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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Toward Understanding and Controlling Organic Reactions on Metal Oxide Catalysts

Metal oxides have structurally complex surfaces on which a variety of adsorption site types can occur, including cation sites, anion sites, oxygen vacancy sites, and Brønsted acid sites. These sites can catalyze the catalytic transformation of organic molecules via diverse routes, thus enabling H abstraction, O abstraction, C–C bond formation, and other reactions. This Perspective provides an update on recent advances and future directions for various organic reactions on metal oxide catalyst surfaces, particularly for C–H activation of alkanes and for C–C bond formation with organic oxygenate reactants. Here, we put emphasis on the molecular scale details, on the active site structures required to enable the formation of kinetically relevant transition states, energetic descriptors, as well as contemporary ideas to enable low activation energies. This progress has been enabled by specialized experiments and the increased capabilities of modern electronic structure calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Enhanced descriptor identification and mechanism understanding for catalytic activity using a data-driven framework: revealing the importance of interactions between elementary steps

We report accurate identification of descriptors for catalytic activities has long been essential to the in-depth understanding of catalysis and recently to set the basis for catalyst screening. However, commonly used methods suffer from low accuracy in predictability. This study reports an enhanced approach to accurately identify the descriptors from a kinetic dataset using a machine learning (ML) surrogate model. CO hydrogenation to methanol over Cu-based catalysts was taken as a case study. Our model captures not only the contribution from individual elementary steps but also the interaction between relevant steps within a reaction network, which was found to be essential for high accuracy. As a result, six effective descriptors are identified, which are accurate enough to ensure the trained gradient boosted regression (GBR) model for good prediction of the methanol turnover frequency (TOF) over metal (M)-doped Cu(111) model surfaces (M = Au, Cu, Pd, Pt, Ni). More importantly, going beyond the purely mathematical ML model, the catalytic role of each identified descriptor can be revealed by using model-agnostic interpretation tools, which enhances the insight into the promoting effect of alloying. The trained GBR model outperforms the conventional derivative-based methods in terms of both the predictability and the mechanism understanding. It opens alternative possibilities toward accurate descriptor-based rational catalyst optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1990

On the mathematical modeling of the Reynolds stress's equations

By considering the Reynolds stress equations as a possible descriptor of complex turbulent fields, pressure-velocity interaction and turbulence dissipation are studied as two of the main physical contributions to Reynolds stress balancing in turbulent flow fields. It is proven that the pressure interaction term contains turbulence generation elements. However, the usual 'return to isotropy' element appears more weakly than in the standard models. In addition, convection-like elements are discovered mathematically, but there is no mathematical evidence that the pressure fluctuations contribute to the turbulent transport mechanism. Calculations of some simple one-dimensional fields indicate that this extra convection, rather than the turbulent transport, is needed mathematically. Similarly, an expression for the turbulence dissipation is developed. The end result is a dynamic equation for the dissipation tensor which is based on the tensorial length scales.

Lin, Avi↗
DOE OSTI2022

Hydrolysis of Acetamide on Low-Index CeO 2 Surfaces: Ceria as a Deamidation and General De-esterification Catalyst

Using DFT calculations and acetamide as the main example, we show that ceria is a potential catalyst for the hydrolysis of amide and similar bonds. The overall reaction is endergonic in the gas phase, yielding acetic acid and ammonia, but is slightly exergonic in the aqueous phase, which facilitates ionization of the products (CH 3 COO – and NH 4 + ). Neighboring Ce and O sites on the CeO 2 (111), (110), and (100) facets are conducive to the formation of an activated metastable tetrahedral intermediate (TI) complex, followed by C–N bond scission. With van der Waals and solvation effects taken into account, the overall reaction energetics is found to be most favorable on the (111) facet as desorption of acetic acid is much more uphill energetically on (110) and (100). We further suggest that the Ce–O–Ce sites on ceria surfaces can activate X(=Y)–Z type bonds in amides, amidines, and carboxylate and phosphate esters, among many others that we term “generalized esters”. A Brønsted-Evans–Polanyi relationship is identified correlating the stability of the transition and final states of the X–Z generalized ester bond scission. A simple descriptor (ΣΔχ) based on the electronegativity of the atoms that constitute the bond (X, Y, Z) versus those of the catalytic site (O, Ce, Ce) captures the trend in the stability of the transition state of generalized ester bond scission and suggests a direction for modifying ceria for targeting specific organic substrates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Exploring Causal Physical Mechanisms via Non-Gaussian Linear Models and Deep Kernel Learning: Applications for Ferroelectric Domain Structures

Rapid emergence of multimodal imaging in scanning probe, electron, and optical microscopies has brought forth the challenge of understanding the information contained in these complex data sets, targeting the intrinsic correlations between different channels, and further exploring the underpinning causal physical mechanisms. Here, we develop such an analysis framework for Piezoresponse Force Microscopy. We argue that under certain conditions, we can bootstrap experimental observations with the prior knowledge of materials structure to get information on certain nonobserved properties, and demonstrate linear causal analysis for PFM observables. We further demonstrate that the strength of individual causal links between complex descriptors can be ascertained using the deep kernel learning (DKL) model. In this DKL analysis, we use the prior information on domain structure within the image to predict the physical properties. This analysis demonstrates the correlative relationships between morphology, piezoresponse, elastic property, etc., at nanoscale. The prediction of morphology and other physical parameters illustrates a mutual interaction between surface condition and physical properties in ferroelectric materials. Overall, this analysis is universal and can be extended to explore the correlative relationships of other multichannel data sets, and allow for high-fidelity reconstruction of underpinning functionalities and physical mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Microstructure prediction for Ti-22Al-25Nb in laser powder bed fusion

This work presents a physics-informed framework for predicting solidification morphology and defect susceptibility in additively manufactured Ti–22Al–25Nb across a broad processing space. The framework integrates solidification microstructure selection (SMS) analysis with a single-track defect-based printability map to establish a unified methodology linking processing parameters to both interfacial morphology and manufacturability. Thermal gradients G and solidification rates R are first computed using the Thermo-Calc Additive Manufacturing (TC-AM) module, a finite-interface-dissipation (FID) phase-field (PF) model coupled with CALPHAD method is then employed to systematically distinguish planar and dendritic regimes as functions of $G$ and $R$. By superimposing the printability map onto the morphology projections, a comprehensive process–structure framework is obtained. Across most processing conditions, the predicted microstructure is predominantly dendritic, while planar growth emerges only under selected laser power $P$ and scan speed $v$ combinations. In addition to morphology classification, the framework quantifies the dendritic area fraction and introduces a width-based morphology descriptor to characterize the spatial extent of planar/dendritic regions within the melt pool. It provides mechanistic insight into the interplay between solidification physics and defect formation, offering practical guidance for parameter selection and microstructural control in Ti–22Al–25Nb additive manufacturing (AM).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Thermodynamic Framework for Water in Deep Eutectic Solvents and Ionic Liquids

Water is widely recognized as critical to the tunability and electrochemical stability of nonaqueous solvents such as deep eutectic solvents (DESs) and ionic liquids (ILs). Traditionally, the water content of these solvents has been controlled by either drying or adding small amounts of water to control their bulk properties to meet specific application requirements. The total water content by itself, does not provide sufficient information about the chemical reactivity and molecular interactions within DES– and IL–water mixtures. In this concept article, water activity is highlighted as a thermodynamically more rigorous descriptor to quantify the influence of the co‐solvent water on DES– and IL–water mixtures. Water activity relates measurable physical properties, such as vapor pressure, density, viscosity, electrochemical stability, and conductivity of DESs and ILs to the underlying molecular interactions between their components. Furthermore, water activity of DESs and ILs correlates with changes in local solvent structures and thermodynamic excess properties, including excess molar volume, enthalpy, and Gibbs energy.

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

Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids

Ionic liquids (ILs) have unique solvent properties and have thus garnered significant interest. However, exhaustive experimental determination of the physicochemical properties of ILs is unrealistic due to the large structural diversity of anions and cations, their high cost, the requirements of elevated temperature and pressure, and the time required. To circumvent these experimental costs, computational approaches to accurately calculate these properties have emerged. Here in the present study, we present a demonstration of two machine learning (ML) models for the prediction of two critical IL physical properties, the surface tension and the speed of sound, across a wide range of temperatures and pressures. The models make use of molecular descriptors derived from the COSMO-RS, a quantum chemical-based model. The ML models show excellent agreement with experimental observations, with an R2 value of 0.96–0.99 and RMSE of 1.71 mN/m and 16.12 m/s for the surface tension and speed of sound, respectively. This work paves the way for the development of COSMO-RS-informed ML models for the prediction of IL properties which can help to further optimize and accelerate technology development for ILs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Benchmarking structural evolution methods for training of machine learned interatomic potentials

When creating training data for machine-learned interatomic potentials (MLIPs), it is common to create initial structures and evolve them using molecular dynamics (MD) to sample a larger configuration space. Here, we benchmark two other modalities of evolving structures, contour exploration (CE) and dimer-method (DM) searches against MD for their ability to produce diverse and robust density functional theory training data sets for MLIPs. We also discuss the generation of initial structures which are either from known structures or from random structures in detail to further formalize the structure-sourcing processes in the future. The polymorph-rich zirconium-oxygen composition space is used as a rigorous benchmark system for comparing the performance of MLIPs trained on structures generated from these structural evolution methods. Using Behler–Parrinello neural networks as our MLIP models, we find that CE and the DM searches are generally superior to MD in terms of spatial descriptor diversity and statistical accuracy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Decoding structure-spectrum relationships with physically organized latent spaces

Here, a semisupervised machine learning method for the discovery of structure-spectrum relationships is developed and then demonstrated using the specific example of interpreting x-ray absorption near-edge structure (XANES) spectra. This method constructs a one-to-one mapping between individual structure descriptors and spectral trends. Specifically, an adversarial autoencoder is augmented with a rank constraint (RankAAE). The RankAAE methodology produces a continuous and interpretable latent space, where each dimension can track an individual structure descriptor. As a part of this process, the model provides a robust and quantitative measure of the structure-spectrum relationship by decoupling intertwined spectral contributions from multiple structural characteristics. This makes it ideal for spectral interpretation and the discovery of descriptors. The capability of this procedure is showcased by considering five local structure descriptors and a database of >50 000 simulated XANES spectra across eight first-row transition metal oxide families. The resulting structure-spectrum relationships not only reproduce known trends in the literature but also reveal unintuitive ones that are visually indiscernible in large datasets. The results suggest that the RankAAE methodology has great potential to assist researchers in interpreting complex scientific data, testing physical hypotheses, and revealing patterns that extend scientific insight.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Nanometer Scale Imaging to Develop Quantitative Descriptors of Bipolar Membrane Junction Structure

Swings in pH can be achieved by electrically polarizing a bipolar membrane (BPM) to drive water dissociation at the BPM junction for electrochemical conversion and separation processes. BPM junction design is critical to tailor performance for specific applications; however, characterization techniques capable of resolving the nanometer scale physical structure of the junction are limited. We present sample preparation, imaging, and analysis workflows that are adaptable to a variety of BPM junction architectures. Atomic force microscopy produces BPM junction images with nanometer scale lateral resolution for samples with and without a graphene oxide water dissociation catalyst in the junction. Subsequent image segmentation and analysis quantify line edge roughness and catalyst layer thickness as descriptors of junction structure. Comparison of pre- and post-electrodialysis junctions suggests electric field-induced alignment of catalyst particles during electrodialysis. This characterization workflow can inform manufacturing protocols, computational modeling, and failure mode analysis for next-generation BPMs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

E min : A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability

Predicting the synthesizability of a new molecule remains an unsolved challenge that chemists have long tackled with heuristic approaches. Here, in this study, we report a new method for predicting synthesizability using a simple yet accurate thermochemical descriptor. We introduce E min , the energy difference between a molecule and its lowest energy constitutional isomer, as a synthesizability predictor that is accurate, physically meaningful, and first-principles based. We apply E min to 134,000 molecules in the QM9 data set and find that E min is accurate when used alone and reduces incorrect predictions of "synthesizable" by up to 52% when used to augment commonly used prediction methods. Our work illustrates how first-principles thermochemistry and heuristic approximations for molecular stability are complementary, opening a new direction for synthesizability prediction methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1990

Segmentation, modeling and classification of the compact objects in a pile

The problem of interpreting dense range images obtained from the scene of a heap of man-made objects is discussed. A range image interpretation system consisting of segmentation, modeling, verification, and classification procedures is described. First, the range image is segmented into regions and reasoning is done about the physical support of these regions. Second, for each region several possible three-dimensional interpretations are made based on various scenarios of the objects physical support. Finally each interpretation is tested against the data for its consistency. The superquadric model is selected as the three-dimensional shape descriptor, plus tapering deformations along the major axis. Experimental results obtained from some complex range images of mail pieces are reported to demonstrate the soundness and the robustness of our approach.

Gupta, Alok↗