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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 199 records · Page 11
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CFD Predictions of Boiling Regime Transitions during Line Chilldown validated against a 1G LN2 Experiment

Introduction Before filling a propellant tank on the ground or in Space, the transfer line between the donor and receiver tanks must be cooled down preferably by sacrificing a minimum amount of the cryogenic fluid. The cryogenic line chill-down process involves a transition between different flow boiling regimes, namely, film boiling, transition film boiling, and nucleate boiling which are complex and may be quite gravity-dependent. Capturing these boiling phenomena and predicting the transition between them in a CFD framework is new and challenging both for 1g and microgravity applications. Materials & Methods The present work addresses this challenge by employing a two-phase Eulerian approach in the context of a homogeneous fluid mixture together with the Lee phase change model to capture the film boiling regime of the chill-down process using ANSYS Fluent®. The nucleate boiling regime is predicted by incorporating an in-house developed sub-grid model that accounts for bubble nucleation, bubble growth, bubble departure diameter, and their shedding frequency. The sub-grid model is encoded and implemented into Fluent via a user-defined function for the wall-fluid heat flux calculations. The mathematical formulation and numerical implementation of the CFD model are described in detail. The coupled CFD-Subgrid model is validated against published experimental data for liquid nitrogen chill-down of a heated stainless-steel pipe in 1g. Results Numerical simulation results show good agreements between the CFD predictions of the wall temperature evolution, rewetting temperature, and transition between film and nucleate boiling, with the experimental measurements published by Darr et al [2] for several different LN2 flowrates in the vertical pipe orientation. The CFD predictions for the wall temperature distribution indicate a rapid quenching of the wall at two upstream and downstream temperature sensing locations as compared to the experimental measurement. The only tuning parameter in the CFD model is the Lee mass transfer coefficient. The CFD Model predicts the Liedenfrost rewetting temperature in close agreement with the experiment. This marks a transition between stable and transitionary flow boiling regimes. The CFD-predicted boiling curve for the downstream sensor location is also compared against its experimental counterpart and indicates that the model is able to predict all the key temperature and heat flux parameters during the transitions from stable to transitionary film boiling to nucleate boiling in close agreement with the experiment. A sequence of predicted volume fraction, and temperature contours depicting these transitions will be presented.

Evaporation Condensation↗
DOE OSTI2025

An NV − center in magnesium oxide as a spin qubit for hybrid quantum technologies

Recent predictions suggest that oxides, such as MgO and CaO, could serve as hosts of spin defects with long coherence times and thus be promising materials for quantum applications. However, in most cases, specific defects have not yet been identified. Here, by using a high-throughput first-principles framework and advanced electronic structure methods, we identify a negatively charged complex between a nitrogen interstitial and a magnesium vacancy in MgO with favorable electronic and optical properties for hybrid quantum technologies. We show that this NV − center has stable triplet ground and excited states, with singlet shelving states enabling optical initialization and spin-dependent readout. We predict several properties, including absorption, emission, and zero-phonon line energies, as well as zero-field splitting tensor, and hyperfine interaction parameters, which can aid in the experimental identification of this defect. Our calculations show that due to a strong pseudo-Jahn Teller effect and low-frequency phonon modes, the NV − center in MgO is subject to a substantial vibronic coupling. We discuss design strategies to reduce such coupling and increase the Debye-Waller factor, including the effect of strain and the localization of the defect states. We propose that the favorable properties of the NV − defect, along with the technological maturity of MgO, could enable hybrid classical-quantum applications, such as spintronic quantum sensors and single qubit gates.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Bridging microscopic dynamics and rheology in the yielding of charged colloidal suspensions

The yielding of soft materials is critical to many natural and industrial processes, yet experimental insights into microscopic aspects of yielding are limited. This study combines angle X-ray scattering, X-ray photon correlation spectroscopy, and in situ rheology (Rheo-SAXS-XPCS) with fast lubrication dynamics simulations to examine how interparticle interactions influence yielding in charged colloidal suspensions. By tuning attraction through salt addition, we compare repulsive and attractive systems under deformation. Repulsive suspensions yield uniformly with Andrade-like creep and minimal structural change. In contrast, attractive suspensions show complex behaviors, including shear banding, delayed yielding, and resolidification, governed by transient dynamics at shear band interfaces. These results directly link microscopic particle dynamics to macroscopic flow and demonstrate how interaction potentials control rheological behavior. This work offers a framework for designing soft materials with tailored properties for applications in coatings, food processing, drug delivery, and other technologies requiring precise mechanical control.

Molecular Dynamics Simulation↗
DOE OSTI2023

ZeoNet: 3D convolutional neural networks for predicting adsorption in nanoporous zeolites

Zeolites are one of the most widely used materials in the chemical industry due to their nanometer-sized pores that can adsorb and react upon molecules selectively. With hundreds of known framework topologies and hundreds of thousands of computationally predicted structures, the ability to rapidly predict zeolite performance allows researchers to prioritize their efforts on the most promising structures for a given application. Although the accuracy of forcefield-based atomistic simulations has advanced significantly in the past two decades, these simulations can be computationally expensive, especially for long-chain, complex molecules. Here, we present ZeoNet, a representation learning framework using convolutional neural networks (ConvNets) and 3D volumetric representations for predicting adsorption in zeolites. ZeoNet was trained on the task of predicting Henry's constants for adsorption, k H , of n-octadecane in more than 330 000 known and predicted zeolite materials. Employing a 3D grid based on the distances to solvent-accessible surfaces, a volumetric representation that can be generated efficiently, the best-performing ZeoNet achieved a correlation coefficient r 2 = 0.977 and a mean-squared error MSE = 3.8 in ln k H , which corresponds to an error of 9.3 kJ mol -1 in adsorption free energy. In comparison, a model based on hand-designed geometric features has values of r 2 = 0.783 and MSE = 35.7. ZeoNet is also relatively efficient and can process ≈8 structures per second on an Nvidia RTX 2080TI GPU, orders of magnitude faster than forcefield-based simulations. A systematic analysis was conducted to investigate how the choice of ConvNet architectures, the linear dimension (L) and spatial resolution (Δd) of the distance grids, batch size, optimizer, and learning rate impact the model performance. We found that ConvNets based on the ResNet architecture offer the best tradeoff between expressiveness and efficiency. The performance for all models reaches a plateau at L = 30–45 Å and depends less sensitively on grid resolution, with a small benefit around Δd = 0.30–0.45 Å. Finally, saliency maps were visualized to identify which regions of the materials contributed the most to model predictions. It was found, interestingly, that the predictions are driven primarily by the accessible pore volume rather than the region occupied by the framework atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Programmable Photoluminescence via Intrinsic and DNA-Fluorophore Association in a Mixed Cluster Heterometallic MOF

In this report a rapid and facile design strategy to create a highly complex optical tag with programmable, multimodal photoluminescent properties is described. This was achieved via intrinsic and DNA-fluorophore hidden signatures. As a first covert feature of the tag, an intricate novel heterometallic near-infrared (NIR)-emitting mesoporous metal–organic framework (MOF) was designed and synthesized. The material is constructed from two chemically distinct, homometallic hexanuclear clusters based on Nd and Yb. Uniquely, the Nd-based cluster is observed here for the first time in a MOF and consists of two staggered Nd μ3-oxo trimers. To generate controlled, multimodal, and tailorable emission with difficult to counterfeit features, the NIR-emissive MOF was post-synthetically modified via a fluorescent DNA oligo labeling design strategy. The surface attachment of several distinct fluorophores, including the simultaneous attachment of up to three distinct fluorescently labeled oligos was achieved, with excitation and emission properties across the visible spectrum (480-800 nm). The DNA inclusion as a secondary covert element in the tag was demonstrated via the detection of SYBR Gold dye association. Importantly, the approach implemented here serves as a rapid and tailorable way to encrypt distinct information in a facile and modular fashion and provides an innovative technology in the quest toward complex optical tags.

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

Facile microwave synthesis of zirconium metal-organic framework thin films on gold and silicon and application to sensor functionalization

Zirconium-based metal-organic frameworks, including UiO-66 and related frameworks, have become the focus of considerable research in the area of chemical warfare agent (CWA) decontamination. However, little work has been reported exploring these metal-organic frameworks (MOFs) for CWA sensing applications. For many sensing approaches, the growth of high-quality thin films of the active material is required, and thin film growth methods must be compatible with complex device architectures. Several approaches to synthesize thin films of UiO-66 have been described but many of these existing methods are complex or time consuming. In this work, we describe the development of a simple and rapid microwave assisted synthesis of oriented UiO-66 thin films on unmodified silicon (Si) and gold (Au) substrates. Thin films of UiO-66 and UiO-66-NH 2 can be grown in as little as 2 min on gold substrates and 30 min on Si substrates. The film morphology and orientation are characterized and the effects of reaction time and temperature on thin film growth on Au are investigated. Both reaction time and temperature impact the overgrowth of protruding discrete crystallites in the thin film layer but, surprisingly, no strong correlation is observed between film thickness and reaction time or temperature. We also briefly describe the synthesis of Zr/Ce solid solution thin films of UiO-66 on Au and report the first synthesis of a solid solution thin film MOF. Finally, we demonstrate the utility of the microwave method for the facile functionalization of two sensor architectures, plasmonic nanohole arrays and microresonators, with UiO-66 thin films.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys

Abstract The design of materials and identification of optimal processing parameters constitute a complex and challenging task, necessitating efficient utilization of available data. Bayesian Optimization (BO) has gained popularity in materials design due to its ability to work with minimal data. However, many BO-based frameworks predominantly rely on statistical information, in the form of input-output data, and assume black-box objective functions. In practice, designers often possess knowledge of the underlying physical laws governing a material system, rendering the objective function not entirely black-box, as some information is partially observable. In this study, we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process. We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO. The applicability of this approach is showcased through the design of NiTi shape memory alloys, where the optimal processing parameters are identified to maximize the transformation temperature.

Chemistry↗
DOE OSTI2024

Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials

Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A machine learning framework for elastic constants predictions in multi-principal element alloys

On the one hand, multi-principal element alloys (MPEAs) have created a paradigm shift in alloy design due to large compositional space, whereas on the other, they have presented enormous computational challenges for theory-based materials design, especially density functional theory (DFT), which is inherently computationally expensive even for traditional dilute alloys. In this paper, we present a machine learning framework, namely PREDICT (PRedict properties from Existing Database In Complex alloys Territory), that opens a pathway to predict elastic constants in large compositional space with little computational expense. The framework only relies on the DFT database of binary alloys and predicts Voigt–Reuss–Hill Young’s modulus, shear modulus, bulk modulus, elastic constants, and Poisson’s ratio in MPEAs. We show that the key descriptors of elastic constants are the A–B bond length and cohesive energy. The framework can predict elastic constants in hypothetical compositions as long as the constituent elements are present in the database, thereby enabling property exploration in multi-compositional systems. We illustrate predictions in a FCC Ni-Cu-Au-Pd-Pt system.

Linton, Nathan (ORCID:0000000315485613)↗
DOE OSTI2023

A Continuous Action Space Tree search for INverse desiGn (CASTING) framework for materials discovery

Abstract Material properties share an intrinsic relationship with their structural attributes, making inverse design approaches crucial for discovering new materials with desired functionalities. Reinforcement Learning (RL) approaches are emerging as powerful inverse design tools, often functioning in discrete action spaces. This constrains their application in materials design problems, which involve continuous search spaces. Here, we introduce an RL-based framework CASTING (Continuous Action Space Tree Search for inverse design), that employs a decision tree-based Monte Carlo Tree Search (MCTS) algorithm with continuous space adaptation through modified policies and sampling. Using representative examples like Silver (Ag) for metals, Carbon (C) for covalent systems, and multicomponent systems such as graphane, boron nitride, and complex correlated oxides, we showcase its accuracy, convergence speed, and scalability in materials discovery and design. Furthermore, with the inverse design of super-hard Carbon phases, we demonstrate CASTING’s utility in discovering metastable phases tailored to user-defined target properties and preferences.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effect of the electronic charge gap on LO bond-stretching phonons in undoped La 2 CuO 4 calculated using LDA + U

Typical density-functional theory calculations that wrongly predict undoped cuprates to be metallic also predict Cu-O half- and full-breathing phonon energies that are significantly softer than observed, presumably because of weak on-site Coulomb repulsion on the Cu 3d orbitals. We used DFT+U calculations with antiferromagnetic supercells of La 2 CuO 4 to establish correlation between the on-site repulsion strength, tuned via adjusting the value of U, and phonon dispersions. Here, we find that breathing and half-breathing phonons reach experimental values when U is tuned to obtain the correct optical gap and magnetic moments. We demonstrate that using distorted supercells within DFT+U is a promising framework to model phonons in undoped cuprates and other perovskite oxides with complex, interrelated structural and electronic degrees of freedom.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Materials representation and transfer learning for multi-property prediction

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements as well as the relationships among multiple properties to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates: (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 three-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data are available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with transfer learning [H-CLMP(T)] wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well suited for multi-target regression across the physical sciences.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Refining T c Prediction in Hydrides via Symbolic‐Regression‐Enhanced Electron‐Localization‐Function‐Based Descriptors

Hydrogen‐based materials are able to possess extremely high superconducting critical temperatures, T c s , due to hydrogen's low atomic mass and strong electron–phonon interaction. Recently, a descriptor based on the Electron Localization Function (ELF) has enabled the rapid estimation of the T c of hydrogen‐containing compounds from electronic networking properties, but its applicability has been limited by the small size and homogeneity of the training dataset used. Herein, the model is re‐examined, compiling a publicly available combined dataset of 244 binary and ternary hydride superconductors. The analysis shows that though ELF‐based networking remains a valuable descriptor, its predictive power declines with increasing compositional complexity. However, by introducing the molecularity index, defined as the highest value of the ELF at which two hydrogen atoms connect, and applying symbolic regression, the accuracy of the predictions can be substantially enhanced. These results establish a more robust framework for assessing superconductivity in hydride materials, facilitating accelerated screening of novel candidates through integration with crystal structure prediction methods or high‐throughput searches.

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

Phase Stability and Kinetics of Topotactic Dual Ca 2+ –Na + Ion Electrochemistry in NaSICON NaV 2 (PO 4 ) 3

Recent reports of reversible calcium plating and stripping have rekindled interest in the development of Ca-ion batteries (CIBs) as next-generation energy storage devices. This technology has the potential to overcome the limitations of conventional Li-ion batteries, but CIBs are plagued by a paucity of suitable cathode materials. To date, NaSICON-structured NaV 2 (PO 4 ) 3 has been demonstrated as a successful cathode candidate, exhibiting reversible (de)intercalation of 0.6 mol Ca 2+ along with stable cycling performance. However, a complex multiphase mixture forms on discharge so the Ca-ion charge storage mechanism in the NaSICON framework is poorly understood. Here in this work, we report on an investigation of the structure and/or Na + /Ca 2+ environment(s) of a variety of chemically prepared NaSICON Ca x Na y V 2 (PO 4 ) 3 phases which were characterized using synchrotron XRD, SEM-EDS, 23 Na NMR, and TEM. Highly calciated CaV 2 (PO 4 ) 3 , Ca 1.5 V 2 (PO 4 ) 3 , and CaNaV 2 (PO 4 ) 3 phases can be prepared at high temperature, but -unlike Ca 0.6 NaV 2 (PO 4 ) 3 -these materials are electrochemically inactive. To better understand the fundamental factors impacting successful Ca 2+ electrochemistry in this system, DFT was employed to examine the Ca x Na y V 2 (PO 4 ) 3 phase diagram and Ca 2+ diffusion mechanism. Theoretical insights show that phase separation into Na-rich and Ca-rich phases is a reason for the capacity limitation and demonstrate that Na + ions in the host materials assist the migration of neighboring Ca 2+ ions, enabling reversible electrochemistry in Ca x Na y V 2 (PO 4 ) 3 . This investigation of fundamental principles affecting reversible Ca 2+ (de)intercalation in Ca x Na y V 2 (PO 4 ) 3 allows for the development of design principles to enable the discovery of a variety of successful cathodes for CIBs.

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

Uncovering obscured phonon dynamics from powder inelastic neutron scattering using machine learning

The study of phonon dynamics is pivotal for understanding material properties, yet it faces challenges due to the irreversible information loss inherent in powder inelastic neutron scattering spectra and the limitations of traditional analysis methods. In this study, we present a machine learning framework designed to reveal obscured phonon dynamics from powder spectra. Using a variational autoencoder, we obtain a disentangled latent representation of spectra and successfully extract force constants for reconstructing phonon dispersions. Notably, our model demonstrates effective applicability to experimental data even when trained exclusively on physics-based simulations. The fine-tuning with experimental spectra further mitigates issues arising from domain shift. Analysis of latent space underscores the model’s versatility and generalizability, affirming its suitability for complex system applications. Furthermore, our framework’s two-stage design is promising for developing a universal pre-trained feature extractor. This approach has the potential to revolutionize neutron measurements of phonon dynamics, offering researchers a potent tool to decipher intricate spectra and gain valuable insights into the intrinsic physics of materials.

domain adaptation↗
DOE OSTI2025

Micropolar deep material network

This study extends the Deep Material Network (DMN), a physics-informed machine learning framework, to predict the homogenized mechanical response of composite materials with micropolar (Cosserat-type) constitutive behavior. This extension incorporates microstructure-dependent size effects, enabling accurate, efficient, and size-aware predictions for composites with complex internal architectures. While traditional, direct numerical simulation micropolar models effectively capture size effects by introducing extra local degrees of freedom, they bring significant computational challenges, particularly for multiscale analyses relevant to engineering applications. The micropolar DMN developed in this paper achieves high accuracy while significantly reducing computation time compared to micropolar direct numerical simulations. This advancement enables multiscale analyses and parameter studies that were previously impractical, such as high-cycle fatigue simulations and comprehensive investigations of internal length scale effects notably in size-dependent plastic response and the optimization of lattice structures. By uniting microstructure-sensitive modeling, physics-driven learning, and scalable surrogate modeling, the micropolar DMN paves the way for accelerated material design, large-scale parametric studies, and the reliable incorporation of size-dependent effects across a wide range of engineering applications, including optimization and next-generation composite design.

36 MATERIALS SCIENCE↗
NASA NTRS1973

Gravitation

This textbook on gravitation physics (Einstein's general relativity or geometrodynamics) is designed for a rigorous full-year course at the graduate level. The material is presented in two parallel tracks in an attempt to divide key physical ideas from more complex enrichment material to be selected at the discretion of the reader or teacher. The full book is intended to provide competence relative to the laws of physics in flat space-time, Einstein's geometric framework for physics, applications with pulsars and neutron stars, cosmology, the Schwarzschild geometry and gravitational collapse, gravitational waves, experimental tests of Einstein's theory, and mathematical concepts of differential geometry.

Misner, C. W.↗
DOE OSTI2020

A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts

Abstract For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy. In this article, we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗