery exercise to search for new stable, highly active perovskite oxide catalyst materials.

domain of applicability↗
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

Frontiers in Magnetic Materials

Magnetism is crucial to many modern technologies, a driver for condensed matter physics research and one of the most remarkable and diverse properties of matter. We propose to develop understanding of novel magnetism and magnetic related behavior in materials and use this to accelerate the discovery of forefront magnetic materials. The approach is via the connection of magnetic properties to specific structures and materials. Topics that will be addressed are (1) Metallic magnetic materials with unusually low carrier concentrations and/or moments (2) Magnetism arising from unusual chemistry including 4d and 5d magnetism and (3) Materials with strong spin-fluctuations, which can lead to quantum criticality, spin-fluctuation induced superconductivity and other novel quantum behavior. These topics overlap, for example, the 4d ruthenates include ferromagnets (perovskite SrRuO 3 ), extremely high ordering temperature antiferromagnets (honeycomb lattice SrRu 2 O 6 ) and well as quantum materials with strong spin fluctuations (layered perovskite Sr 2 RuO 4 and Sr 3 Ru 2 O 7 ). We will use of density calculations to connect magnetic properties with chemistry and structure and employ phenomenological theories to extend these results to properties that are not directly given by direct first principles methods and we will conduct tests to explore the limitations of density functional approximations and new functionals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning Thermodynamics And Kinetics of Defects For Accelerated Materials Discovery

Atomistic defects play a pivotal role in functional and structural materials’ performance across a myriad of technology applications. Quantitative prediction of the thermodynamics and kinetics of defect formation and migration, respectively, typically requires accurate but expensive first-principles approaches, such as density functional theory (DFT). Their computational expense limits the throughput needed to perform high-throughput materials discovery/screening exercises or to perform materials modeling tasks relying on extensive sampling techniques. Therefore, in this Sandia National Laboratories Laboratory Directed Research and Development (LDRD) project (Project #229366), we developed a variety of machine learning techniques, trained on density functional theory calculations, to accelerate the discovery and modeling of materials in which vacancy and interstitial defects primarily dictate material performance. These include applications such as metal oxides for water-splitting or mixed ionic-electronic conduction, metal hydrides for hydrogen storage, and transition metal dichalcogenides for electronics, and the approaches developed herein can further be applied to many other domains that similarly depend on materials’ thermodynamic and kinetic defect properties for their desired functionality.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Advanced Materials and Manufacturing Technologies (AMMT) 2025 Roadmap

The mission of the Advanced Materials and Manufacturing Technologies (AMMT) program is to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies in support of the United States (US) leadership in a broad range of nuclear energy applications. The vision of the AMMT program is the expansion of reliable and economical nuclear energy enabled by advanced materials and manufacturing technologies. Four major goals were set to realize the mission and vision of the AMMT program, including: (1) develop advanced materials and manufacturing technologies that have cross-reactor applications, (2) establish and demonstrate a rapid qualification framework that supports diverse materials and manufacturing technology needs, (3) evaluate materials performance in a range of nuclear environments, and (4) accelerate commercialization of new technologies through technology maturation. The program is designed to deliver solutions that support a wide range of reactor technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Plasmon and Photon Excitations in Two-Dimensional and Layered Materials

Light-matter interactions in layered and two-dimensional materials allows one to achieve extreme optical confinement approaching the atomic scale, enabling exploration of new materials phenomena. Layered narrow bandgap and zero bandgap materials, such as black phosphorus and graphene, support unusual and intriguing quantum-confined electronic states in thin layers and surface electronic states. The incomplete screening of applied electrostatic fields in ultrathin materials permits the exploration of light-matter interactions at high electric fields and over a wide range of carrier densities in a single sample, facilitating exploration of the optical and plasmonic properties of ultrathin and layered materials under electrochemical potential control. We have made advances in understanding the nature of optical interband and intraband plasmon excitations in layered materials such as graphene, black phosphorus, molybdenum diselenide, and molybdenum ditelluride.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Environmental Durability Assessment and Development of Subcomponent Test Methods for Advanced Materials in Marine Energy Applications

Marine energy developers are beginning to rely more heavily on advanced composite materials for their load-bearing structures. Understanding how these materials respond and degrade in harsh marine environments is a complex problem. Much research has been performed at smaller laboratory scales to understand the fundamental physical and chemical degradation mechanisms, but little structural testing research has been performed at larger scales to understand how these physical and chemical interactions scale and how they should be accounted for in the design process. The Advanced Materials project is a multilaboratory research collaboration that addresses these issues with the intention of reducing risks and barriers to marine energy developers wanting to adopt advanced composite materials. The National Laboratory of the Rockies has been playing a leading role in developing larger-scale testing infrastructure and test methods to validate complex numerical degradation models and standardize testing procedures to do so. This paper reviews past structural testing activities, how they have grown into current research activities, and how they will continue to evolve to directly address industry needs. This paper covers key research areas - large-scale water absorption and subsequent structural validation testing, qualification of bolted and adhesive connections, and combined submerged fatigue testing at increasing scales. It provides insights into the complex requirements for testing infrastructure and test method development to effectively qualify materials and certify marine energy structures for harsh marine environments. This research program paves the way for better guidance and standardization within the industry when adopting advanced composite materials.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Spectral Analysis of Regular Material Point Method and its Application to Study High Pressure Reverse Osmosis Membrane Compaction and Embossing

Material Point Method (MPM) is gaining widespread interest in applied continuum mechanics. The fact that all the continuum properties are stored on the particles (or material points) and the governing equations are solved on these material points makes MPM extremely suited to problems involving severe material deformations, such as crack propagation, soil movement, and fluid flows. Despite its popularity, only a few studies have focused on the numerical properties of MPM. This presentation introduces a global spectral analysis of the regular material point method. Contrary to previous studies, the analysis focuses on the numerical properties of the method in the spectral space. The amplification factor is derived as a function of the non- dimensional wave numbers. It provides insights into the stability and dissipative properties of the method for various CFL and Fourier numbers. The effect of the grid shape functions, number of particles per cell and their locations inside the grid cell are also analyzed. The EXAGOOP MPM solver (https://github.com/NREL/Exagoop.git) is developed at the National Renewable Energy Laboratory as a part of the NAWI UHPRO project and is based on the AMReX framework. A single-level, uniform cartesian grid is used as the background mesh, while the particle class in AMReX is used to manage the material point operations. Linear hat and B-splines are used as grid shape functions, while the time integration is performed using explicit Euler time integration. EXAGOOP is both CPU and GPU compatible and has been demonstrated to work well on multiple compute architectures. The performance of EXAGOOP on various computing architectures is presented along with its application to study compaction and embossing of high-pressure reverse osmosis membranes. The MPM solution accurately reproduces the membrane deformation. The deformed pore size and structure simulated using MPM also agree well with experimental SEM images.

material point method↗
DOE OSTI2024

Femto-second Laser’s Enabling New Length Scale Fabrications for Rapid Post Irradiation Examination of Materials: Concluding LDRD Project Poster

Mechanical testing campaigns are required to qualify materials for advanced reactor conditions, yet economical and safety limitations restrict the number of standardized mechanical tests that can be performed. Reducing the size of the sample is one approach to addressing these challenges and to accelerating testing. Previous research has shown that smaller mechanical test samples produce higher yield and ultimate stress values compared to values measured from standard sample sizes: the “smaller is stronger” effect. Specimens used in accelerated material testing campaigns must reflect bulk material performance to enable engineering scale material property measurement. The objective of this research project was to determine if engineering scale mechanical behavior—the yield stress—could be measured with micro-tensile test samples smaller than traditional standard testing geometries. The relationship between yield stress and sample size was explored with two different nuclear-relevant structural materials: Zircaloy-4 and tungsten. Mechanical testing of both metals demonstrated decreasing yield stress values with increasing sample gauge size across three different sizes. Yield stress values from the largest gauge size, fabricated with a femto-second laser ablation system, approach bulk material yield stress values reported in published literature. Preliminary analysis of the tungsten samples indicates the yield stress value depends on the grain characteristics within the gauge section, in addition to the gauge size. Accompanying modeling efforts, including response surface generation and crystal plasticity approaches, further demonstrated that the size of the sample gauge section alone cannot explain the change in yield stress values.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Environmental Durability Assessment and Development of Subcomponent Test Methods for Advanced Materials in Marine Energy Applications: Preprint

Marine energy developers are beginning to rely more heavily on advanced composite materials for their load bearing structures. Understanding how these materials respond and degrade in harsh marine environments is a complex problem. Much research has been performed at smaller laboratory scales to understand the fundamental physical and chemical degradation mechanisms, but little structural testing research has been performed at larger scales to understand how these physical and chemical interactions scale and how they should be accounted for in the design process. The Advanced Materials project is a multilaboratory research collaboration that has been addressing these issues with the intention of reducing risks and barriers to marine energy developers wanting to adopt advanced composite materials. The National Laboratory of the Rockies has been playing a leading role in developing larger scale testing infrastructure and test methods to validate complex numerical degradation models and standardize testing procedures to do so. This paper provides a review of past structural testing activities and how they have grown into current research activities and will continue to evolve into the future to directly address industry needs. This paper covers key research areas large-scale water absorption and subsequent structural validation testing, qualification of bolted and adhesive connections, and conducting combined submerged fatigue testing at increasing scales. This paper provides insights into the complex requirements for testing infrastructure and test method development to effectively qualify materials and certify marine energy structures for harsh marine environments. This research program paves the way for better guidance and standardization within the industry when adopting advanced composite materials.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Molecules to Masterpieces: Bridging Materials Science and the Arts

Art and materials innovation have always been intertwined, dating back to the earliest human creations. In modern times, however, the increasing specialization of materials science often restricts artists' access to cutting-edge materials. Here, the materials science aspects of an art-science collaboration between artist Kimsooja and the Wiesner Lab at Cornell University, are detailed. The project involves the development of a custom-made iridescent block copolymer coating by means of self-assembly, originally applied to transparent window panels of a façade for the ≈14 m tall art installation: A Needle Woman: Galaxy Is a Memory, Earth is a Souvenir by artist Kimsooja. After several exhibitions in the US and Europe, the installation is now part of the permanent museum collection at Yorkshire Sculpture Park in Wakefield, UK. Full characterization of the solution blade-cast coatings show shear aligned, standing up lamellar morphologies that behave as volume-phase gratings with periodicities between 300 and 400 nm. Coatings are also applied to foldable (origami) paper and converted into iridescent porous ceramic materials. Furthermore, it is hoped this work inspires and informs communities across materials science, the arts, and architecture.

Architectural nanomaterials↗