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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Interaction energy and isosteric heat of adsorption between hydrogen and magnesium diboride

Hydrogen storage materials form a crucial research topic for future energy utilization employing hydrogen and among those of interest magnesium diboride (MgB 2 ) has shown its prevalence. In this study, a first-principles analytical adsorption model of one hydrogen molecule in the vicinity of various magnesium diboride crystal surfaces was developed in order to obtain surface thermodynamic properties as a function of molecular and lattice properties. Henry's law constant (K H ) and isosteric heat of adsorption (ΔH ads ) indicators of the affinity between a gaseous molecule and a solid surface are thus calculated. The results in this paper not only address questions pertaining to the first stage of hydrogen storage processes but also advance the understanding of physisorption thermodynamics of a neutral molecule (H 2 ) coming in contact with a layered metallic-like surface (MgB 2 ). Although the model is built from a framework of classical calculations, quantum effects are incorporated as the fractional charge of the ions on the free surfaces, which is essential for the calculation of analytic thermodynamic values that approximate calculations from other methods. To benchmark our theoretical models, periodic density functional calculations were performed to determine the interactions between H 2 and different MgB 2 surfaces from first-principles. By considering both the top and sublayers of MgB 2 in calculating interaction energy, we have analytically and computationally calculated the interaction energies of H 2 molecules and MgB 2 's terminated planes, and witnessed the strong dependence of interaction energies on surface charges. We have also observed a dipole flipping phenomenon which explains the discontinuity seen in the interaction energy graph of Mg(0001). Both analytical and computational results showed heat of adsorption at zero coverage varying at a very low range (<7 kJ mol -1 ).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Identifying the Unique Properties of α-Bi 2 Mo 3 O 12 for the Activation of Propene

In order to understand the remarkable activity of α-Bi 2 Mo 3 O 12 for selective oxidation and ammoxidation of propene, the propene activation ability of four molybdenum-based mixed metal oxides - Bi 2 Mo 3 O 12 , PbMoO 4 , Bi 2 Pb 5 Mo 8 O 32 , and MoO 3 - was investigated using density functional theory. Propene activation is considered to occur via abstraction of a hydrogen atom from the methyl group of physisorbed propene by lattice oxygen. For each material, the apparent activation energy was estimated by summing the heat of adsorption of propene, the C-H bond dissociation energy, and the hydrogen attachment energy (HAE) for hydrogen addition to lattice oxygen; this sum provides a lower bound for the apparent activation energy. It was found that two structural features of oxide surfaces are essential to achieve low activation barriers: under-coordinated surface cation sites enable strong propene adsorption, and suitable 5- or 6-coordinate geometries at molybdenum result in favorable HAEs. The impact of molybdenum coordination on HAE was elucidated by carrying out a molecular orbital analysis using a cluster model of the molybdate unit. This effort revealed that, in 5- and 6-coordinate molybdates, oxygen donor atoms trans to molybdenyl oxo atoms destabilize the molybdate prior to H addition but stabilize the molybdate after H addition, thereby providing an HAE ~15 kcal/mol more favorable than that on 4-coordinate molybdate oxo atoms. Bi 3+ cations in Bi 2 Mo 3 O 12 thus promote catalytic activity by providing both strong adsorption sites for propene and forcing molybdate into 5-coordinate geometries that lead to particularly favorable values of the HAE. (Graph Presented).

36 MATERIALS SCIENCE↗
NASA NTRS1999

Modeling of THz Lasers Based on Intersubband Transitions in Semiconductor Quantum Wells

In semiconductor quantum well structures, the intersubband energy separation can be adjusted to the terahertz (THz) frequency range by changing the well width and material combinations. The electronic and optical properties of these nanostructures can also be controlled by an applied dc electric field. These unique features lead to a large frequency tunability of the quantum well devices. In the on-going project of modeling of the THz lasers, we investigate the possibility of using optical pumping to generate THz radiation based on intersubband transitions in semiconductor quantum wells. We choose the optical pumping because in the electric current injection it is difficult to realize population inversion in the THz frequency range due to the small intersubband separation (4-40 meV). We considered both small conduction band offset (GaAs/AlGaAs) and large band offset (InGaAs/AlAsSb) quantum well structures. For GaAs/AlGaAs quantum wells, mid-infrared C02 lasers are used as pumping sources. For InGaAs/AlAsSb quantum wells, the resonant intersubband transitions can be excited by the near-infrared diode lasers. For three- and four-subband quantum wells, we solve the pumpfield-induced nonequilibrium distribution function for each subband of the quantum well system from a set of rate equations that include both intrasubband and intersubband relaxation processes. Taking into account the coherent interactions between pump and THz (signal) waves, we calculate the optical gain for the THz field. The gain arising from population inversion and stimulated Raman processes is calculated in a unified manner. A graph shows the calculated THz gain spectra for three-subband GaAs/AlGaAs quantum wells. We see that the coherent pump and signal wave interactions contribute significantly to the gain. The pump intensity dependence of the THz gain is also studied. The calculated results are shown. Because of the optical Stark effect and pump-induced population redistribution, the maximum THz gain saturates at larger pump intensities.

Liu, Ansheng↗
NASA NTRS2004

Particulate Titanium Matrix Composites Tested--Show Promise for Space Propulsion Applications

Uniformly distributed particle-strengthened titanium matrix composites (TMCs) can be manufactured at lower cost than many types of continuous-fiber composites. The innovative manufacturing technology combines cold and hot isostatic pressing procedures to produce near-final-shape components. Material stiffness is increased up to 26-percent greater than that of components made with conventional titanium materials at no significant increase in the weight. The improved mechanical performance and low-cost manufacturing capability motivated an independent review to assess the improved properties of ceramic titanium carbide (TiC) particulate-reinforced titanium at elevated temperature. Researchers at the NASA Glenn Research Center creatively designed and executed deformation and durability tests to reveal operating regimes where these materials could lower the cost and weight of space propulsion systems. The program compares the elevated-temperature performance of titanium alloy Ti-6Al-4V matrix material to an alloy containing 10 wt% of TiC particles. Initial experiments showed that at these relatively low particle concentrations the material stiffness of the TMC was improved 20 percent over that of the plain Ti-6Al-4V alloy when tested at 427 C. The proportional limit and ultimate strength of the composite in tension are 21- and 14-percent greater than those of the plain alloy. Compression tests showed that the proportional limit is about 30 percent greater for TMC than for the plain alloy. The enhanced deformation resistance of the TMC was also evident in a series of tensile and compressive stress relaxation tests that were made. Specimens were subjected to tensile or compressive strain amplitudes of 0.75 percent for 24 hr followed by a return to zero strain imposed for 24 hr. The stress relaxation data were normalized with respect to the maximum stress for each case and plotted as a function of time in the following graph. Tensile stresses relaxed 19 percent for the TMC and 25 percent for the plain Ti-6Al-4V alloy. Compressive stresses relaxed 25 percent for the TMC and 39 percent for the plain Ti-6Al-4V alloy. The superior deformation resistance of the TMC extends to a creep rate that is 28-percent slower for the TMC when it is loaded to stress levels that are 26-percent higher than for the plain Ti-6Al-4V alloy.

Lerch, Bradley A.↗
DOE OSTI2019

Materials design of perovskite solid solutions for thermochemical applications

Perovskites are excellent candidate materials as oxygen carriers in thermochemical processes. Due to their versatile composition, it is possible to fine-tune the perovskite's properties. Here, we present a method for the rational design of AMO 3– δ perovskite solid solutions with two different species on the Msite in order to tune their redox behavior. To account for the different ionic radii of different transition metal species M, two distinct ions are used in a solid solution on the A site, allowing tolerance-factor adjusted materials design. Using this methodology, we can create stable perovskites over a large range of different compositions. Leveraging the infrastructure of Materials Project, we calculate redox enthalpies for the reduction of over 240 of these $(\text {A}^{\prime}_x\text {A}^{\prime\prime}_{1-x})(\text {M}^{\prime}_y\text {M}^{\prime\prime}_{1-y})$O 3 perovskites to their brownmillerite phases based on density functional theory (DFT). We compare this data to experimentally measured data on thermodynamics of 24 of these materials to verify our theoretical framework. An empirical model is formulated for predicting the enthalpy and entropy changes as a function of the perovskites non-stoichiometry δ , which can be used to simulate the equilibrium composition as a function of temperature and oxygen partial pressure and to create a perovskite search engine based on an energetic analysis of the redox cycles. The data has been added as a contribution to MPContribs, which now includes publicly available user-controlled interactive graphs based on our theoretical and experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Can a deep-learning model make fast predictions of vacancy formation in diverse materials?

The presence of point defects, such as vacancies, plays an important role in materials design. Here, we explore the extrapolative power of a graph neural network (GNN) to predict vacancy formation energies. We show that a model trained only on perfect materials can also be used to predict vacancy formation energies (E vac ) of defect structures without the need for additional training data. Such GNN-based predictions are considerably faster than density functional theory (DFT) calculations and show potential as a quick pre-screening tool for defect systems. To test this strategy, we developed a DFT dataset of 530 E vac consisting of 3D elemental solids, alloys, oxides, semiconductors, and 2D monolayer materials. We analyzed and discussed the applicability of such direct and fast predictions. We applied the model to predict 192 494 E vac for 55 723 materials in the JARVIS-DFT database. Our work demonstrates how a GNN-model performs on unseen data.

2D materials↗
DOE OSTI2018

Representing local atomic environment using descriptors based on local correlations

Statistical learning of material properties is an emerging topic of research and has been tremendously successful in areas such as representing complex energy landscapes as well as in technologically relevant areas, like identification of better catalysts and electronic materials. However, analysis of large data sets to efficiently learn characteristic features of a complex energy landscape, for example, depends on the ability of descriptors to effectively screen different local atomic environments. Thus, discovering appropriate descriptors of bulk or defect properties and the functional dependence of such properties on these descriptors remains a difficult and tedious process. To this end, we develop here a framework to generate descriptors based on many-body correlations that can effectively capture intrinsic geometric features of the local environment of an atom. These descriptors are based on the spectrum of two-body, three-body, four-body, and higher order correlations between an atom and its neighbors and are evaluated by calculating the corresponding two-body, three-body, and four-body overlap integrals. They are invariant to global translation, global rotation, reflection, and permutations of atomic indices. By systematically testing the ability to capture the local atomic environment, it is shown that the local correlation descriptors are able to successfully reconstruct structures containing 10-25 atoms which was previously not possible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1998

Antiproton Trapping for Advanced Space Propulsion Applications

The Summary of Research parallels the Statement of Work (Appendix I) submitted with the proposal, and funded effective Feb. 1, 1997 for one year. A proposal was submitted to CERN in October, 1996 to carry out an experiment on the synthesis and study of fundamental properties of atomic antihydrogen. Since confined atomic antihydrogen is potentially the most powerful and elegant source of propulsion energy known, its confinement and properties are of great interest to the space propulsion community. Appendix II includes an article published in the technical magazine Compressed Air, June 1997, which describes CERN antiproton facilities, and ATHENA. During the period of this grant, Prof. Michael Holzscheiter served as spokesman for ATHENA and, in collaboration with Prof. Gerald Smith, worked on the development of the antiproton confinement trap, which is an important part of the ATHENA experiment. Appendix III includes a progress report submitted to CERN on March 12, 1997 concerning development of the ATHENA detector. Section 4.1 reviews technical responsibilities within the ATHENA collaboration, including the Antiproton System, headed by Prof. Holzscheiter. The collaboration was advised (see Appendix IV) on June 13, 1997 that the CERN Research Board had approved ATHENA for operation at the new Antiproton Decelerator (AD), presently under construction. First antiproton beams are expected to be delivered to experiments in about one year. Progress toward assembly of the ATHENA detector and initial testing expected in 1999 has been excellent. Appendix V includes a copy of the minutes of the most recently documented collaboration meeting held at CERN of October 24, 1997, which provides more information on development of systems, including the antiproton trapping apparatus. On February 10, 1998 Prof. Smith gave a 3 hour lecture on the Physics of Antimatter, as part of the Physics for the Third Millennium Lecture Series held at MSFC. Included in Appendix VI are notes and graphs presented on the ATHENA experiment. Portable antiproton trap has been under development. The goal is to store and transport antiprotons from a production site, such as Fermilab near Chicago, to a distant site, such as Huntsville, AL, thus demonstrating the portability of antiprotons.

Smith, Gerald A.↗
DOE OSTI2025

Search for Stable and Low-Energy Ce–Co–Cu Ternary Compounds Using Machine Learning

Cerium-based intermetallics have garnered significant research attention as potential new permanent magnets. In this study, we explore the compositional and structural landscape of Ce−Co−Cu ternary compounds using a machine learning (ML)- guided framework integrated with first-principles calculations. We employ a crystal graph convolutional neural network (CGCNN), which enables efficient screening for promising candidates, significantly accelerating the material discovery process. With this approach, we predict five stable compounds, Ce 3 Co 3 Cu, CeCoCu 2 , Ce 12 Co 7 Cu, Ce 11 Co 9 Cu, and Ce 10 Co 11 Cu 4 , with formation energies below the convex hull, along with hundreds of low-energy (possibly metastable) Ce−Co−Cu ternary compounds. Firstprinciples calculations reveal that several structures are both energetically and dynamically stable. Notably, two Co-rich low-energy compounds, Ce 4 Co 33 Cu and Ce 4 Co 31 Cu 3 , are predicted to have high magnetizations.

Chemical structure↗
DOE OSTI2016

Improved Mechanical Performance Fracture Properties and Reliability of Radical-Cured Thermosets

The aim of this study was to alter polymerization chemistry to improve network homogeneity in free-radical crosslinked systems. It was hypothesized that a reduction in heterogeneity of the network would lead to improved mechanical performance. Experiments and simulations were carried out to investigate the connection between polymerization chemistry, network structure and mechanical properties. Experiments were conducted on two different monomer systems - the first is a single monomer system, urethane dimethacrylate (UDMA), and the second is a two-monomer system consisting of bisphenol A glycidyl dimethacrylate (BisGMA) and triethylene glycol dimethacrylate (TEGDMA) in a ratio of 70/30 BisGMA/TEGDMA by weight. The methacrylate systems were crosslinked using traditional radical polymeriza- tion (TRP) with azobisisobutyronitrile (AIBN) or benzoyl peroxide (BPO) as an initiator; TRP systems were used as the control. The monomers were also cross-linked using activator regenerated by electron transfer atom transfer radical polymerization (ARGET ATRP) as a type of controlled radical polymerization (CRP). FTIR and DSC were used to monitor reac- tion kinetics of the systems. The networks were analyzed using NMR, DSC, X-ray diffraction (XRD), atomic force microscopy (AFM), and small angle X-ray scattering (SAXS). These techniques were employed in an attempt to quantify differences between the traditional and controlled radical polymerizations. While a quantitative methodology for characterizing net- work morphology was not established, SAXS and AFM have shown some promising initial results. Additionally, differences in mechanical behavior were observed between traditional and controlled radical polymerized thermosets in the BisGMA/TEGDMA system but not in the UDMA materials; this finding may be the result of network ductility variations between the two materials. Coarse-grained molecular dynamics simulations employing a novel model of the CRP reaction were carried out for the UDMA system, with parameters calibrated based on fully atomistic simulations of the UDMA monomer in the liquid state. Detailed metrics based on network graph theoretical approaches were implemented to quantify the bond network topology resulting from simulations. For a broad range of polymerization parameters, no discernible differences were seen between TRP and CRP UDMA simulations at equal conversions, although clear differences exist as a function of conversion. Both findings are consistent with experiments. Despite a number of shortcomings, these models have demonstrated the potential of molecular simulations for studying network topology in these systems.

36 MATERIALS SCIENCE↗
NASA NTRS2004

Optical Calibration Process Developed for Neural-Network-Based Optical Nondestructive Evaluation Method

A completely optical calibration process has been developed at Glenn for calibrating a neural-network-based nondestructive evaluation (NDE) method. The NDE method itself detects very small changes in the characteristic patterns or vibration mode shapes of vibrating structures as discussed in many references. The mode shapes or characteristic patterns are recorded using television or electronic holography and change when a structure experiences, for example, cracking, debonds, or variations in fastener properties. An artificial neural network can be trained to be very sensitive to changes in the mode shapes, but quantifying or calibrating that sensitivity in a consistent, meaningful, and deliverable manner has been challenging. The standard calibration approach has been difficult to implement, where the response to damage of the trained neural network is compared with the responses of vibration-measurement sensors. In particular, the vibration-measurement sensors are intrusive, insufficiently sensitive, and not numerous enough. In response to these difficulties, a completely optical alternative to the standard calibration approach was proposed and tested successfully. Specifically, the vibration mode to be monitored for structural damage was intentionally contaminated with known amounts of another mode, and the response of the trained neural network was measured as a function of the peak-to-peak amplitude of the contaminating mode. The neural network calibration technique essentially uses the vibration mode shapes of the undamaged structure as standards against which the changed mode shapes are compared. The published response of the network can be made nearly independent of the contaminating mode, if enough vibration modes are used to train the net. The sensitivity of the neural network can be adjusted for the environment in which the test is to be conducted. The response of a neural network trained with measured vibration patterns for use on a vibration isolation table in the presence of various sources of laboratory noise is shown. The output of the neural network is called the degradable classification index. The curve was generated by a simultaneous comparison of means, and it shows a peak-to-peak sensitivity of about 100 nm. The following graph uses model generated data from a compressor blade to show that much higher sensitivities are possible when the environment can be controlled better. The peak-to-peak sensitivity here is about 20 nm. The training procedure was modified for the second graph, and the data were subjected to an intensity-dependent transformation called folding. All the measurements for this approach to calibration were optical. The peak-to-peak amplitudes of the vibration modes were measured using heterodyne interferometry, and the modes themselves were recorded using television (electronic) holography.

Decker, Arthur J.↗
DOE OSTI2025

A quantitative comparison of the fingerprint of twinned microstructures through surface and three-dimensional techniques

Assessing the fingerprint of a material’s microstructure is key for supporting materials design. With the emergence of a wide range of 3D characterization techniques, it is critical to understand the main differences in fingerprints reconstructed from 2D and 3D datasets. To this end, we introduce a graph-based microstructure reconstruction framework that enables structural comparisons of twin domain networks in high purity Ti using 3D and 2D electron backscatter diffraction. Insights into the structure of the twin networks are facilitated by combining statistical analysis of twin crystallography with visual and graphical analysis of the novel graph abstractions of the twins. We demonstrate that compared to 3D reconstructions, conventional 2D views of twinning miss key aspects of the microstructure including the high interconnectivity of domains into networks that span the full reconstruction volume. The reduced cross-grain and in-grain twin connectivity typically observed in 2D has notable implications on our understanding of how twinning mediates the plastic response of microstructures and how twin networks evolve. It is thus clear that 3D characterization is critical for accurately inferring both twin network morphologies as well as the key unit processes facilitating network formation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Verification Testing of OLI Systems Mixed Solvent Electrolyte Model for the Na-K-Mg-Ca-H-Cl-SO 4 -OH-HCO 3 -CO 3 -CO 2 -H 2 ) System to High Ionic Strength at 25°C

This technical report summarizes model verification results and summary statistics for 41 evaporite mineral solubility cases evaluated by Savannah River National Laboratory using OLI Systems’ aqueous electrolyte thermodynamic modeling software. The 41 verification cases containing a total of 60 solubility curves comprise mineral solubility data from low to high ionic strength at 25°C for the eight-component system Na-K-Mg-Ca-H-Cl-SO 4 -OH-HCO 3 -CO 3 -CO 2 -H 2 O as reported by Harvie et al. (1984). Thermodynamic calculations were executed using OLI Systems’ Stream Analyzer computation module within the OLI Studio software platform (Ver. 11.0, Rev. 11.0.1.9). The Mixed Solvent Electrolyte (MSE) thermodynamic framework was chosen for this investigation because of its superiority in modeling high ionic-strength inorganic salt solutions and actinide redox chemistry and solubility, both of which are relevant to the geological repository conditions at the Waste Isolation Pilot Plant in Carlsbad, New Mexico. Mineral solubility data in various inorganic salt solutions were digitized and extracted from figures generated by Harvie et al. (1984). For each of the 60 solubility curves, a case-specific chemistry model and input file were generated in OLI Studio using OLI Stream Analyzer and the MSE (H 3 O + ion) public databank provided by OLI Systems. Model simulation results were exported to Microsoft Excel to calculate summary statistics and to generate graphs comparing the OLI model predictions to the solubility data. Summary statistics include residuals (model – data) and concordance (accuracy × precision, where precision is indicated by the Pearson correlation coefficient and accuracy accounts for bias and scale differential). Private databanks were not developed, and activity coefficient model regressions were not performed to improve OLI model fits to the data. Of the 41 model verification plots, 83% have a mean of the percent residuals less than or equal to 25%. Similarly, 75% display a concordance greater than or equal to 0.75. Only seven of the 41 verification plots fail to show good agreement between the model and data. Of these seven, three are relevant to the WIPP repository because they involve the Mg-OH-Cl-SO 4 -CO 3 aqueous system. The remaining four address salt solubilities at the pH extremes (strong acid and strong base). It should be noted that in two of the three Mg-OH-Cl-SO 4 -CO 3 system cases, the regressed Harvie et al. (1984) solubility curve also deviated from the data. Lack of agreement between the OLI model-predicted solubility curves and the data is attributable to one or more of the following: specific solid species are not included in the OLI MSE databank; there is significant variation among the different solubility datasets chosen by Harvie et al. (1984); the OLI MSE model’s thermodynamic parameters were determined using different solubility datasets; and the activity coefficient parameters for certain relevant ion-ion and ion-molecule pairs have not been optimized via data regression. Two recommendations for future work are to (1) evaluate solubility data for the Mg-OH-Cl-SO 4 -CO 3 system at high ionic strength and, if necessary, develop a private OLI MSE database that includes missing species and, where necessary, regressed standard state properties and interaction parameters; (2) perform similar verification testing of the OLI model for actinide solubility data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Faster Johnson–Lindenstrauss transforms via Kronecker products

The Kronecker product is an important matrix operation with a wide range of applications in signal processing, graph theory, quantum computing and deep learning. In this work, we introduce a generalization of the fast Johnson–Lindenstrauss projection for embedding vectors with Kronecker product structure, the Kronecker fast Johnson–Lindenstrauss transform (KFJLT). The KFJLT reduces the embedding cost by an exponential factor of the standard fast Johnson–Lindenstrauss transform’s cost when applied to vectors with Kronecker structure, by avoiding explicitly forming the full Kronecker products. Here, we prove that this computational gain comes with only a small price in embedding power: consider a finite set of $p$ points in a tensor product of $d$ constituent Euclidean spaces $\bigotimes _{k=d}^{1}{\mathbb{R}}^{n_k}$, and let $N = \prod _{k=1}^{d}n_k$. With high probability, a random KFJLT matrix of dimension $m \times N$ embeds the set of points up to multiplicative distortion $(1\pm \varepsilon )$ provided $m \gtrsim \varepsilon ^{-2} \, \log ^{2d - 1} (p) \, \log N$. We conclude by describing a direct application of the KFJLT to the efficient solution of large-scale Kronecker-structured least squares problems for fitting the CP tensor decomposition.

Kronecker structure↗
DOE OSTI2017

Monte Carlo Estimates of Eigenvalue Sensitivity to System Dimensions using Kernel Density Estimators

In the last decade there has been an increasing interest in calculating sensitivity coefficients in order to quantify the impact of uncertainty in material properties and system parameters on quantities of interest. Extensive work has been done developing Monte Carlo methods to estimate eigenvalue sensitivity to material properties. While deterministic methods exist for determining the eigenvalue sensitivity to system dimensions, the use of Monte Carlo methods to efficiently calculate eigenvalue sensitivity coefficients to system dimensions is still an open topic. Current Monte Carlo methods for calculating eigenvalue sensitivity coefficients to system dimensions are inefficient. Direct Monte Carlo perturbations require two additional simulations of high statistical precision in order to determine the eigenvalue sensitivity to the location of a single boundary. If the sensitivity to the location of multiple surfaces are desired, two additional simulations must be run for each surface. Kiedrowski and Favorite extended continuous energy Monte Carlo methods for sensitivities of nuclear cross sections to calculate sensitivities to interface boundaries or system dimensions to create the only current alternative to direct Monte Carlo calculations. However this method relies on the simulation of additional random walks launched at each surface crossing event. The present method extends upon Kiedrowski's work and eliminates the inefficient portions of the calculation through the use of Kernel Density Estimators (KDEs). The new method efficiently estimates eigenvalue sensitivities to the uniform expansion or contraction of material interface boundaries or system dimensions, negligibly impacting the runtime performance of the Monte Carlo code. The new estimator is tested on bare and reflected critical configurations in spherical geometry and is compared to estimates obtained from direct Monte Carlo perturbations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Structural constraint integration in a generative model for the discovery of quantum materials

Billions of organic molecules have been computationally generated, yet functional inorganic materials remain scarce due to limited data and structural complexity. Here, in this work, we introduce Structural Constraint Integration in a GENerative model (SCIGEN), a framework that enforces geometric constraints, such as honeycomb and kagome lattices, within diffusion-based generative models to discover stable quantum materials candidates. SCIGEN enables conditional sampling from the original distribution, preserving output validity while guiding structural motifs. This approach generates ten million inorganic compounds with Archimedean and Lieb lattices, over 10% of which pass multistage stability screening. High-throughput density functional theory calculations on 26,000 candidates shows over 95% convergence and 53% structural stability. A graph neural network classifier detects magnetic ordering in 41% of relaxed structures. Furthermore, we synthesize and characterize two predicted materials, TiPd 0.22 Bi 0.88 and Ti 0.5 Pd 1.5 Sb, which display paramagnetic and diamagnetic behaviour, respectively. Our results indicate that SCIGEN provides a scalable path for generating quantum materials guided by lattice geometry.

36 MATERIALS SCIENCE↗
NASA NTRS2004

Depth-Penetrating Measurements Developed for Thermal Barrier Coatings Incorporating Thermographic Phosphors

The insulating properties of thermal barrier coatings (TBCs) provide highly beneficial thermal protection to turbine engine components by reducing the temperature sustained by those components. Therefore, measuring the temperature beneath the TBC is critical for determining whether the TBC is performing its insulating function. Currently, noncontact temperature measurements are performed by infrared pyrometry, which unfortunately measures the TBC surface temperature rather than the temperature of the underlying component. To remedy this problem, the NASA Glenn Research Center, under the Information Rich Test Instrumentation Project, developed a technique to measure the temperature beneath the TBC by incorporating a thin phosphor layer beneath the TBC. By performing fluorescence decay-time measurements on light emission from this phosphor layer, Glenn successfully measured temperatures from the phosphor layer up to 1100 C. This is the first successful demonstration of temperature measurements that penetrate beneath the TBC. Thermographic phosphors have a history of providing noncontact surface temperature measurements. Conventionally, a thermographic phosphor is applied to the material surface and temperature measurements are performed by exciting the phosphor with ultraviolet light and then measuring the temperature-dependent decay time of the phosphor emission at a longer wavelength. The innovative feature of the new approach is to take advantage of the relative transparency of the TBC (composed of yttria-stabilized zirconia) in order to excite and measure the phosphor emission beneath the TBC. The primary obstacle to achieving depth-penetrating temperature measurements is that the TBCs are completely opaque to the ultraviolet light usually employed to excite the phosphor. The strategy that Glenn pursued was to select a thermographic phosphor that could be excited and emit at wavelengths that could be transmitted through the TBC. The phosphor that was selected was yttria doped with europia (Y2O3:Eu), which has a minor excitation peak at 532 nm (green) and an emission peak at 611 nm (red)--both are wavelengths that exhibit significant transmission through the TBC. The measurements were performed on specimens consisting of a 25- m-thick phosphor layer beneath a 100- m-thick TBC. The 532-nm (green) excitation light was provided by a frequency-doubled YAG:Nd (yttrium-aluminum-garnet:neodymium) laser, and the fluorescence decay time measurements were acquired with a modified Raman microscope. The preceding graph compares the intensity of the phosphor emission of the phosphor layer above the TBC versus that of the phosphor layer beneath the TBC. Although there was considerable attenuation of the phosphor signal (a factor of 30), the phosphor emission at the reduced intensity was more than sufficient to perform fluorescence decay time measurements. The following graph shows the fluorescence lifetime temperature dependency for the Y2O3:Eu phosphor layers both above and below the TBC. These curves show an excellent match and indicate that, despite the attenuation due to the overlying TBC, the phosphor layer beneath the TBC still functions as an effective temperature indicator.

Eldridge, Jeffrey I.↗
DOE OSTI2023

Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics

Computational methods for creating in silico libraries of molecular descriptors (e.g., collision cross sections) are becoming increasingly prevalent due to the limited number of authentic reference materials available for traditional library building. These so-called “reference-free metabolomics” methods require sampling sets of molecular conformers in order to produce high accuracy property predictions. Due to the computational cost of the subsequent calculations for each conformer, there is a need to sample the most relevant subset and avoid repeating calculations on conformers that are nearly identical. The goal of this study is to introduce a heuristic method of finding the most dissimilar conformers from a larger population in order to help speed up reference-free calculation methods and maintain a high property prediction accuracy. Finding the set of the n items most dissimilar from each other out of a larger population becomes increasingly difficult and computationally expensive as either n or the population size grows large. Because there exists a pairwise relationship between each item and all other items in the population, finding the set of the n most dissimilar items is different than simply sorting an array of numbers. For instance, if you have a set of the most dissimilar n = 4 items, one or more of the items from n = 4 might not be in the set n = 5. An exact solution would have to search all possible combinations of size n in the population exhaustively. We present an open-source software called similarity downselection (SDS), written in Python and freely available on GitHub. SDS implements a heuristic algorithm for quickly finding the approximate set(s) of the n most dissimilar items. We benchmark SDS against a Monte Carlo method, which attempts to find the exact solution through repeated random sampling. We show that for SDS to find the set of n most dissimilar conformers, our method is not only orders of magnitude faster, but it is also more accurate than running Monte Carlo for 1,000,000 iterations, each searching for set sizes n = 3–7 out of a population of 50,000. We also benchmark SDS against the exact solution for example small populations, showing that SDS produces a solution close to the exact solution in these instances. Using theoretical approaches, we also demonstrate the constraints of the greedy algorithm and its efficacy as a ratio to the exact solution.

97 MATHEMATICS AND COMPUTING↗