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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

An accelerated framework for predicting creep rupture lifetimes in engineering alloys

Confidently predicting high-temperature deformation, including creep and creep rupture, is paramount for the design and commercialization of candidate materials for advanced nuclear energy systems. To accelerate creep quantification, we introduce a framework that enables rapid, cost-effective, and reliable prediction of creep rupture lifetimes, minimizing reliance on time-intensive bulk creep testing. Unlike conventional creep analysis, which requires extensive time and resources, our method leverages a maximum of four short-term bulk creep tests as training data for prediction. This framework combines high-throughput nanoindentation up to 700 °C with these targeted bulk tests to inform our creep rupture model in order to predict rupture lifetimes. The strong agreement between our predictions and conventional experimental data demonstrates the effectiveness of our approach for accelerated creep analysis and lifetime prediction of structural components in high-temperature applications. Our multi-pronged approach motivates further integration of computational tools and advanced instrumentation to establish a universal framework for understanding high-temperature material responses.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A robust alloy design (RAD) strategy for next-generation (IV) nuclear fission reactors

Next-generation nuclear reactors demand structural materials capable of withstanding extreme conditions, including high temperatures, intense neutron flux, and corrosive environments. Multi-Principal Element Alloys (MPEAs) have emerged as promising candidates due to their exceptional radiation tolerance, thermal stability, and compositional flexibility. This study introduces a versatile and customizable Robust Alloy Design (RAD) strategy for systematically designing MPEAs for GEN-IV reactor fuel cladding. The RAD framework integrates nuclear-relevant selection criteria, empirical parameter assessments, and high-throughput CALPHAD simulations to efficiently narrow compositional space and identify stable alloys. A unified RAD score developed for the first time, combines key performance metrics, including fuel-clad chemical interaction (FCCI), neutron absorption cross-section (NAC), valence electron configuration (VEC), and melting point factor (MPF), into a flexible ranking system adaptable to reactor-specific priorities. Among 724 candidates, V555(5Al–5Cr–5Fe–85V) emerged as the top alloy, validated experimentally with a homogeneous single-phase BCC microstructure and superior mechanical properties (nano-indentation: 3.389 ± 0.258 GPa; Vickers hardness: 240 ± 6.7 HV), significantly outperforming Zircaloy-4 and V-4Cr-4Ti. Importantly, the RAD strategy is not limited to nuclear applications; its customizable weighting system enables scalability to other extreme environments. This adaptability positions RAD strategy as a versatile tool for advanced materials design across multiple industries.

Alloy design↗
DOE OSTI2020

Fabrication of flexible perovskite solar cells via rapid thermal annealing

Adaptability, lightweight, and low cost of materials make flexible perovskite solar cells essential for the perovskite technology to breach commercial photovoltaic markets. Roll-to-roll manufacturing has great potential for high-throughput perovskite manufacturing; however, conventional annealing methods are not capable of producing flexible perovskite solar cells at scale. Conventional methods are time consuming and may compromise functionality of flexible substrates under tension. Here, we introduce a bladecoated perovskite thin film on flexible ITO-coated PET annealed via rapid thermal annealing (RTA) that reduces post-deposition processing time from ~150 s to ~14 s. As a result, performance was not sacrificed for scalability as a perovskite solar cell was produced with a power conversion efficiency (PCE) of 14.58%.

14 SOLAR ENERGY↗
DOE OSTI2025

High entropy alloys as catalysts: A focused review

A brief literature review of recent experimental and computational efforts on the use of high entropy alloys (HEAs) as catalysts is presented, while also sharing some perspectives and future insights. To fully broach the vast compositional possibilities of HEA materials, integrating computational modeling with high-throughput experimental synthesis and validation is necessary to accelerate their design and development. Once identified, specific HEAs can be a class of materials for the next generation of catalysts when addressing global challenges related to energy independence, commodity chemical production, environmental remediation, abating emissions, and modernized domestic supply chain resilience.

42 - ENGINEERING↗
DOE OSTI2023

A database of ultrastable MOFs reassembled from stable fragments with machine learning models

High-throughput screening of hypothetical metal-organic framework (MOF) databases can uncover new materials, but their stability in real-world applications is often unknown. We leverage community knowledge and machine learning (ML) models to identify MOFs that are thermally stable and stable upon activation. We separate these MOFs into their building blocks and recombine them to make a new hypothetical MOF database of over 50,000 structures with orders of magnitude more (1) connectivity nets and (2) inorganic building blocks than were present in prior databases. Further, this database shows a 10-fold enrichment of ultrastable MOF structures that are stable upon activation and more than 1 standard deviation more thermally stable than the average experimentally characterized MOF. For nearly 10,000 ultrastable MOFs, we compute elastic moduli to confirm that these materials have good mechanical stability, and we report methane deliverable capacities. We identify privileged metal nodes in ultrastable MOFs that optimize gas storage and mechanical stability simultaneously.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Designing transparent conductors using forbidden optical transitions

Many semiconductors present weak or forbidden transitions at their fundamental band gaps, inducing a widened region of transparency. This occurs in high-performing n-type transparent conductors (TCs) such as Sn-doped In 2 O 3 (ITO); however, thus far, the presence of forbidden transitions has been neglected in the search for new p-type TCs. To address this, we first compute high-throughput absorption spectra across ~18, 000 semiconductors, showing that over half exhibit forbidden or weak optical transitions at their band edges. Next, we demonstrate that compounds with highly localized band-edge states are more likely to present forbidden transitions. Lastly, we search this set for p-type and n-type TCs with forbidden transitions and, by performing defect calculations, propose unexplored TC candidates such as ambipolar BeSiP 2 , p-type wurtzite BAs, and n-type Ba 2 InGaO 5 , among others. In conclusion, we share our dataset and recommend that future screenings for optical properties consider the impact of forbidden transitions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening

Here, we present an updated version of the Computation-Ready, Experimental (CoRE) Metal-Organic Framework (MOF) database, which includes a curated set of computation-ready MOF crystal structures designed for high-throughput computational materials discovery. Data collection and curation procedures were improved from the previous version to enable more frequent updates in the future. Machine-learning-predicted properties, such as stability metrics and heat capacities, are included in the dataset to streamline screening activities. An updated version of MOFid was developed to provide detailed information on metal nodes, organic linkers, and topologies of an MOF structure. DDEC6 partial atomic charges of MOFs were assigned based on a machine-learning model. Gibbs ensemble Monte Carlo simulations were used to classify the hydrophobicity of MOFs. The finalized dataset was subsequently used to perform integrated material-process screening for various carbon-capture conditions using high-fidelity temperature-swing adsorption (TSA) simulations. Our workflow identified multiple MOF candidates that are predicted to outperform CALF-20 for these applications.

CoRE MOF database↗
DOE OSTI2025

Are quantum materials economically and environmentally sustainable?

Quantum materials have revolutionized energy, information, and healthcare technologies, yet their development has largely prioritized performance over economic and environmental impacts—key factors for industrial adoption. Using topological materials as a case study, we present a data-driven framework that evaluates over 16,000 materials based on cost, supply chain resilience, energy demand, toxicity, and environmental footprint. By integrating the recently proposed quantum weight – a metric quantifying quantum behavior – we reveal a striking trend: materials with stronger quantum effects often exhibit higher environmental impact, posing challenges for scalability and industrial adoption. To address this, we identify a small set of materials that achieve a balance between quantum functionality and sustainability. Furthermore, our approach enables high-throughput, AI-driven materials discovery that incorporates economic and environmental influences from the outset, guiding the development of quantum materials for next-generation microelectronics and energy harvesting technologies.

AI↗
DOE OSTI2020

Modeling of nitrogen fixation and polymer production in the heterotrophic diazotroph Azotobacter vinelandii DJ

Nitrogen fixation is an important metabolic process carried out by microorganisms, which converts molecular nitrogen into inorganic nitrogenous compounds such as ammonia (NH 3 ). These nitrogenous compounds are crucial for biogeochemical cycles and for the synthesis of essential biomolecules, i.e. nucleic acids, amino acids and proteins. Azotobacter vinelandii is a bacterial non-photosynthetic model organism to study aerobic nitrogen fixation (diazotrophy) and hydrogen production. Moreover, the diazotroph can produce biopolymers like alginate and polyhydroxybutyrate (PHB) that have important industrial applications. However, many metabolic processes such as partitioning of carbon and nitrogen metabolism in A. vinelandii remain unknown to date. Genome-scale metabolic models (M-models) represent reliable tools to unravel and optimize metabolic functions at genome-scale. M-models are mathematical representations that contain information about genes, reactions, metabolites and their associations. M-models can simulate optimal reaction fluxes under a wide variety of conditions using experimentally determined constraints. Here we report on the development of a M-model of the wild type bacterium A. vinelandii DJ (iDT1278) which consists of 2,003 metabolites, 2,469 reactions, and 1,278 genes. We validated the model using high-throughput phenotypic and physiological data, testing 180 carbon sources and 95 nitrogen sources. iDT1278 was able to achieve an accuracy of 89% and 91% for growth with carbon sources and nitrogen source, respectively. This comprehensive M-model will help to comprehend metabolic processes associated with nitrogen fixation, ammonium assimilation, and production of organic nitrogen in an environmentally important microorganism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Compact ammonia reforming at low temperature using catalytic membrane reactors

Ammonia is a leading carrier for the storage and transport of renewable hydrogen, but its deployment requires scalable technologies for efficient decomposition and purification. In this work, we report on the efficient delivery of high purity hydrogen from ammonia decomposition using a catalytic membrane reactor (CMR). Improvements to the electroless plating process reduced the Pd membrane thickness by >35%, resulting in commensurate increases in hydrogen permeance without sacrificing selectivity. To increase throughput a commercial Ru/Al 2 O 3 catalyst was added to the lumen, and the CMR could process ammonia flowrates 10–50 times higher than an equivalent packed bed reactor while maintaining the same level of conversion. It is shown that the earth-abundant zeolite clinoptilolite could reduce ammonia impurities in the permeated H 2 to the levels required by PEM fuel cells (<25 ppb). Performance increased significantly across a >500-h durability test due to improvements in membrane permeability. Finally, the results show that CMRs are a viable technology for distributed production of hydrogen from ammonia.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The need for ion-exchange membranes with high charge densities

Ion-exchange membranes (IEMs) are essential for controlling ion transport in electrochemical membrane-based technologies for water purification, energy generation, and energy storage. In general, IEMs must exhibit high ionic conductivity and selectivity, both of which are significantly influenced by the charge and water content of the hydrated membrane. In this perspective, we compiled the charge and water contents of approximately 1,000 IEMs reported over the past several decades to assess their range and variability, as well as to reveal gaps and set clear targets for future membrane design. We compared the charge and water content of negatively and positively charged membranes, as well as membranes developed for desalination and energy applications. The majority of IEMs, regardless of structure or intended application, have volumetric charge densities below 3 mol/L[membrane]. This narrow range of charge densities limits the performance of IEMs and contributes to the observed tradeoff between ion throughput and selectivity. It is critical to develop IEMs with higher charge densities to overcome this performance limitation. Further, we highlighted several design strategies that yielded IEMs with fixed charge densities higher than 3 mol/L[membrane]. These strategies provide valuable insights and can guide the design of next-generation IEMs with high charge densities.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Improved manufacturability of vacuum thermoforming molds by segmentation

Thin polymer shell components used in consumer goods, the automotive industry, and marine applications are often produced by thermoforming. Depending on the quantity of parts, molding processes can increase throughput significantly when compared to processes such as milling or additive manufacturing. One disadvantage to mold-based manufacturing methods, however, is that molds must be produced to form the required geometry. Mold production is often complex and requires a large capital investment. For large-scale molding processes, mold production is sometimes complicated by the requirement for large working volume machine tools which are capable of machining the cavities to the appropriate tolerances for the selected molding process. Here, this paper focuses on a segmented design for a large draw ratio (>3:1 molded surface area to sheet surface area) thermoforming mold and a cost-benefit analysis of segmentation versus monolithic design. Discussion of the thermoforming tests are included. The primary goals were to improve manufacturability and reduce the overall cost of the mold.

Manufacturability↗
DOE OSTI2022

Compositionally graded aluminum-silicon alloy fabricated via friction extrusion

Herein, a novel solid-phase gradient alloying technique was developed for a high throughput composition-microstructure-mechanical property assessment. Friction extrusion of a two-piece billet setup facilitated the formation of a seamless compositionally graded aluminum (Al)-silicon (Si) extrudate rod, with Si concentration in Al matrix gradually increasing from ~1 at% at one end to ~7 at% at the other end of the rod. We observed a strong dependence of the Al grain size, morphology, and texture on the Si concentration. The Si particles pinned the recrystallized grain boundaries resulting in refined (< 10 µm) and textured grains in the high Si content region, while the Al grains in the low Si region are equiaxed, randomly textured, and with a larger average size (~50 µm). The hardness of Al-Si gradient extrudate increases by ~50% percent with a 6 at% increase in Si content due to Hall Petch and particle strengthening effects. Our approach demonstrates the use of a solid phase processing technique to successfully produce a defect-free continuously compositionally graded bulk components thus overcoming the limitations of porosity and material heterogeneities commonly observed in other techniques such as by additive laser deposition.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Data mining the missing ordered phases of Li/Na metal oxides

Data-driven discovery of Li-ion and Na-ion battery materials has been pioneered by generic materials data platforms such as the Materials Project. After decades of progress, it is timely to ask whether there remain underexplored compositional spaces. Here, in this work, we present a systematic data-mining effort to uncover missing ordered binary, ternary and quaternary Li/Na-containing metal oxides using high-throughput density functional theory (DFT). Building on 19,120 stable and metastable oxides entries from the Materials Project, we performed 13,245 additional calculations through isovalent substitutions of known ground states, experimentally reported compounds, and specific prototype structures. Our study identifies 36 new ground states within the GGA/GGA + U convex hull and 45 within the r 2 SCAN convex hull. Additionally, we identified 840 metastable compounds from GGA/GGA + U and 979 from r 2 SCAN that are absent in the present Materials Project databases. Moreover, we have tripled the metastable materials in compositional spaces with a molar ratio of cation/anion >1, highlighting the overlooked opportunities in this compositional space.

25 ENERGY STORAGE↗
DOE OSTI2020

Efficient computational search for lanthanide-binding additive dopants for advanced U-Zr based fuels

A rational design strategy combining theory and experiment is highly desirable for the development of U-Zr based fuels that are resistant to lanthanide-induced fuel-cladding chemical interaction (FCCI). Here, we performed rapid computational screening of FCCI-mitigating (via strong lanthanide-binding) elements across the periodic table by utilizing three simple criteria and density functional theory (DFT) calculated total energies of relevant elements and compounds retrieved from high-throughput DFT databases. In addition to successfully identifying previously demonstrated additives, our search leads to the discovery of Bi as a highly effective element among all 68 elements screened, and its effectiveness was experimentally confirmed in this study.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Prediction of ambient pressure superconductivity in cubic ternary hydrides with MH 6 octahedra

Exploring high-temperature superconducting (high-T c ) material at ambient pressure holds immense significance for physics, chemistry, and materials science. In this study, we perform a high-throughput screening of strong electron-phonon interactions in X 2 MH 6 compounds (X = Li, Na, Mg, Al, K, Ca, Ga, Rb, Sr, and In; M are 3d, 4d, and 5d transition metals). These compounds have a cubic structure featuring an MH 6 octahedron motif. Our screening calculations suggest that 26 compounds exhibit dynamic stability and strong electron-phonon coupling. Among them, Mg 2 RhH 6 , Mg 2 IrH 6 , Al 2 MnH 6 , and Li 2 CuH 6 show promising energetic stability and T c of more than 50 K at ambient pressure. This study underscores promising high-T c compounds at ambient pressure with distinctive MH 6 motifs.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Cascading economic losses from port disruptions under capacity constrained multimodal freight networks

This study quantifies how throughput disruptions at major seaports cascade through capacity-constrained multimodal freight networks and interregional production systems. We couple an agent-based model (ABM) multimodal freight simulation that resolves rerouting, terminal queueing, and inventory drawdown under binding modal and facility capacities with a multiregional output loss input-output (MRIIM) model that propagates realized delivery shortfalls across regions and sectors. The framework is demonstrated for the Port of Los Angeles using Freight Analysis Framework flows and Bureau of Economic Analysis input-output accounts and is evaluated over a 52-week horizon under deterministic sector targeted shocks and stochastic disruption realizations with uncertain severity and duration. Results indicate nonlinear amplification: realized national losses concentrate in manufacturing and transportation/warehousing even when exogenous port shocks are dispersed, suggesting that congestion spillback and limited short-run substitution can dominate the initial shock allocation. We further evaluate a tabular reinforcement-learning (Q-learning) intervention layer that selects among a small set of implementable system level levers (truck-to-rail and truck-to-barge shift settings) without overriding shipper routing, finding that such interventions reduce total losses for moderate disruptions but yield diminishing returns once substitute modes approach capacity. By linking operational freight behavior to system wide impacts under uncertainty, the proposed ABM-MRIIM pipeline provides a reusable workflow for port disruption stress testing, identification of structurally critical sectors/corridors, and evaluation of resilience interventions under realistic capacity limits.

42 ENGINEERING↗
DOE OSTI2026

Segmentation method comparison for residual fiber length measurement across tiled microscopy images

Fiber length distribution (FLD), in part, governs mechanical properties in discontinuous fiber composites, yet manual measurement methods limit the high-throughput characterization needed for materials design optimization. This study compares deep learning segmentation approaches for automated FLD measurement in large-field microscopy, evaluating how method choice affects the microstructural descriptors used in structure-property-processing relationships. A critical challenge is that high-resolution microscopy images (10,000×10,000 pixels) must be tiled for deep learning analysis, fragmenting fibers at boundaries. We demonstrate that segmentation method proves crucial for measurement accuracy. For example, instance segmentation with Slicing Aided Hyper Inference (SAHI) preserves individual fiber integrity across tiles while semantic segmentation prioritizes speed. Comparing against manual measurement of extracted carbon fibers, YOLOv11-SAHI matched manual ground truth (238 μm weighted mean) with 40x speedup (4.5 vs 167 minutes per image). U-Net provides rapid quantification although it is at the cost of reduced accuracy due only reliably measuring stand-alone fibers. Our comparative analysis reveals that instance segmentation with SAHI better preserves length measurements while semantic segmentation prioritizes speed, providing empirical guidance for method selection. The characterization provides essential inputs for mechanical property prediction models and inverse design workflows, accelerating composite materials development cycles.

Additive manufacturing↗