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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quantitative Imaging of Cobalt Phthalocyanine Distribution on Carbon Nanotubes: A Deep Learning Approach to Catalyst Characterization

Electrochemical reduction of carbon dioxide (CO 2 ) offers a pathway to valuable products, with catalysts playing a crucial role. This study investigates the distribution of cobalt tetraaminophthalocyanine (CoPc-NH 2 ) immobilized on carbon nanotubes (CNTs), utilizing high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) to characterize CoPc-NH 2 distribution. A challenge in the quantitative HAADF-STEM analysis is the introduction of bias from manual Co atom identification. To address this, we developed and trained a convolutional neural network (CNN) using a data set generated from images of CoPc-NH 2 /CNT samples with varying Co loadings. The CNN, implemented in TensorFlow and Keras, facilitated Co atom detections. Analysis of the CNN-generated data confirmed a correlation between Co loading and surface density, consistent with findings from UV–vis spectroscopy. Furthermore, the application of Ripley’s L(d) function highlighted the presence of slight Co atom clustering. Furthermore, this work demonstrates the utility of the combined HAADF-STEM and CNN approach for providing spatially resolved information about catalyst distribution on nonplanar supports, revealing structural details that are typically lost through other characterization methods.

HAADF-STEM↗
DOE OSTI2023

Quantifying Atomically Dispersed Catalysts Using Deep Learning Assisted Microscopy

The catalytic performance of atomically dispersed catalysts (ADCs) is greatly influenced by their atomic configurations, such as atom–atom distances, clustering of atoms into dimers and trimers, and their distributions. Scanning transmission electron microscopy (STEM) is a powerful technique for imaging ADCs at the atomic scale; however, most STEM analyses of ADCs thus far have relied on human labeling, making it difficult to analyze large data sets. Here, we introduce a convolutional neural network (CNN)-based algorithm capable of quantifying the spatial arrangement of different adatom configurations. The algorithm was tested on different ADCs with varying support crystallinity and homogeneity. Results show that our algorithm can accurately identify atom positions and effectively analyze large data sets. Here, this work provides a robust method to overcome a major bottleneck in STEM analysis for ADC catalyst research. We highlight the potential of this method to serve as an on-the-fly analysis tool for catalysts in future in situ microscopy experiments.

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

Uncertainty-aware molecular dynamics from Bayesian active learning for phase transformations and thermal transport in SiC

Abstract Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present a general Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide (SiC) polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured. The resulting model exhibits close agreement with both ab initio calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set

Abstract This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases ofd-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictions across the transition metals for two leading MLFF models: a kernel-based atomic cluster expansion method implemented using sparse Gaussian processes (FLARE), and an equivariant message-passing neural network (NequIP). Early transition metals present higher relative errors and are more difficult to learn relative to late platinum- and coinage-group elements, and this trend persists across model architectures. Trends in complexity of interatomic interactions for different metals are revealed via comparison of the performance of representations with different many-body order and angular resolution. Using arguments based on perturbation theory on the occupied and unoccupieddstates near the Fermi level, we determine that the large, sharpddensity of states both above and below the Fermi level in early transition metals leads to a more complex, harder-to-learn potential energy surface for these metals. Increasing the fictitious electronic temperature (smearing) modifies the angular sensitivity of forces and makes the early transition metal forces easier to learn. This work illustrates challenges in capturing intricate properties of metallic bonding with current leading MLFFs and provides a reference data set for transition metals, aimed at benchmarking the accuracy and improving the development of emerging machine-learned approximations.

Chemistry↗
DOE OSTI2026

Optimal invariant sets for atomistic machine learning

The representation of atomic configurations for machine learning models has led to numerous sets of descriptors. However, many descriptor sets are incomplete and/or functionally dependent. Incomplete sets cannot faithfully represent atomic environments. Yet complete constructions often suffer from a high degree of functional dependence, where some descriptors are functions of others. These redundant descriptors do not improve discrimination between atomic environments. We employ pattern recognition techniques to remove dependent descriptors to produce the smallest possible set that satisfies completeness. We apply this in two ways: First, we refine an existing description, the atomic cluster expansion. Second, we augment an incomplete construction, yielding a new message-passing neural network architecture that can recognize up to 5-body patterns. This architecture shows strong accuracy on state-of-the-art benchmarks while retaining low computational cost. Our results demonstrate the utility of this strategy to optimize descriptor sets across a range of descriptors and application datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Elucidating ion capture and transport mechanisms of Preyssler anions in aqueous solutions using biased MACE-accelerated MD simulations

Equilibrium and biased multi-atomic cluster expansion (MACE) accelerated molecular dynamics (MD) simulations in aqueous solutions are performed to investigate the ion capture and transport mechanisms of the {P 5 W 30 } Preyssler anion (PA) as the smallest representative member of the extended polyoxometalate (POM) family with an internal cavity. The unique interatomic interactions present in the internal cavity vs the exterior of PA are carefully investigated using equilibrium MACE MD simulations for two representative Na(H 2 O)@PA and Na@PA complexes in aqueous solutions. Our careful analyses of radial distribution functions and coordination numbers show that the presence of confined water in Na(H 2 O)@PA has profound modulating effects on the nature of the interactions of the encapsulated ion with the oxygens of the PA cavity. Using well-converged MACE-accelerated multiple walker well-tempered metadynamics simulations with nanosecond timescales, two different associative (ion exchange) and dissociative (ion ejection) ion transport mechanisms were carefully investigated for Na + as one of the most abundant and representative ions present in seawater and saline solutions. By comparing systems with and without confined water, it was found that the presence of only one pre-encapsulated confined water in Na(H 2 O)@PA dramatically changes the free energy landscape of ion transport processes. It was also found that the contraction and dilation of the two windows present in PA directly influence the Na + and H 2 O transport. Furthermore, the results from this work are helpful, as they show a viable path toward tuning the ion exchange and transport phenomena in aqueous solutions of POM molecular clusters and frameworks.

25 ENERGY STORAGE↗
DOE OSTI2025

Ferroelectric phase transition in group-IV monochalcogenides from an equivariant machine learned force field

Group-IV monochalcogenides are a class of layered ferroelectric semiconductors that have demonstrated spontaneous intrinsic polarization above room temperature. Here, in this study, we use the multi-atomic cluster expansion (MACE) machine learning architecture to train and test a force field capable of modeling the structural properties and second-order ferroelectric-to-paraelectric phase transition in a Group-IV monochalcogenide, GeSe. The model captures the double-well potential energy surface associated with the onset of macroscopic polarization in bulk GeSe within 12.5 meV/atom, as well as near-equilibrium properties like the phonon dispersion. The development of this quantitatively accurate force field enables long-time molecular dynamics simulations, which predict the critical temperature of the ferroelectric-to-paraelectric phase transition in bulk GeSe to be T c = 600 K. This study demonstrates the capabilities of equivariant force-fields to accurately describe phenomena associated with structural symmetry breaking.

ferroelectricity↗
DOE OSTI2023

Control of intense light with avalanche-ionization plasma gratings

High-peak-power lasers are fundamental to high-field science: increased laser intensity has enabled laboratory astrophysics, relativistic plasma physics, and compact laser-based particle accelerators. However, the meter-scale optics required for multi-petawatt lasers to avoid light-induced damage make further increases in power challenging. Plasma tolerates orders-of-magnitude higher light flux than glass, but previous efforts to miniaturize lasers by constructing plasma analogs for conventional optics were limited by low efficiency and poor optical quality. We describe a new approach to plasma optics based on avalanche ionization of atomic clusters that produces plasma volume transmission gratings with dramatically increased diffraction efficiency. We measure an average efficiency of up to 36% and a single-shot efficiency of up to 60%, which is comparable to key components of high-power laser beamlines, while maintaining high spatial quality and focusability. These results suggest that plasma diffraction gratings may be a viable component of future lasers with peak power beyond 10 PW.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Electrocatalytic alkene epoxidation at disrupted metal ensembles in blended electrolytes

The project aims to achieve a molecular understanding of oxygen-atom transfer from water to alkenes at electrocatalytic interfaces. Molecular oxygen is the most common oxygen-atom source for epoxidations, and our group is developing sustainable routes through which epoxidation of olefins is achieved using water as the oxygen source. This route can improve the safety of the reaction while also co-producing hydrogen, demonstrating the relevance of this reaction to the energy transition. If successful in our efforts, we may enable oxygen-atom transfer reactions at the anode of water electrolyzers in the place of conventional oxygen evolution, allowing for the synthesis of sustainable value-added co-products. In this vein, we explore several approaches to acquiring high selectivity toward epoxidation over competing reactions, such as oxygen evolution. One of our aims is to allow for rational control of epoxide selectivity by disrupting contiguous metal ensembles at the surface of catalytic metal oxide nanoparticles. Specifically, we aim to synthesize single-atom, few-atom, and many-atom clusters supported on metal oxides and study the mechanism of oxygen evolution and alkene epoxidation on these materials. Thus, this approach will determine the impact of disrupting metal ensembles on the selectivity for alkene epoxidation versus oxygen evolution in blended electrolytes. Another aim is to develop a molecular-level understanding of how a blended electrolyte (i.e., a mixture of aqueous and organic solvents) influences rates of alkene epoxidation versus oxygen evolution. In other words, we are interested in understanding the catalytic influence of the solvent, as our preliminary work shows that the selectivity and reactivity of epoxidation depend strongly on the solvent composition. This investigation includes blended electrolytes and electrolytes containing redox mediator species that improve selectivity toward the desired epoxidation reaction. Overall, our proposed work will help to provide a detailed molecular-level picture of how solvents interact with substrates at the electrode-electrolyte interface, including their involvement in proton transfer reactions and screening of electric fields.

14 SOLAR ENERGY↗
NASA NTRS1978

Photon degradation effects in terrestrial solar cells

Reduction in cell output was observed in N(+)/P cells upon exposure to illumination or upon the application of a sufficiently high forward bias. Conversely, an enhancement in output was observed when P(+)/N cells were illuminated. Investigations performed on N(+)/P cells indicated that a recombination center located at E sub c - 0.37 eV in the forbidden band was responsible for the loss in output. The center was electrically inactive in its ground state but was activated either by raising the minority carrier quasi-Fermi level sufficiently close to the latent center energy level in the band gap, or by direct excitation of electrons from the valence band to the latent center level. The center was identified as a complex of a lattice defect and a silver atom or cluster of atoms.

Weizer, V. G.↗
NASA NTRS1979

Photon-degradation effects in terrestrial silicon solar cells

The effect of instability in terrestrial solar cells and identification of mechanisms involved are presented. The effect is similar to photon-induced degradation in radiation-damaged space solar cells, with reduction in cell output in n(+)/p cells upon exposure to illumination or upon the application of a sufficiently high forward bias. It was found that the photon-degradation effect is caused by a recombination center identified as a complex of a lattice defect and a silver atom or cluster of atoms. The center is electrically inactive in its ground state but can be activated by raising the minority-carrier quasi-Fermi level to coincide with the position of the latent-center level in the band gap, or by direct excitation of electrons from the valence band to the latent-center level. Photon degradation can be prevented by avoiding the introduction of silver through the use of a clean diffusion system and clean initial material, or by eliminating lattice damage by sufficient surface material removal prior to diffusion and restricting diffusion temperatures to 875 C or below.

Weizer, V. G.↗
NASA NTRS1980

Carbon-fiber technology

The state of the art of PAN based carbon fiber manufacture and the science of fiber behavior is surveyed. A review is given of the stabilization by oxidation and the subsequent carbonization of fibers, of the apparent structure of fibers deduced from scanning electron microscopy, from X-ray scattering, and from similarities with soft carbons, and of the known relations between fiber properties and heat treatment temperature. A simplified model is invoked to explain the electrical properties of fibers and recent quantum chemical calculations on atomic clusters are used to elucidate some aspects of fiber conductivity. Some effects of intercalation and oxidative modification of finished fibers are summarized.

Hansen, C. F.↗
NASA NTRS1989

Electronic structure of BaO/W cathode surfaces

The local electronic structure of the emissive layer of barium dispenser thermionic cathodes is investigated theoretically using the relativistic scattered-wave approach. The interaction of Ba and O with W, Os, and W-Os alloy surfaces is studied with atomic clusters modeling different absorption environments representative of B- and M-type cathodes. Ba is found to be strongly oxidized, while O and the metal substrate are in a reduced chemical state. The presence of O enhances the surface dipole and Ba binding energy relative to Ba on W. Model results for W-Os alloy substrates show only relatively small changes in Ba and O for identical geometries, but very large charge redistributions inside the substrate, which are attributed to the electronegativity difference between Os and W. If Os is present in the surface layer, the charge transfer from Ba to the substrate and the Ba binding energy increase relative to W. Explanations are offered for the improved electron emission from alloy surfaces and the different emission enhancement for different alloy substrates.

Muller, Wolfgang↗
NASA NTRS2002

Modeling of the Site Preference in Ternary B2-Ordered Ni-Al-Fe Alloys

The underlying equilibrium structure, site substitution behavior, and lattice parameter of ternary Ni-Fe-Al alloys are determined via Monte Carlo-Metropolis computer simulations and analytical calculations using the BFS method for alloys for the energetics. As a result of the theoretical calculations presented, a simple approach based on the energetics of small atomic clusters is introduced to explain the observed site preference schemes.

Bozzolo, Guillermo H.↗
NASA NTRS2003

Difference in Icosahedral Short-Range Order in Early and Late Transition Metals Liquids

New short-range order data are presented for equilibrium and undercooled liquids of Ti and Ni. These were obtained from in-situ synchrotron x-ray diffraction measurements of electrostatically-levitated droplets. While the short-range order of liquid Ni is icosahedral, consistent with Frank's hypothesis, significantly distorted icosahedral order is observed in liquid Ti. This is the first experimental observation of distorted icosahedral short-range order in any liquid, although this has been predicted by theoretical studies on atomic clusters.

Lee, G. W.↗
NASA NTRS2004

Difference in Icosahedral Short-Range Order in Early and Late Transition Metal Liquids

New short-range order data are presented for equilibrium and undercooled liquids of Ti and Ni. These were obtained from in-situ synchrotron x-ray diffraction measurements of electrostatically-levitated droplets. While the short-range order of liquid Ni is icosahedral, consistent with Frank's hypothesis, significantly distorted icosahedral order is observed in liquid Ti. This is the first experimental observation of distorted icosahedral short-range order in any liquid. although this has been predicted by theoretical studies on atomic clusters.

Lee, G. W.↗
NASA NTRS2019

Secondary Ion Mass Spectroscopy

Secondary Ion mass Spectroscopy (SIMS), as the name suggests, involves characterizing metallic and other materials trough the spectroscopic analysis of secondary ions emanating from the surface of the material to be characterized by the impact of the high energy primary ions. The primary ion source including the choice of its gun, voltage and current can be selected and used depending on the purpose of the analysis. In most instruments more than one primary ion gun is lined up to the sample stage and can be activated with selected accelerating parameters (voltage and beam intensity). The impingement of primary ions on to the sample surface generates positive, negative or neutral ions, electrons, atoms and atomic clusters. Majority of these sample fragments being neutral, could not be utilized as such fragments cannot be manipulated through the use of electromagnetic or electrostatic lenses. Secondary ions that are positively or negatively charged possess large variation in velocity, charge, and mass. These ionic fragments eventually travel through a system of several lenses in very high vacuum to reach detector/counter. Relative amounts of alloying elements or impurities in an alloy can be calculated from the counts of related ions accumulated in the detector/counter.

Panda, Binayak↗
DOE OSTI2022

Interfacial Unit-Dependent Catalytic Activity for CO Oxidation over Cerium Oxysulfate Cluster Assemblies

Atomically precise cerium oxo clusters offer a platform to investigate structure–property relationships that are much more complex in the ill-defined bulk material cerium dioxide. We investigated the activity of the MCe 70 torus family (M = Cd, Ce, Co, Cu, Fe, Ni, and Zn), a family of discrete oxysulfate-based Ce 70 rings linked by monomeric cation units, for CO oxidation. CuCe 70 emerged as the best performing MCe 70 catalyst among those tested, prompting our exploration of the role of the interfacial unit on catalytic activity. Temperature-programmed reduction (TPR) studies of the catalysts indicated a lower temperature reduction in CuCe 70 as compared to CeCe 70 . In situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) indicated that CuCe 70 exhibited a faster formation of Ce 3+ and contained CO bridging sites absent in CeCe 70 . Isothermal CO adsorption measurements demonstrated a greater uptake of CO by CuCe 70 as compared to CeCe 70 . The calculated energies for the formation of a single oxygen defect in the structure significantly decreased with the presence of Cu at the linkage site as opposed to Ce. Furthermore, this study revealed that atomic-level changes in the interfacial unit can change the reducibility, CO binding/uptake, and oxygen vacancy defect formation energetics in the MCe 70 family to thus tune their catalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗