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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Next-Generation Materials Design: Quantum Mechanics and Data-Driven Modeling

The future of materials design is rapidly advancing through the combination of quantum mechanics and data-driven modeling. These approaches integrate quantum principles with advanced data analysis, enabling precise insights into material behavior. This talk will highlight recent progress in using these methods for computational design, particularly in high-entropy alloy catalysts, emphasizing the role of hierarchical machine-learning architectures for accurate predictions. Additionally, I will discuss our work on developing machine learning interatomic potentials (MLPs) for single-element metals, metal oxides, and alloys under extreme conditions, focusing on melting behavior and phase properties at high temperatures and pressures. We have also refined our MLP models to capture dynamic surface interactions, such as CO2 and CO adsorption on MgO, using both static and molecular dynamics simulations. These models maintain high accuracy while significantly reducing computational costs compared to first-principles calculations. By enabling efficient and accurate simulations, this work supports broader community adoption, optimizes datasets for materials discovery, and extends the accessible time, size, and environmental conditions beyond the limits of experiments and traditional simulations.

machine learning↗
DOE OSTI2025

Examining Ni Coarsening in Solid Oxide Electrolysis Cells by Characterizing NiH on Ni (111) Using a Combined Theoretical Approach

Ni coarsening in the fuel electrode of solid oxide cells (SOCs) is an important degradation mechanism. In this talk, density-functional theory and kinetic Monte Carlo methods are used to explore the hypothesis that the surface diffusion of NiH on Ni may promote Ni coarsening in the SOC operated in electrolysis cell mode. Using both methods and defining the diffusivity as the product of the surface coverage and single-molecule diffusivity, the diffusivity of NiH on Ni (111) is found to be sufficiently large under a significant overpotential to support the above hypothesis. Also, the time between the formation and dissociation of NiH on Ni (111) is predicted to be short at low coverages of H on Ni (111). Thus, significant progress is made toward developing a model of Ni coarsening considering both molecular and dissociated forms of NiH on Ni (111).

density functional theory (DFT)↗
DOE OSTI2025

Developing an oxidation materials ontology for data-driven materials design

Materials data is complex, and managing and storing materials data for use and reuse is a common challenge. An ontology-based data management framework can address these challenges through encoding data attributes and relationships in a flexible way. This presentation discusses the creation of an ontology for alloy oxidation test data and reviews the logic, structure and interoperability of the ontology.

advanced alloy development↗
DOE OSTI2025

The Effect of Metal Promoters in an Mo-Supported HZSM-5 Catalyst for Microwave-Assisted Methane Dehydroaromatization to Aromatics

Microwave (MW)-assisted methane dehydroaromatization (MDHA) using an Mo-supported HZSM-5 catalyst (Mo/HZ5) can convert methane into value-added aromatic products in modular microwave reactor systems, enabling producers to generate revenue from an otherwise wasted resource. Modifying the local environments of active Mo species with metal promoters potentially regulates the reaction/deactivation pathways and improves the heating properties of the Mo/HZ5 under microwaves. Herein, metal promoters (M), including monovalent K+ and bivalent Co2+ and Ni2+, were incorporated to form M-Mo/HZ5 and their MDHA performance was investigated.

metal promoters↗
DOE OSTI2025

C-C Coupling Mechanism on Cu(100) A Molecular Dynamics Study at 298K

The electrochemical reduction of carbon dioxide (CO2) into valuable fuels such as C1 (syngas, methane) and C2 (ethylene, ethanol) products is a key strategy for achieving a carbon-neutral economy. Computational studies of C-C coupling, a critical step in CO2 reduction, are essential for designing more efficient catalysts. However, simulating these processes under realistic electrochemical conditions, including temperature and solvent effects, is computationally demanding. In this work, we develop a machine learning-based atomistic potential to study CO2 reduction on Cu(100) surfaces, accounting for temperature and explicit water solvent effects. We compute thermodynamic free energies of the possible C-C coupling pathways, CO*+CO*→OCCO*, CO*+CHO*→OCCHO*, CO*+COH*→OCCOH*, CHO*+COH*→OHCCOH*, COH*-COH*→HOCCOH*, and CHO*-CHO*→OHCCHO*. Our results quantify the thermodynamic tendencies of these reactions and reveal that, in addition to the well-established CO* + CO* → OCCO* pathway, CHO* is a critical intermediate in the formation of C2 products on Cu(100). Furthermore, we demonstrate that the machine learning approach offers a cost-efficient framework for studying CO2 reduction on diverse catalysts under realistic electrochemical conditions.

machine learning↗
DOE OSTI2025

Descriptors for Cu facets for CO2 reduction reaction activity

Computation screening is crucial for designing efficient electrochemical catalysts for carbon dioxide (CO2R) reduction to valuable hydrocarbons and oxygenates. Herein, leveraging density functional theory calculations of the CO adsorption energy ΔE_CO on seventeen Cu terminations, we discover a strong linear correlation between ΔE_CO and the recently experimentally measured CO2R electrochemical currents (ACS Catal. 2022, 12, 11, 6578–6588). Examining the ab initio thermodynamics of the early critical intermediates CO*, COH*, and CHO*, we find that CO* → CHO* is the thermodynamically preferred step, and notably shows a volcano trend with the experimental currents where the maximum CO2R current corresponds to the moderate CHO* formation energy. Importantly, we show that increasing the step and kink density of the Cu termination not only enhances CO adsorption strength but also modulates the CO* → CHO* pathway, as respectively exemplified in the (941) and (741) facets. We also explain why (741) is exceptional with high CO2R activity as measured experimentally due to its relatively low activity toward the hydrogen evolution reaction compared with the other Cu surfaces. Beyond the general CO adsorption energy that only shows a linear trend with CO2R activity, we show that the reaction CO* → CHO* free energy is a descriptor that displays a volcano relationship with the overall CO2R activity on Cu facets.

machine learning↗
DOE OSTI2025

Influence of Mo- and Ga-Supported HZSM-5 Co-Catalyst Configuration in Microwave-Assisted Methane and Ethane Dehydroaromatization

Microwave (MW)-assisted dehydroaromatization (DHA) using an Mo-supported HZSM-5 catalyst (Mo/HZSM-5) enhances the value of natural gas resources by converting stranded or underutilized natural gas into value-added chemicals in modular microwave reactor systems. This approach offers the potential to generate economic value. However, natural gas mixtures often contain multiple components, including ethane (C2H6) and propane (C3H8), which complicate the reaction pathways. Ga-supported HZSM-5 (Ga/HZSM-5) catalysts are generally inactive toward CH4 but exhibit higher activity toward C2H6 and C3H8. Therefore, investigating the combination of Mo/HZSM-5 and Ga/HZSM-5 in various catalyst bed configurations is essential. This study explores different co-catalyst bed configurations and CH4/C2H6 feed compositions to determine the most effective way for enhanced natural gas conversion and benzene production.

cocatalyst bed configuration↗
DOE OSTI2025

eXtremeMAT: Uncertainty Quantification of the LApx Model

Presentation on the uncertainty quantification efforts to parameterize and fit the LApx model for ferritic-martensitic steels and austenitic stainless steels. These workflows support ease of adoption of the code to new materials and enable rapid model fitting and understanding of the level of confidence in predictions.

mechanical properties↗
DOE OSTI2025

First-Principles Thermodynamic Assessments of Sr-Containing Secondary Phase Formation in La1-xSrxMnO3±δ Perovskites for Solid Oxide Cell Applications

Sr-secondary phase formation is a potentially significant degradation mode threatening solid-oxide cell (SOC) commercial viability. A first-principles thermodynamic study was performed for rhombohedral perovskite (La1-xSrx) MnO3±δ (LSM) to assess its stability against Sr secondary phase formation in SOC applications. In this work, the Sr secondary phase formation reaction free energies were determined by combining ab initio lattice dynamics calculations for the solid phases and an ab initio thermodynamics approach for the gas phases. Furthermore, this approach goes beyond previous thermodynamic modeling studies by integrating first-principles based point-defect equilibria into the analyses. The modeling results indicate an increased tendency to form SrO oxide from LSM upon decreasing the oxygen partial pressure. Additionally, enhancing factors to form the Sr-related secondary phase from the associated SrO activity in LSM are further quantified by considering the equilibrium of SrO reacting with contaminant gas species as a function of temperature and gas pressure.

Defect and phase stability↗
DOE OSTI2025

Computationally Guided and Experimentally Validated Design of Custom Chelators for Critical Mineral Recovery

Selective, high throughput separation of target critical metals from complex environments such as fly ash leachates and mining process streams presents a significant challenge for economical production. Custom chelators and sorbents are an attractive technology for selective metal extraction, however it can be difficult to predict their performance, and significant experimental efforts are often required to develop chelating technologies. Here, we present a computational strategy focused on modelling chelator-metal binding interactions and benchmark these results versus experimental data. A computational pipeline combining forcefield, semiempirical, and meta-GGA methods with a thermodynamic framework optimized for error cancellation has been developed to predict binding energies of chelator complexes towards critical mineral recovery applications. This approach, originally validated on [2.2.2] cryptates binding mono- and divalent cations, demonstrated robust predictive capabilities with an R2 of 0.850 against experimental aqueous binding energies. The workflow includes metadynamics for exploring high-dimensional potential energy surfaces and a cluster-continuum model for accurate yet computationally efficient solvation modeling. Error cancellation between solvation energies of free and chelator-coordinated ions enables faster convergence, even with finite cluster sizes. Initial studies on the cryptates revealed consistent metal-ligand coordination patterns, with systematic variations influenced by ion size and charge, highlighting key structural features linked to binding selectivity. Further studies of a proprietary chelator have resulted in identification of previously unreported selectivity towards economically significant metals, which in-house experiments have confirmed, demonstrating the feasibility of this approach. By applying this methodology to new chelators targeting critical minerals such as lithium, cobalt, nickel and other strategic metals, we aim to accelerate the discovery of next-generation chelators for efficient recovery, recycling, and separation processes. This computational framework serves as the backbone of a high-throughput design pipeline tailored for sustainable resource utilization and may be applied to a wide range of systems to meet experimental needs.

computational materials↗
DOE OSTI2024

Theoretical Prediction of Thermal Expansion Anisotropy for Y 2 Si 2 O 7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment

Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, the development of new coating systems is hindered by the large design space and difficulty in predicting the properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs. In this work, we use machine learning to train a deep neural network potential (DNP) for Y 2 Si 2 O 7 , which is then applied to calculate the thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict the phonon-based thermodynamic properties of Y 2 Si 2 O 7 with good agreement to DFT and experiments. We also utilize the DNP to calculate the anisotropic, lattice direction-dependent coefficients of thermal expansion (CTEs) for Y 2 Si 2 O 7 . Molecular dynamics trajectories using the DNP correctly demonstrate the accurate prediction of the anisotropy of the CTE in good agreement with the diffraction experiments. In the future, this DNP could be applied to accelerate additional property calculations for Y 2 Si 2 O 7 compared to DFT or experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Data Mining of Polymer Phase Transitions upon Temperature Changes by Small and Wide-Angle X-ray Scattering Combined with Raman Spectroscopy

The complex physical transformations of polymers upon external thermodynamic changes are related to the molecular length of the polymer and its associated multifaceted energetic balance. The understanding of subtle transitions or multistep phase transformation requires real-time phenomenological studies using a multi-technique approach that covers several length-scales and chemical states. A combination of X-ray scattering techniques with Raman spectroscopy and Differential Scanning Calorimetry was conducted to correlate the structural changes from the conformational chain to the polymer crystal and mesoscale organization. Current research applications and the experimental combination of Raman spectroscopy with simultaneous SAXS/WAXS measurements coupled to a DSC is discussed. In particular, we show that in order to obtain the maximum benefit from simultaneously obtained high-quality data sets from different techniques, one should look beyond traditional analysis techniques and instead apply multivariate analysis. Data mining strategies can be applied to develop methods to control polymer processing in an industrial context. Crystallization studies of a PVDF blend with a fluoroelastomer, known to feature complex phase transitions, were used to validate the combined approach and further analyzed by MVA.

36 MATERIALS SCIENCE↗
NASA NTRS2018

Developing a Remotely Sensed Drought Monitoring Indicator for Morocco

Drought is one of the most serious climatic and natural disasters inflicting serious impacts on the socio-economy of Morocco, which is characterized both by low-average annual rainfall and high irregularity in the spatial distribution and timing of precipitation across the country. This work aims to develop a comprehensive and integrated method for drought monitoring based on remote sensing techniques. The main input parameters are derived monthly from satellite data at the national scale and are then combined to generate a composite drought index presenting different severity classes of drought. The input parameters are: Standardized Precipitation Index calculated from satellite-based precipitation data since 1981 (CHIRPS), anomalies in the day-night difference of Land Surface Temperature as a proxy for soil moisture, Normalized Difference Vegetation Index anomalies from Moderate Resolution Imaging Spectroradiometer (MODIS) data and Evapotranspiration anomalies from surface energy balance modeling. All of these satellite-based indices are being used to monitor vegetation condition, rainfall and land surface temperature. The weighted combination of these input parameters into one composite indicator takes into account the importance of the rainfall-based parameter (SPI). The composite drought index maps were generated during the growing seasons going back to 2003. These maps have been compared to both the historical, in situ precipitation data across Morocco and with the historical yield data across different provinces with information being available since 2000. The maps are disseminated monthly to several main stakeholders' groups including the Ministry of Agriculture and Department of Water in Morocco.

Drought monitoring↗
NASA NTRS2018

Automated Fiber Placement Defect Identity Cards: Cause, Anticipation, Existence, Significance, and Progression

Automated Fiber Placement (AFP), a major composite manufacturing process, can result in many defects during the layup process that often require manual corrective action to produce a part with acceptable quality. These defects are the main limitation of the technology and can be hard to categorize or define in many situations. This paper provides a thorough definition and classification of all AFP defects. This effort constitutes a comprehensive and extensive library relevant to AFP defects. The defects selected and defined in this work are based on understanding and experience from the manufacture and research of advanced composite structure. Proper classification of these defects required an in-depth literature review and consideration of various viewpoints ranging from designers, manufacturers, analysts, and inspection professionals. Collectively, these sources were utilized to develop the most accurate view of each of the individual defect types. The results are presented as identity cards for each defect type, intended to provide researchers and the manufacturing industry a clear understanding of the (1) cause, (2) anticipation, (3) existence, (4) significance, and (5) progression of the defined AFP defects. The link between AFP defects and process planning, layup strategies, and machining was also investigated. Categorization of all important automated fiber placement defects is presented.

Harik, Ramy↗
NASA NTRSDate not supplied

A Machine Learning-Derived Atomistic Potential for Y2Si2O7

Incorporation of SiC/SiC ceramic matrix composite (CMC) hot section components into aircraft engines promises to increase efficiency and safety. However, SiC/SiC CMCs are subject to water vapor-induced oxidation and recession at the high temperatures of engine operation, and thus environmental barrier coatings (EBCs) are required to reduce this degradation and enable their widespread adoption. An understanding of EBCs failure mechanisms, including thermochemical and thermomechanical mechanisms, is essential as coating degradation leads to reduced CMC component service life. Computational modeling approaches can provide insight into EBC material properties important for coating design. However, density functional theory (DFT) is computationally expensive and atomistic potentials are lacking for materials of interest. In this work, we utilize a machine learning approach and DFT training data to parameterize atomistic potentials for two candidate EBC materials, Y2Si2O7 and Yb2Si2O7. These potentials enable near DFT-accurate calculations of thermodynamic and thermomechanical properties essential to EBC design.

Cameron J Bodenschatz↗