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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Curiosity driven exploration to optimize structure–property learning in microscopy

Rapidly determining structure–property correlations in materials is an important challenge in better understanding fundamental mechanisms and greatly assists in materials design. In microscopy, imaging data provides a direct measurement of the local structure, while spectroscopic measurements provide relevant functional property information. Deep kernel active learning approaches have been utilized to rapidly map local structure to functional properties in microscopy experiments, but are computationally expensive for multi-dimensional and correlated output spaces. Here, we present an alternative lightweight curiosity algorithm which actively samples regions with unexplored structure–property relations, utilizing a deep-learning based surrogate model for error prediction. We show that the algorithm outperforms random sampling for predicting properties from structures, and provides a convenient tool for efficient mapping of structure–property relationships in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Examination of gamma-irradiated calcium silicate hydrates. Part I: Chemical-structural properties

The chemical-structural properties of synthetic calcium silicate hydrate (C-S-H) with C/S (Ca/Si) ratios of 0.75, 1 and 1.33 were studied after gamma irradiation-absorbed doses ranging from 0.145 to 0.784 MGy and compared with those of control specimens. The results showed that the total water content given by thermogravimetric analysis and the dimension of the basal spacing given by x-ray diffraction were comparable for control and irradiated samples, which indicate that the interlayer water content did not change. The morphology, C/S ratios, and silicate mean chain length were also found to be unaltered by the absorbed gamma doses.

36 MATERIALS SCIENCE↗
DOE OSTI2026

First-principles investigation of structure-property relationships in stable and metastable MXenes

Understanding the structure–property relationships in layered transition-metal carbides or nitrides, known as MXenes, is of critical importance for their rational design, synthesis, and application. However, the vast chemical and structural diversity of MXenes, stemming from their wide range of M and X elements, surface terminations, and different atomic coordination environments, makes it challenging to clearly understand these structure–property relationships. Here, in this work, we perform first-principles density functional theory (DFT) calculations and molecular dynamics (MD) simulations to comprehensively investigate the stability and a variety of physical properties of MXenes with different coordination environments. Using Ti- and Mo-based carbide MXenes as model systems, energetic calculations reveal that Ti-based MXenes are most stable in octahedral coordination, whereas Mo-based MXenes preferentially adopt prismatic coordination. This fundamental difference in preferred atomic coordination gives rise to markedly distinct properties between these two systems as a function of the fraction of octahedral and prismatic sites. For instance, the in-plane stiffness of Ti-based MXenes increases as octahedral coordination becomes dominant, but it decreases in the Mo-based MXenes under the same conditions. Additional stability analyses based on mechanical, lattice-dynamical, and temperature-dependent thermodynamic properties demonstrate that many metastable MXenes not only satisfy the strict stability criteria but can also undergo phase transitions among different structures and even become stabilized at elevated temperatures. Although surface terminations, such as F and O atoms, do not alter the energetic ordering or the overall stiffness trends among stable and metastable MXenes, they influence other material properties. For instance, O termination can induce semiconducting behavior in both stable and metastable Ti 2 ⁢CO 2 MXenes. This study significantly advances the fundamental understanding of structure–property relationships in MXenes and provides valuable guidance for developing coordination-based design principles to precisely engineer MXenes with improved properties.

Oyeniran, Noah [University of Alabama, Tuscaloosa,↗
DOE OSTI2020

Active Learning-driven Quantitative Synthesis-Structure-Property Relations for Improving Performance and Revealing Active Sites of Nitrogen-Doped Carbon for the Hydrogen Evolution Reaction

While quantitative structure-properties relations (QSPRs) have been developed successfully in multiple fields, catalyst synthesis affects structure and in turn performance, making simple QSPRs inadequate. Furthermore, catalysts often have multiple active sites preventing one from obtaining insights into structure-property relations. Here, we develop a data-driven quantitative synthesis-structure-property relation (QS2PRs) methodology to elucidate correlations between catalyst synthesis conditions, structural properties as well as observed performance and to provide fundamental insights into active sites and a systematic way to optimize practical catalysts. Here, we demonstrate the approach to the synthesis of nitrogen-doped catalysts (NDC) made via pyrolysis for the performance of the electrochemical hydrogen evolution reaction (HER), quantified by the onset potential and the current density. We determine crystallinity, nitrogen species type and fraction, surface area, and pore structure of the NDC’s using XRD, XPS, and BET characterization. We demonstrated that an active learning-based optimization combined with various elementary machine learning tools (regression, principal component analysis, partial least squares) can efficiently identify optimum pyrolysis conditions to tune structural characteristics and performance with concomitant savings in materials and experimental time. Unlike previous reports on the importance of pyridinic or graphitic nitrogen, we discover that the electrochemical performance is not driven by a single catalyst property; rather, it arises from a multivariate influence of nitrogen dopants, pore structure and disorder in the NDC materials. Identification of active sites can help mechanistic understanding and further catalyst improvement.

42 ENGINEERING↗
DOE OSTI2024

Navigating high-dimensional process-structure–property relations in nanocrystalline Pt-Au alloys with machine learning

For decades, materials scientists have relied on the process-structure–property paradigm to guide investigations into material behaviors. Traditional studies often examine a limited number of process-structure–property variables, striving to elucidate mechanisms governing material response. However, this approach is time consuming and can limit exploration, as well as the discovery of process-structure–property relations in novel materials. In this paper, we combined combinatorial sputter deposition and multi-modal high-throughput materials characterization with feedforward neural networks to establish high-dimensional process-structure–property relations in Pt-Au alloys, yielding nanocrystalline alloys with high hardness and low resistivity relevant to electrical contact switch applications. We mapped three indicators of process conditions (composition and two atomic deposition characteristics) onto four indicators of material structure (X-ray diffraction, film thickness, density, and surface roughness) and two indicators of material properties (hardness and resistivity), resulting in 784 unique combinations evaluated over a 13-dimensional space. The neural networks predicted Pt-Au alloys with 18–24 at.% Au, when deposited at specific conditions, to have a nanoindentation hardness up to 7.2 GPa. This high hardness value, comparable to some steels, represents a 3-fold improvement in hardness over “hard gold”, a commonly used electrical contact alloy, while maintaining requisite electrical conductivity. The neural network models provide an avenue to identify expected process windows capable of maximizing material performance.

Electrical contact materials↗
DOE OSTI2024

Orientation-dependent structural properties during growth and growth mechanism of CoO films

The orientation-dependent local structural properties of CoO films on sapphire substrates during growth were investigated through linearly-polarized extended X-ray absorption fine structure (EXAFS) measurements. Specifically, CoO(1 1 1) and (100) crystals with a rock-salt structure (Fm3m) were epitaxially grown on alpha-Al2O3(0001) and (1012) substrates, respectively, at 700 degrees C using a radio-frequency sputtering system. The local structural properties of CoO films in the in-plane and out-of-plane orientations were quantitatively determined using linearly-polarized EXAFS at the Co K-edge during growth. The EXAFS analysis revealed that during the initial stages of growth, the local structural properties exhibit significant differences compared to thick films, with short atomic distances and large (small) Debye-Waller factors observed in the out-of-plane (in-plane) orientations. The local structural strain mostly diminished when approximately 20 CoO layers accumulated on the substrate. Density functional theory (DFT) calculations further supported these findings by confirming that cobalt atoms initially form stable bonds with the sapphire surface, leading to the simultaneous growth of oxygen and cobalt layers in a coordinated manner through layer-by-layer growth.

36 MATERIALS SCIENCE↗
NASA NTRS2012

Determination of HART I Blade Structural Properties by Laboratory Testing

The structural properties of higher harmonic Aeroacoustic Rotor Test (HART I) blades were measured using the original set of blades tested in the German-dutch wind tunnel (DNW) in 1994. the measurements include bending and torsion stiffness, geometric offsets, and mass and inertia properties of the blade. the measured properties were compared to the estimated values obtained initially from the blade manufacturer. The previously estimated blade properties showed consistently higher stiffness, up to 30 percent for the flap bending in the blade inboard root section.

Jung, Sung N.↗
DOE OSTI2020

Physically Based Modeling of PEMFC Cathode Catalyst Layers: Effective Microstructure and Ionomer Structure–Property Relationship Impacts

Abstract This work presents a pseudo-two-dimensional proton exchange membrane fuel cell (PEMFC) model incorporating Nafion ionomer structure–property relationships in the cathode catalyst layer (CL) to capture and explain losses at low Pt loading. Structural data from neutron reflectometry and thin film Nafion conductivity measurements predict variations in the oxygen diffusion coefficient and ionic conductivity with changing CL ionomer thickness and Pt loading. By including these structure–property relationships, predicted polarization curves agree closely with previously published experimental data from cells with Pt loadings between 0.025 and 0.2 mg/cm2. Results demonstrate that structure–property relationships based on physically measurable ionomer and CL properties provide a feasible interpretation of PEMFC CL phenomena for a range of Pt loadings and help explain previously unaccounted-for losses at low Pt. Results also show that simulations must account for surface species coverage variations in order to properly capture the kinetic losses. Finally, results suggest that an increase in ionomer thickness surrounding the C/Pt surfaces may lead to improved cell performance due to improved ionic conductivity.

Electrochemistry↗
DOE OSTI2019

Process–structure–property relationships in bimodal machined microstructures using robust structure descriptors

Machining is a severe plastic deformation process that subjects materials to high rates of deformation and elevated temperatures. Dynamic recrystallization during severe plastic deformation drives grain refinement into the sub-micron range but the ductility and thermal stability of these structures is poor. In contrast, bimodal microstructures consisting of both fine and coarse grains have been shown to exhibit attractive strength and ductility properties at these temperatures. In this paper, we study the process–structure–property relationships for pure copper subject to a machining process where the cutting speeds are varied. Further, we investigate the role of thermal effects by varying the post-deformation cooling rates. Microstructures generated under these deformation conditions are quantified using angularly resolved chord length statistics. The derived metrics are found to be robust descriptors of the studied microstructures as they automatically capture complex features such as unimodal and bimodal distributions of grain structure, grain constituent length scales, and morphological anisotropy induced by shear deformation. The uniaxial equivalent yield strength of the generated structures are estimated using spherical nanoindentation and inverse modeling techniques. Lastly, we present a methodology for identifying the constrained property-process inverse mapping for machining using a Bayesian framework and the established forward process–structure–property mapping.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Integrated Process-Structure-Property Simulations for Additive Manufacturing Using the Open-Source Materialite Package

The microstructure and properties of additively manufactured (AM) metals are strongly dependent on process conditions. Therefore, process-structure-property (PSP) simulations are a useful tool for exploring process parameter space, studying process variations, and quantifying uncertainty in material properties. However, integrating process-structure and structure-property simulations often involves connecting multiple software packages. Each package may use unique data structures and require substantial domain knowledge. This presentation demonstrates PSP simulation capabilities of Materialite, an open-source package developed at NASA Langley Research Center. Materialite simplifies model linkages by using a common data structure and model interface, enabling straightforward simulation across a PSP model chain. Physics-based models, including kinetic Monte Carlo and crystal plasticity, are implemented within the package. The model interface is also intended to simplify implementation of new models and enable integration with external simulation tools. Example use cases include uncertainty quantification with PSP models and GPU-accelerated powder bed fusion AM process models.

additive manufacturing↗
DOE OSTI2026

Attention-based explainability for structure–property relationships

Machine learning methods are emerging as a universal paradigm for constructing correlative structure–property relationships in materials science based on multimodal characterization. However, this necessitates the development of methods for the physical interpretability of the resulting correlative models. Here, we demonstrate the potential of attention-based neural networks for revealing structure–property relationships and the underlying physical mechanisms, using the ferroelectric properties of PbTiO3 thin films as a case study. Through the analysis of attention scores, we disentangle the influence of distinct domain patterns on the polarization switching process. The attention-based Transformer model is explored both as a direct interpretability tool and as a surrogate for explaining representations learned via unsupervised machine learning, enabling the identification of physically grounded correlations. We compare attention-derived interpretability scores with classical SHapley Additive exPlanations analysis and show that, in contrast to applications in natural language processing, attention mechanisms in materials science exhibit high efficiency in highlighting meaningful structural features.

Slautin, Boris [Independent Researcher]↗
DOE OSTI2023

Multi-fidelity Uncertainty Quantification for Homogenization Problems in Structure-Property Relationships from Crystal Plasticity Finite Elements

Crystal plasticity finite element method (CPFEM) has been an integrated computational materials engineering (ICME) workhorse to study materials behaviors and structure-property relationships for the last few decades. These relations are mappings from the microstructure space to the materials properties space. Due to the stochastic and random nature of microstructures, there is always some uncertainty associated with materials properties, for example, in homogenized stress-strain curves. For critical applications with strong reliability needs, it is often desirable to quantify the microstructure-induced uncertainty in the context of structure-property relationships. However, this uncertainty quantification (UQ) problem often incurs a large computational cost because many statistically equivalent representative volume elements (SERVEs) are needed. In this article, we apply a multi-level Monte Carlo (MLMC) method to CPFEM to study the uncertainty in stress-strain curves, given an ensemble of SERVEs at multiple mesh resolutions. By using the information at coarse meshes, we show that it is possible to approximate the response at fine meshes with a much reduced computational cost. We focus on problems where the model output is multi-dimensional, which requires us to track multiple quantities of interest (QoIs) at the same time. In conclusion, our numerical results show that MLMC can accelerate UQ tasks around 2.23x, compared to the classical Monte Carlo (MC) method, which is widely known as ensemble average in the CPFEM literature.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Energy–structure–property relationships in uranium metal–organic frameworks

Energy–structure–property relationships in uranium metal–organic frameworks offer a deeper understanding of their behaviors, explaining why structure produces function. This understanding becomes crucial for the rational design of such materials with enhanced properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2015

Finite Element Estimation of Meteorite Structural Properties

The goal of the project titled Asteroid Threat Assessment at NASA Ames Research Center is to develop risk assessment tools. The expertise in atmospheric entry in the Entry Systems and Technology Division is being used to describe the complex physics of meteor breakup in the atmosphere. The breakup of a meteor is dependent on its structural properties, including homogeneity of the material. The present work describes an 11-week effort in which a literature survey was carried for structural properties of meteoritic material. In addition, the effect of scale on homogeneity isotropy was studied using a Monte Carlo approach in Nastran. The properties were then in a static structural response simulation of an irregularly-shape meteor (138-scale version of Asteroid Itokawa). Finally, an early plan was developed for doctoral research work at Georgia Tech. in the structural failure fragmentation of meteors.

Structural Properties↗
DOE OSTI2022

Building Structure-Property Relationships of Cycloalkanes in Support of Their Use in Sustainable Aviation Fuels

In 2018 13.7 EJ of fuel were consumed by the global commercial aviation industry. Worldwide, demand will increase into the foreseeable future. Developing Sustainable Aviation Fuels (SAFs), with decreased CO 2 and soot emissions, will be pivotal to the on-going mitigation efforts against global warming. Minimizing aromatics in aviation fuel is desirable because of the high propensity of aromatics to produce soot during combustion. Because aromatics cause o-rings to swell, they are important for maintaining engine seals, and must be present in at least 8 vol% under ASTM-D7566. Recently, cycloalkanes have been shown to exhibit some o-ring swelling behavior, possibly making them an attractive substitute to decrease the aromatic content of aviation fuel. Cycloalkanes must meet specifications for a number of other physical properties to be compatible with jet fuel, and these properties can vary greatly with the cycloalkane chemical structure, making their selection difficult. Building a database of structure-property relationships (SPR) for cycloalkanes greatly facilitates their furthered inclusion into aviation fuels. The work presented in this paper develops SPRs by building a data set that includes physical properties important to the aviation industry. The physical properties considered are energy density, specific energy, melting point, density, flashpoint, the Hansen solubility parameter, and the yield sooting index (YSI). Further, our data set includes cycloalkanes drawn from the following structural groups: fused cycloalkanes, n-alkylcycloalkanes, branched cycloalkanes, multiple substituted cycloalkanes, and cycloalkanes with different ring sizes. In addition, a select number of cycloalkanes are blended into Jet-A fuel (POSF-10325) at 10 and 30 wt%. Comparison of neat and blended physical properties are presented. One major finding is that ring expanded systems, those with more than six carbons, have excellent potential for inclusion in SAFs. Our data also indicate that polysubstituted cycloalkanes have higher YSI values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1986

Structural properties of bismuth-bearing semiconductor alloys

The structural properties of bismuth-bearing III-V semiconductor alloys are addressed. Because the Bi compounds are not known to form zincblende structures, only the anion-substituted alloys InPBi, InAsBi, and InSbBi are considered candidates as narrow-gap semiconductors. Miscibility calculations indicate that InSbBi will be the most miscible, and InPBi, with the large lattice mismatch of the constituents, will be the most difficult to mix. Calculations of the hardness of the Bi compounds indicate that, once formed, the InPBi alloy will be harder than the other Bi alloys, and substantially harder than the currently favored narrow-gap semiconductor HgCdTe. Thus, although InSbBi may be an easier material to prepare, InPBi promises to be a harder material. Growth of the Bi compounds will require high effective growth temperatures, probably attainable only through the use of nonequilibrium energy-assisted epitaxial growth techniques.

Berding, M. A.↗
NASA NTRS1988

Structural properties of bismuth-bearing semiconductor alloys

The structural properties of bismuth-bearing III-V semiconductor alloys InPBi, InAsBi, and InSbBi were studied theoretically. Bond energies, bond lengths, and strain coefficients were calculated for pure AlBi, GaBi, and InBi compounds and their alloys, and predictions were made for the mixing enthalpies, miscibility gaps, and critical metastable-to-stable material transition temperatures. Miscibility calculations indicate that InSbBi will be the most miscible, and the InPBi will be the the most difficult to mix. However, calculations of the hardness of the Bi compounds indicate that, once formed, the InPBi alloy will be harder than the other Bi alloys and substantially harder than the currently favored narrow-gap semiconductor HgCdTe.

Berding, M. A.↗