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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Machine Learning Predictions of Simulated Self-Diffusion Coefficients for Bulk and Confined Pure Liquids

Diffusion properties of bulk fluids have been predicted using empirical expressions and machine learning (ML) models, suggesting that predictions of diffusion also should be possible for fluids in confined environments. The ability to quickly and accurately predict diffusion in porous materials would enable new discoveries and spur development in relevant technologies such as separations, catalysis, batteries, and subsurface applications. Here in this work, we apply artificial neural network (ANN) models to predict the simulated self-diffusion coefficients of real liquids in both bulk and pore environments. The training data sets were generated from molecular dynamics (MD) simulations of Lennard-Jones particles representing a diverse set of 14 molecules ranging from ammonia to dodecane over a range of liquid pressures and temperatures. Planar, cylindrical, and hexagonal pore models consisted of walls composed of carbon atoms. Our simple model for these liquids was primarily used to generate ANN training data, but the simulated self-diffusion coefficients of bulk liquids show excellent agreement with experimental diffusion coefficients. ANN models based on simple descriptors accurately reproduced the MD diffusion data for both bulk and confined liquids, including the trend of increased mobility in large pores relative to the corresponding bulk liquid.

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

Paradigms of frustration in superionic solid electrolytes

Superionic solid electrolytes have widespread use in energy devices, but the fundamental motivations for fast ion conduction are often elusive. In this Perspective, we draw upon atomistic simulations of a wide range of superionic conductors to illustrate some ways frustration can lower diffusion cation barriers in solids. Based on our studies of halides, oxides, sulfides and hydroborates and a survey of published reports, we classify three types of frustration that create competition between different local atomic preferences, thereby flattening the diffusive energy landscape. These include chemical frustration, which derives from competing factors in the anion–cation interaction; structural frustration, which arises from lattice arrangements that induce site distortion or prevent cation ordering; and dynamical frustration, which is associated with temporary fluctuations in the energy landscape due to anion reorientation or cation reconfiguration. For each class of frustration, we provide detailed simulation analyses of various materials to show how ion mobility is facilitated, resulting in stabilizing factors that are both entropic and enthalpic in origin. We propose the use of these categories as a general construct for classifying frustration in superionic conductors and discuss implications for future development of suitable descriptors and improvement strategies.

25 ENERGY STORAGE↗
DOE OSTI2022

Experimental discovery of structure–property relationships in ferroelectric materials via active learning

Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these functionalities have been discovered and quantified via local scanning probe microscopy methods. However, the search has until now been based on either trial and error, or using auxiliary information such as the topography or domain wall structure to identify potential objects of interest on the basis of the intuition of operator or pre-existing hypotheses, with subsequent manual exploration. Here we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization-switching characteristics in ferroelectric materials encoded in the hysteresis loop. The hysteresis loops and their scalar descriptors such as nucleation bias, coercive bias and the hysteresis loop area (or more complex functionals of hysteresis loop shape) and corresponding uncertainties are used to guide the discovery of these relationships via automated piezoresponse force microscopy and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high-dimensional data, as well as human-based physics insights encoded into the acquisition function. For ferroelectric materials, this automated workflow demonstrates that the discovery path and sampling points of on- and off-field hysteresis loops are largely different, indicating that on- and off-field hysteresis loops are dominated by different mechanisms. Here, the proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.

36 MATERIALS SCIENCE↗
NASA NTRS1981

Characteristics of flight simulator visual systems

The physical parameters of the flight simulator visual system that characterize the system and determine its fidelity are identified and defined. The characteristics of visual simulation systems are discussed in terms of the basic categories of spatial, energy, and temporal properties corresponding to the three fundamental quantities of length, mass, and time. Each of these parameters are further addressed in relation to its effect, its appropriate units or descriptors, methods of measurement, and its use or importance to image quality.

Statler, I. C.↗
DOE OSTI2022

A Stochastic Reduced-Order Model for Statistical Microstructure Descriptors Evolution

Integrated computational materials engineering (ICME) models have been a crucial building block for modern materials development, relieving heavy reliance on experiments and significantly accelerating the materials design process. However, ICME models are also computationally expensive, particularly with respect to time integration for dynamics, which hinders the ability to study statistical ensembles and thermodynamic properties of large systems for long time scales. To alleviate the computational bottleneck, we propose to model the evolution of statistical microstructure descriptors as a continuous-time stochastic process using a non-linear Langevin equation, where the probability density function (PDF) of the statistical microstructure descriptors, which are also the quantities of interests (QoIs), is modeled by the Fokker–Planck equation. In this work, we discuss how to calibrate the drift and diffusion terms of the Fokker–Planck equation from the theoretical and computational perspectives. The calibrated Fokker–Planck equation can be used as a stochastic reduced-order model to simulate the microstructure evolution of statistical microstructure descriptors PDF. Considering statistical microstructure descriptors in the microstructure evolution as QoIs, we demonstrate our proposed methodology in three integrated computational materials engineering (ICME) models: kinetic Monte Carlo, phase field, and molecular dynamics simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Phase Selection Rules of Multi‐Principal Element Alloys

Abstract Computational prediction of phase stability of multi‐principal element alloys (MPEAs) holds a lot of promise for rapid exploration of the enormous design space and autonomous discovery of superior structural and functional properties. Regardless of many plausible works that rely on phenomenological theory and machine learning, precise prediction is still limited by insufficient data and the lack of interpretability of some machine learning algorithms, e.g., convolutional neural network. In this work, a comprehensive approach is presented, encompassing the development of a complete dataset that contains 72 387 density functional theory calculations, as well as a predictive global phenomenological descriptor. The phase selection descriptor, based on atomic electronegativity and valence electron concentration, significantly outperforms the widely used valence electron concentration, excelling in both accuracy (with an f1 score of 63% compared to 47%) and its ability to predict the HCP phase (0.48 recall compared to 0). The comprehensive data mining on the global design space of 61 425 quaternary MPEAs made from 28 possible metals, together with the phenomenological theory and physical interpretation, will set up a solid computational science foundation for data‐driven exploration of MPEAs.

Chemistry↗
DOE OSTI2024

Is the Local Ion Density Sufficient to Drive NaCl Nucleation from the Melt and Aqueous Solution?

Even though nucleation is ubiquitous in different science and engineering problems, investigating nucleation is extremely difficult due to the complicated ranges of time and length scales involved. In this work, we simulate NaCl nucleation in both molten and aqueous environments using enhanced sampling of all-atom molecular dynamics with deep-learning-based estimation of reaction coordinates. By incorporating various structural order parameters and learning the reaction coordinate as a function thereof, we achieve significantly improved sampling relative to traditional ad hoc descriptions of what drives nucleation, particularly in an aqueous medium. Our results reveal a one-step nucleation mechanism in both environments, with reaction coordinate analysis highlighting the importance of local ion density in distinguishing solid and liquid states. However, although fluctuations in the local ion density are necessary to drive nucleation, they are not sufficient. Our analysis shows that near the transition states, descriptors such as enthalpy and local structure become crucial. Furthermore, our protocol proposed here enables robust nucleation analysis and phase sampling and could offer insights into nucleation mechanisms for generic small molecules in different environments.

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

The impact of G-quadruplex dynamics on inter-tetrad electronic couplings: a hybrid computational study

The G-quadruplex is a fascinating nucleic acid motif with implications in biology, medicine, and nanotechnologies. G-quadruplexes can form in the telomeres at the edges of chromosomes and in other guanine-rich regions of the genome. They can also be engineered for exploitation as biological materials for nanodevices. Their higher stiffness and higher charge transfer rates make them better candidates in nanodevices than duplex DNA. For the development of molecular nanowires, it is important to optimize electron transport along the wire axis. One powerful basis to do so is by manipulating the structure, based on known effects that structural changes have on electron transport. Here, for this work, we investigate such effects, by a combination of classical simulations of the structure and dynamics and quantum calculations of electronic couplings. We find that this structure–function relationship is complex. A single helix shape parameter alone does not embody such complexity, but rather a combination of distances and angles between stacked bases influences charge transfer efficiency. By analyzing linear combinations of shape descriptors for different topologies, we identify the structural features that most affect charge transfer efficiency. We discuss the transferability of the proposed model and the limiting effects of inherent flexibility.

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

Rapid macrovoid characterization in membranes prepared via nonsolvent-induced phase separation: A comparison between 2D and 3D techniques

Optimizing the performance of asymmetric membranes prepared via nonsolvent-induced phase separation (NIPS) requires a quantitative understanding of how processing variables influence membrane morphology. Presently, the most useful structural quantification techniques require 3D visualization of the membrane structure and are best suited for studies seeking detailed information on small datasets. This study proposes and validates a rapid and accurate technique for quantifying macroporosity (i.e., D m ), a simple descriptor of sublayer macrovoid content in asymmetric membranes D m . values measured from segmented cross-sectional imaging performed via X-ray computed tomography (XCT) and scanning electron microscopy (SEM) are presented and compared for three asymmetric membranes prepared from commercial polymers. Importantly, analyses of 3D XCT membrane reconstructions reveal that D m is described by a single, centralized mean, which demonstrates that macrovoid content is spatially homogenous. Thus, D m can be approximated from limited sampling of the 2D cross-sectional membrane structure via SEM. A proposed 2D SEM sampling method provides D m estimates within ±6% of corresponding 3D XCT values with 30 independent measurements for the three membranes. Further sensitivity is achieved using complementary descriptors such as macrovoid count density (i.e., C m ). This technique is thus a useful tool for characterizing macroporosity from a broad selection of membrane samples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Decoherence in Molecular Electron Spin Qubits: Insights from Quantum Many-Body Simulations

Quantum states are described by wave functions whose phases cannot be directly measured, but which play a vital role in quantum effects such as interference and entanglement. The loss of the relative phase information, termed decoherence, arises from the interactions between a quantum system and its environment. Decoherence is perhaps the biggest obstacle on the path to reliable quantum computing. Here we show that decoherence occurs even in an isolated molecule although not all phase information is lost via a theoretical study of a central electron spin qubit interacting with nearby nuclear spins in prototypical magnetic molecules. The residual coherence, which is molecule-dependent, provides a microscopic rationalization for the nuclear spin diffusion barrier proposed to explain experiments. The contribution of nearby molecules to the decoherence has a non-trivial dependence on separation, peaking at intermediate distances. Molecules that are far away only affect the long-time behavior. Because the residual coherence is simple to calculate and correlates well with the coherence time, it can be used as a descriptor for coherence in magnetic molecules. This work will help establish design principles for enhancing coherence in molecular spin qubits and serve to motivate further theoretical work

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data

The dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D fast Fourier transforms, correlation, and pair distribution functions are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. Here, the deep learning (DL)-based workflow is developed to analyze detailed particle dynamics and explore the evolution of local geometries. Finally, we use a combination of DL feature extraction and mixture modeling to define particle neighborhoods free of physics constraints, allowing for a separation of possible classes of particle behavior and identification of the associated transitions. Overall, this work establishes the workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Probing the Kinetic Origin of Varying Oxidative Stability of Ethyl- vs. Propyl-spaced Amines for Direct Air Capture

Amine-based adsorbents are promising for direct air capture of CO 2 , yet oxidative degradation remains a key unmitigated risk hindering wide-scale deployment. Borrowing wisdom from the basic auto-oxidation scheme, insights are gained into the underlying degradation mechanisms of polyamines by quantum chemical, advanced sampling simulations, adsorbent synthesis, and accelerated degradation experiments. The reaction kinetics of polyamines are contrasted with that of typical aliphatic polymers and they elucidate for the first time the critical role of aminoalkyl hydroperoxide decomposition in the oxidative degradation of amino-oligomers. The experimentally observed variation in oxidative stability of polyamines with different backbone structures is explained by the relationship between the local chemical structure and the free energy barrier of aminoalkyl hydroperoxide decomposition, suggesting that its energetics can be used as a descriptor to screen and design new polyamines with improved stability. In conclusion, the developed computational capability sheds light on radical-induced degradation chemistry of other organic functional materials.

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

Machine learned features from density of states for accurate adsorption energy prediction

Materials databases generated by high-throughput computational screening, typically using density functional theory (DFT), have become valuable resources for discovering new heterogeneous catalysts, though the computational cost associated with generating them presents a crucial roadblock. Hence there is a significant demand for developing descriptors or features, in lieu of DFT, to accurately predict catalytic properties, such as adsorption energies. Here, we demonstrate an approach to predict energies using a convolutional neural network-based machine learning model to automatically obtain key features from the electronic density of states (DOS). The model, DOSnet, is evaluated for a diverse set of adsorbates and surfaces, yielding a mean absolute error on the order of 0.1 eV. In addition, DOSnet can provide physically meaningful predictions and insights by predicting responses to external perturbations to the electronic structure without additional DFT calculations, paving the way for the accelerated discovery of materials and catalysts by exploration of the electronic space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Descriptors of water aggregation

For this work, we rely on a total of 23 (cluster size, 8 structural, and 14 connectivity) descriptors to investigate structural patterns and connectivity motifs associated with water cluster aggregation. In addition to the cluster size n (number of molecules), the 8 structural descriptors can be further categorized into (i) one-body (intramolecular): covalent OH bond length (r OH ) and HOH bond angle (θ HOH ), (ii) two-body: OO distance (r OO ), OHO angle (θ OHO ), and HOOX dihedral angle ($\phi$ HOOX ), where X lies on the bisector of the HOH angle, (iii) three-body: OOO angle (θ OOO ), and (iv) many-body: modified tetrahedral order parameter (q) to account for two-, three-, four-, five-coordinated molecules (q m , m = 2, 3, 4, 5) and radius of gyration (R g ). The 14 connectivity descriptors are all many-body in nature and consist of the AD, AAD, ADD, AADD, AAAD, AAADD adjacencies [number of hydrogen bonds accepted (A) and donated (D) by each water molecule], Wiener index, Average Shortest Path Length, hydrogen bond saturation (% HB), and number of non-short-circuited three-membered cycles, four-membered cycles, five-membered cycles, six-membered cycles, and seven-membered cycles. We mined a previously reported database of 4 948 959 water cluster minima for (H 2 O) n , n = 3–25 to analyze the evolution and correlation of these descriptors for the clusters within 5 kcal/mol of the putative minima. It was found that r OH and % HB correlated strongly with cluster size n, which was identified as the strongest predictor of energetic stability. Marked changes in the adjacencies and cycle count were observed, lending insight into changes in the hydrogen bond network upon aggregation. A Principal Component Analysis (PCA) was employed to identify descriptor dependencies and group clusters into specific structural patterns across different cluster sizes. The results of this study inform our understanding of how water clusters evolve in size and what appropriate descriptors of their structural and connectivity patterns are with respect to system size, stability, and similarity. The approach described in this study is general and can be easily extended to other hydrogen-bonded systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Impact of intermolecular interactions on the spectroscopic signals, energetics, and redox behavior of high valent 237 Np (Final Report)

Our studies have led to definitive spectral assignments for neptunyl-neptunyl interactions in aqueous solutions that will be valuable for predicting Np(V) speciation in solution. We have definitively demonstrated how actinyl-hydrogen and actinyl-cation interactions can influence spectral features for uranyl and neptunyl system. In addition, we have developed DFT methodologies to predict enthalpies of formation for uranyl and neptunyl systems and linked stability to chemical descriptors (hydrogen interaction energies or the electrostatic attraction energies). The counterion influenced the rate of Np(V) oxidation to Np(VI) neptunates and [Np(VII)O 4 OH] 3- complexes. Oxidation to Np(VII) during ozonolysis takes place through the presence of hydroxyl radicals.

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

Selective Valorization of CO 2 towards Valuable Hydrocarbons through Methanol-mediated Tandem Catalysis

The thermocatalytic conversion of carbon dioxide (CO 2 ) into valuable hydrocarbons presents a promising solution for mitigating anthropogenic CO 2 emissions while producing drop-in replacements for fossil-derived products. Among the two tandem mechanisms for the direct hydrogenation of CO 2 , the methanol synthesis coupled with the methanol to hydrocarbon (MTH) reaction offers an improvement over the reverse water-gas shift coupled with Fischer-Tropsch synthesis reaction (RWGS-FTS), as it is not limited by Anderson-Schulz-Flory distribution and yields higher selectivity towards specific products (e.g., olefins, aromatics, higher hydrocarbons). Herein, we focus on the recent progress achieved through this pathway and outline the existing knowledge gaps. We discuss the challenges involved in the process and highlight the key descriptors in the selection of catalyst components. Finally, we present several potential solutions to circumvent the current challenges, aiming to expedite the advancement of this route toward an efficient CO 2 hydrogenation process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

The role of atomic carbon in directing electrochemical CO (2) reduction to multicarbon products

Electrochemical reduction of carbon-dioxide/carbon-monoxide (CO (2) R) to fuels and chemicals presents an attractive approach for sustainable chemical synthesis, but it also poses a serious challenge in catalysis. Understanding the key aspects that guide CO (2) R towards value-added multicarbon (C 2+ ) products is imperative in designing an efficient catalyst. Herein, we identify the critical steps toward C 2 products on copper through a combination of energetics from density functional theory and micro-kinetic modeling. We elucidate the importance of atomic carbon in directing C 2+ selectivity and how it introduces surface structural sensitivity on copper catalysts. Overall, this insight enables us to propose two simple thermodynamic descriptors that effectively identify C 2+ selectivity on metal catalysts beyond copper and hence it defines an intelligible protocol to screen for materials that selectively catalyze CO (2) to C 2+ products.

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

Toward Rational Design of Supported Vanadia Catalysts of Lignin Conversion to Phenol

In sustainable chemical engineering, catalytic upgrading of lignocellulosic biomass has recently gained attention for producing renewable platform chemicals. To achieve maximal biomass utilization, upgrading the underutilized lignin components is essential. Among various catalysts for lignin upgrading, supported vanadia (V2O5) catalysts are promising because of their cost-effectiveness and tunability of either dopant metals or catalyst supports. Here, computational studies are conducted to derive rational design guidelines of supported V2O5 catalysts for accomplishing the high catalytic activity of lignin upgrading to phenol, a key compound for producing bioplastics and biofuel blendstocks. Guaiacol was used as the model compound since it comprises the highest portion of depolymerized lignin. Computational mechanistic studies for the catalytic guaiacol conversion to phenol were performed for the V2O5 catalysts on Titania (TiO2) and silica (SiO2) to explain higher experimental phenol yields on V2O5/SiO2 than V2O5/TiO2. The hydrogen migration from the methoxy group to the aryl ring was identified as a rate-determining step, and the overall activation energies on the two catalysts were compared. A structural analysis was carried out for the catalysts and rate-determining transition states to gain further insights from mechanistic studies. It was concluded that the tilt angle of the aryl group in the hydrogen migration transition state is a key descriptor determining the catalytic activity of phenol formation. These features correlate well with activation energies and experimental phenol yields, indicating that they provide design guidelines for supported metal catalysts for lignin upgrading before experiments.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗