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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics

Determining a process–structure–property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in the inverse direction are essential. Problems in materials design are often considered in the context of process–property linkage by bypassing the materials structure, or in the context of structure–property linkage as in microstructure-sensitive design problems. However, there is a lack of research effort in studying materials design problems in the context of process–structure linkage, which has a great implication in reverse engineering. In this paper, given a target microstructure, we propose an active learning high-throughput microstructure calibration framework to derive a set of processing parameters, which can produce an optimal microstructure that is statistically equivalent to the target microstructure. The proposed framework is formulated as a noisy multi-objective optimization problem, where each objective function measures a deterministic or statistical difference of the same microstructure descriptor between a candidate microstructure and a target microstructure. Furthermore, to significantly reduce the physical waiting wall-time, we enable the high-throughput feature of the microstructure calibration framework by adopting an asynchronously parallel Bayesian optimization to exploit high-performance computing resources. Case studies in additive manufacturing and grain growth are used to demonstrate the applicability of the proposed framework, where kinetic Monte Carlo (kMC) simulation is used as a forward predictive model, such that for a given target microstructure, the target processing parameters that produced this microstructure are successfully recovered.

36 MATERIALS SCIENCE↗
NASA NTRS1974

Mechanical properties and structural characteristics of Unitemp C-300

A direct comparison of vacuum-melted, argon-atomized, hot-pressed, and extruded Unitemp C-300, an experimental, nickel-base superalloy, is made with as-cast and extruded material, with respect to mechanical properties and structural characteristics, utilizing the optimum thermomechanical processing variables previously established. Comparisons are also made with published mechanical properties of existing high temperature alloys in their temperature range of operation. These studies have established the exceptional versatility of this alloy for both high and low temperature applications and have shown that its strength characteristics significantly exceed those of the best currently available alloys on the market today.

Schwertz, J. H.↗
DOE OSTI2022

Revisiting Multi-Material Composite Structures with Homogenized Composite Properties

Composite structures inherently develop residual stresses during their curing process. Driven predominately by mismatched thermal strains between differing materials or ply orientations, but also affected by curing process phenomena like polymer shrinkage, these residual stresses can lead to failure within composite structures. There are several methods varying in complexity that can be used to model the development of residual stresses, all of which are capable of capturing sufficient detail to understand the residual stress state at the ply level. However, explicitly modeling all plies of a layup in a composite structure can be prohibitively expensive based on the number of plies, structure size, and required element size. The computational cost can be reduced through the homogenization of the composite layup without losing much fidelity of the overall response of the structure. The homogenization process reduces the many plies of a laminate to a single lamina that reduces complexity and increases the mesh size where a single element can span multiple plies. This report focuses on verification and validation efforts for a homogenization process using a suite of finite element simulations rather than an analytic solution derived from classical laminate theory. Initial verification using representative element volumes indicated there was minimal error in the homogenization process; however, this compounded to a small, but acceptable error in strip and split ring experimental composite structures. The error does under predict the residual stress state in the strip and split ring and should be accounted for when simulating composite structures with homogenized properties.

36 MATERIALS SCIENCE↗
NASA NTRS1976

Eigenvalue properties of structural mean-axis systems

The authors review some of the properties of the pseudoinverse and oblique pseudoinverse of a linear transformation T from one finite-dimensional inner-product space into another, and then use these properties and a theorem of Milne (1968), which states that the oblique pseudoinverse can be expressed in terms of a weak generalized inverse and two projection operators, in order to compute a mean-axis influence coefficient matrix for the dynamic analysis of an elastic body. Some eigenvalue invariance properties of the mean-axis structural dynamics equations are demonstrated, and on the simple example of a uniform beam, it is shown that the finite frequencies and mode shapes of the mean axis structural system are identical to the nonzero frequencies and mode shapes of the free structure.

Cavin, R. K., III↗
DOE OSTI2023

Ab initio study of the structure and properties of amorphous silicon hydride from accelerated molecular dynamics simulations

This paper presents a large-scale ab initio simulation study of amorphous silicon hydride (a-Si 1-x H x ) with an emphasis on the structure and properties of the material across a range of hydrogen concentration by combining accelerated molecular dynamics (MD) simulations with first-principles density-functional calculations. The accelerated MD scheme relied on classical metadynamics, which enabled the development of 2500+ high-quality structural models of a-Si 1-x H x , with system sizes ranging from 150 to 6000 atoms and hydrogen concentrations vary from 6 to 20 at. %. The resulting amorphous networks were found to be completely free from any coordination defects and that they all exhibited a pristine band-gap in their electronic spectrum. The microstructural properties of hydrogen distributions were examined with an emphasis on the presence of isolated and clustered environments of hydrogen atoms. The results were compared with experimental data obtained from X-ray diffraction, infrared spectroscopy and nuclear magnetic resonance studies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ab initio study of the structure and properties of amorphous silicon hydride from accelerated molecular dynamics simulations

This paper presents a large-scale ab initio simulation study of amorphous silicon hydride (a-Si 1-x H x ) with an emphasis on the structure and properties of the material across a range of hydrogen concentration by combining accelerated molecular dynamics (MD) simulations with first-principles density-functional calculations. The accelerated MD scheme relied on classical metadynamics, which enabled the development of 2600+ high-quality structural models of a-Si 1-x H x , with system sizes ranging from 150 to 6,000 atoms and hydrogen concentrations vary from 6 to 20 at. %. The resulting amorphous networks were found to be completely free from any coordination defects and that they all exhibited a pristine band-gap in their electronic spectrum. The microstructural properties of hydrogen distributions were examined with great emphasis on the presence of isolated and clustered environments of hydrogen atoms. The results were compared with a suite of experimental data obtained from x-ray diffraction, infrared spectroscopy, spectroscopic ellipsometry and nuclear magnetic resonance studies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Investigation into the crystal structure–dielectric property correlation in barium titanate nanocrystals of different sizes

For high capacitance multilayer ceramic capacitors, high dielectric constant and lead-free ceramic nanoparticles are highly desired. However, as the particle size decreases to a few tens of nanometers, their dielectric constant significantly decreases, and the underlying mechanism has yet to be fully elucidated. Herein, we report a systematic investigation into the crystal structure–dielectric property relationship of combustion-made BaTiO 3 (BTO) nanocrystals. When the nanocrystal size was 100 nm and below, a metastable paraelectric cubic phase was found in the as-received BTO (denoted as arBTO) nanocrystals based on an X-ray diffraction (XRD) study. A stable ferroelectric tetragonal phase was present when the nanocrystal size was above 200 nm. Notably, the cubic arBTO (particle size ≤100 nm) exhibited tetragonal fluctuations as revealed by Raman spectroscopy, whereas the tetragonal arBTO (particle size ≥200 nm) contained ~10% cubic fraction according to the Rietveld fitting of the XRD profiles. Thermal annealing of the multi-grain tetragonal arBTO at 950 °C yielded single crystals of annealed BTO (denoted as anBTO), whose dielectric constants were higher than those of arBTO. However, the single crystalline anBTO prevented the formation of 90° domains; therefore, they exhibited a low dielectric constant of ~300. Although X-ray photoelectron spectroscopy and high-resolution transmission electron microscopy could not identify the exact structural defects, our study revealed that surface and bulk defects formed during synthesis affect the final crystal structures and thus the dielectric properties of BTO nanocrystals with different sizes. Finally, the understanding obtained from this study will help us design high dielectric constant perovskite nanocrystals for next-generation multilayer ceramic capacitor applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Impact of Dihedral Angle in Conjugated Organic Cation on the Structures and Properties of Organic‐Inorganic Lead Iodides

Abstract Conjugated organic cations are intriguing for organic‐inorganic halide perovskites due to their direct participation in the optoelectronic properties of hybrid materials. In conjugated cations, the dihedral angle, or torsion angle, between adjacent aromatic rings is a critical secondary structural element. This angle influences not only the shape of the cations but also the overlap between the π‐orbitals on adjacent rings, thereby affecting their electronic properties. Understanding how variations in the dihedral angle impact the structure and properties of hybrid organic‐inorganic metal halides (HOIMHs) is fundamentally important. In this study, we utilized 2,2′‐dimethyl bipyridinium as the organic cation, reacting it with PbI₂ to form hybrid lead iodides. Remarkably, variations in the dihedral angle between the two pyridinium rings resulted in the formation of two distinct crystal structures with different band gaps. Our findings demonstrate that manipulating the dihedral angle offers a novel approach to controlling the structures and properties of hybrid metal halides with conjugated cations.

Chandra Patra, Bidhan [Department of Chemistry and↗
NASA NTRS1987

Methodology for testing and validating knowledge bases

A test and validation toolset developed for artificial intelligence programs is described. The basic premises of this method are: (1) knowledge bases have a strongly declarative character and represent mostly structural information about different domains, (2) the conditions for integrity, consistency, and correctness can be transformed into structural properties of knowledge bases, and (3) structural information and structural properties can be uniformly represented by graphs and checked by graph algorithms. The interactive test and validation environment have been implemented on a SUN workstation.

Krishnamurthy, C.↗
NASA NTRS1991

Glass formation, properties, and structure of soda-yttria-silicate glasses

The glass formation region of the soda yttria silicate system was determined. The glasses within this region were measured to have a density of 2.4 to 3.1 g/cu cm, a refractive index of 1.50 to 1.60, a coefficient of thermal expansion of 7 x 10(exp -6)/C, softening temperatures between 500 and 780 C, and Vickers hardness values of 3.7 to 5.8 GPa. Aqueous chemical durability measurements were made on select glass compositions while infrared transmission spectra were used to study the glass structure and its effect on glass properties. A compositional region was identified which exhibited high thermal expansion, high softening temperatures, and good chemical durability.

Angel, Paul W.↗
NASA NTRS1992

Glass formation, properties and structure of soda-yttria-silica glasses

The glass formation region of the soda yttria silicate system was determined. The glasses within this region were measured to have a density of 2.4 to 3.1 g/cu cm, a refractive index of 1.50 to 1.60, a coefficient of thermal expansion of 7 x 10(exp -6)/C, softening temperatures between 500 and 780 C, and Vickers hardness values of 3.7 to 5.8 GPa. Aqueous chemical durability measurements were made on select glass compositions while infrared transmission spectra were used to study the glass structure and its effect on glass properties. A compositional region was identified which exhibited high thermal expansion, high softening temperatures, and good chemical durability.

Angel, Paul W.↗
NASA NTRS2010

The Influence of Friction Stir Weld Tool Form and Welding Parameters on Weld Structure and Properties: Nugget Bulge in Self-Reacting Friction Stir Welds

Although friction stir welding (FSW) was patented in 1991, process development has been based upon trial and error and the literature still exhibits little understanding of the mechanisms determining weld structure and properties. New concepts emerging from a better understanding of these mechanisms enhance the ability of FSW engineers to think about the FSW process in new ways, inevitably leading to advances in the technology. A kinematic approach in which the FSW flow process is decomposed into several simple flow components has been found to explain the basic structural features of FSW welds and to relate them to tool geometry and process parameters. Using this modelling approach, this study reports on a correlation between the features of the weld nugget, process parameters, weld tool geometry, and weld strength. This correlation presents a way to select process parameters for a given tool geometry so as to optimize weld strength. It also provides clues that may ultimately explain why the weld strength varies within the sample population.

Schneider, Judy↗
DOE OSTI2020

Redox effects on the structure and properties of Na-Mo-Fe-phosphate glasses

Na-Mo-Fe-phosphate glasses were prepared with reducing and oxidizing raw materials and the effects of the different Fe 2+ /Fe 3+ and Mo 5+ /Mo 6+ ratios on glass structure and properties were determined. Mssbauer spectroscopy confirms significantly greater concentrations of Fe 2+ ions in the reduced glasses and distorted Fe 3+ O 4 sites preferred over the Fe 3+ O 6 sites in the oxidized glasses. Raman spectroscopy reveals the presence of isolated Mo 6+ O 6 sites in the oxidized glasses, and highly distorted Mo 5+ O 5 sites in reduced Mo-rich glasses. The presence of specific Fe- and Mo-polyhedra is correlated with the average phosphate anion length, as characterized by high-pressure liquid chromatography. Generally, for the similar compositions, oxidized glasses have greater molar volumes than the reduced glasses, associated with the formation of isolated Mo 6+ O 6 octahedra and the absence of highly crosslinked Mo 5+ OPO 4 units. Tg increases with increasing Fe 3+ fractions in the Fe-rich glasses, whereas for the Mo-rich glasses, T g increases with greater Mo 5+ fractions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Conspectus The variability of chemical bonding in open-shell transition-metal complexes not only motivates their study as functional materials and catalysts but also challenges conventional computational modeling tools. Here, tailoring ligand chemistry can alter preferred spin or oxidation states as well as electronic structure properties and reactivity, creating vast regions of chemical space to explore when designing new materials atom by atom. Although first-principles density functional theory (DFT) remains the workhorse of computational chemistry in mechanism deduction and property prediction, it is of limited use here. DFT is both far too computationally costly for widespread exploration of transition-metal chemical space and also prone to inaccuracies that limit its predictive performance for localized d electrons in transition-metal complexes. These challenges starkly contrast with the well-trodden regions of small-organic-molecule chemical space, where the analytical forms of molecular mechanics force fields and semiempirical theories have for decades accelerated the discovery of new molecules, accurate DFT functional performance has been demonstrated, and gold-standard methods from correlated wavefunction theory can predict experimental results to chemical accuracy. The combined promise of transition-metal chemical space exploration and lack of established tools has mandated a distinct approach. In this Account, we outline the path we charted in exploration of transition-metal chemical space starting from the first machine learning (ML) models (i.e., artificial neural network and kernel ridge regression) and representations for the prediction of open-shell transition-metal complex properties. The distinct importance of the immediate coordination environment of the metal center as well as the lack of low-level methods to accurately predict structural properties in this coordination environment first motivated and then benefited from these ML models and representations. Once developed, the recipe for prediction of geometric, spin state, and redox potential properties was straightforwardly extended to a diverse range of other properties, including in catalysis, computational “feasibility”, and the gas separation properties of periodic metal–organic frameworks. Interpretation of selected features most important for model prediction revealed new ways to encapsulate design rules and confirmed that models were robustly mapping essential structure–property relationships. Encountering the special challenge of ensuring that good model performance could generalize to new discovery targets motivated investigation of how to best carry out model uncertainty quantification. Distance-based approaches, whether in model latent space or in carefully engineered feature space, provided intuitive measures of the domain of applicability. With all of these pieces together, ML can be harnessed as an engine to tackle the large-scale exploration of transition-metal chemical space needed to satisfy multiple objectives using efficient global optimization methods. In practical terms, bringing these artificial intelligence tools to bear on the problems of transition-metal chemical space exploration has resulted in ML-model assessments of large, multimillion compound spaces in minutes and validated new design leads in weeks instead of decades.

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

Active Causal Machine Learning for Molecular Property Prediction

Predicting properties from molecular structures is paramount to design tasks in medicine, materials science, and environmental management. However, design rules derived from the structure-property relationships using correlative data-driven methods fail to elucidate underlying causal mechanisms controlling chemical phenomena. This preliminary work proposes a workflow to actively learn robust cause-effect relations between structural features and molecular property for a broad chemical space utilizing smaller subsets, entailing partial information.

Fox, Zach↗