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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A machine-learning-aided data recovery approach for predicting multi-material thermal behaviors in advanced test reactor capsules

Instrumented experiments conducted at test reactors are essential to the deployment of new advanced reactor systems. Designing new experiments and generating data on specific reactor conditions require significant investments in terms of both time and cost. Finite element analysis software can be used to create high-fidelity models of experiment environments in order to support the actual experiments, but computation time remains a concern in terms of applying outcomes to real-time usage of data (e.g., a digital twin [DT]). Here, the present research proposes a machine-learning (ML) aided approach to making temperature and displacement predictions based on the thickness of the outer gas gap on the experimental capsule used for in-pile demonstration of a novel new thermal conductivity probe in the Advanced Test Reactor (ATR). This capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. Gas gaps existed between the fuel and the rodlet, and between the inner and the outer capsule. The learning data pertained to an experimental capsule's radial distributions of temperature and displacement, as obtained based on Abaqus and the physical features. For the first step of ML sequence, the temperature was predicted using three positional parameters. Next, the displacement was predicted using seven additional parameters. Each physical feature was normalized in order to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement with the simulation results in all cases involving interpolation and extrapolation. Furthermore, data similarity enhancement increased the similarity between the training and the target data, thereby increasing the predictive accuracy of the ML models. In certain extrapolation cases involving limited original ML model accuracy, data similarity enhancement and data recovery was able to somewhat improve this accuracy.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

On the role of geometrically necessary dislocations in void formation and growth in response to shock loading conditions in wrought and additively manufactured Ta

This study investigates the role of geometrically necessary dislocations (GNDs) and microstructure on void nucleation and growth in wrought and additively manufactured (AM) tantalum subjected to high-strain rate loading. Multi-modal 3D data was collected using TriBeam tomography to calculate GND densities and their spatial relationship to voids. A microstructural comparison between the wrought and AM samples identified distinct void shapes and locations, with intragranular voids and more spherical voids frequently observed in the AM dataset. Results indicate that voids preferentially form at both high-angle grain boundaries and low-angle subgrain boundaries, the latter of which are frequently observed in the AM material. Through a radial distribution analysis of all voids in the datasets, significant GND localization to near-void-surface regions was observed in both samples. 3D crystal plasticity simulations were employed to extend the experimental observations, revealing higher void growth rates in [111] oriented grains when compared to [001] grains. The simulations also suggest that GNDs can be generated as part of the void growth process, with more GND accumulation for growth in a [111] grain than a [001] grain. These findings provide valuable insights into the links between nanoscale void nucleation, mesoscale void growth, and microstructural effects in dynamically loaded tantalum.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The effect of ion pairing on speciation and transport in ion exchange membranes at varying hydration levels: A four-state model

Understanding ion pairing in ion exchange membranes (IEMs) is essential for advancing IEM applications in energy and environmental technologies. Here, this study introduces a four-state molecular dynamics model to quantify speciation and transport within Nafion-117, specifically examining the role of ion pairing in monovalent and divalent counterions (NaCl, Na 2 SO 4 , and MgSO 4 ). By analyzing radial distribution functions (RDFs) and molecular snapshots, we distinguish ion pairing modes and classify counterions into four states: condensed counterion, condensed ion pair, free ion pair, and free counterion. A key finding is that while divalent counterions (e. g., Mg 2+ ) maintain stable speciation across hydration levels, monovalent counterions (e.g., Na + ) show notable speciation shifts with hydration. Both monovalent and divalent counterions are not diffusive when condensed onto the polymer (sorbed to membrane functional groups). In contrast, free counterions are diffusive across all hydration levels. To evaluate the overall diffusivity of counterions, four-state fractions and diffusivities are computed, each contributing to counterion transport. The condensed/free ion speciation for multivalent sulfate salts aligns with previous revisions to the Donnan-Manning framework that include ion pairing, thereby validating its relevance to established membrane theories. The four-state model's diffusivity results support several current ion exchange assumptions, including that the condensed counterions are immobile, while uncondensed counterions are mobile. The four-state model offers insights into contact ion pairing within IEMs, highlighting its potential even when undetected in aqueous solution experiments. This work advances the theoretical understanding of counterion speciation in IEMs while identifying model limitations that suggest avenues for refinement, such as distinguishing water-mediated ion pairs between fully hydrated ions.

Ion exchange membranes↗
DOE OSTI2021

Molecular dynamics simulations of a dicationic ionic liquid for CO 2 capture

A dicationic ionic liquid ([DBU-PEG][Tf 2 N] 2 ) was studied using classical molecular dynamics simulations to examine its structural and gas separation properties. The dication was designed in an attempt to improve CO 2 solubility by means of tuning the cation-anion interactions of the ionic liquid (IL). The computational model was compared to experimentally obtained density, viscosity, and powder X-ray diffraction spectra. The structure of the IL was further investigated with radial distribution functions and free volume analysis through cavity distributions. It was found that the shape and charge distribution of the dication enhances CO 2 interaction: the CO 2 molecule is hugged by the dication along the PEG linker and close to one of the cationic ends. Here, the geminal design of the dication allows for strong interaction with CO 2 , showing promise as a means of carbon capture.

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

Thermophysical properties of FLiBe using moment tensor potentials

Fluoride salts are prospective materials for applications in some next-generation nuclear reactors and their thermophysical properties at various conditions are of interest. Experimental measurement of the properties of these salts is often difficult and, in some cases, unfeasible due to challenges from high temperatures, impurity control, and corrosivity. Therefore, accurate theoretical methods are needed for fluoride salt property prediction. In this work, we used moment tensor potentials (MTP) to approximate the potential energy surface of eutectic FLiBe (66.6% LiF – 33.3% BeF2) predicted by the ab initio (DFT-D3) method. Here, we then used the developed potential and molecular dynamics to obtain several thermophysical properties of FLiBe, including radial distribution functions, density, self-diffusion coefficients, thermal expansion, specific heat capacity, bulk modulus, viscosity, and thermal conductivity. Our results show that the MTP potential approximates the potential energy surface accurately and the overall approach yields very good agreement with experimental values. The converged fitting can be obtained with less than 600 configurations generated from DFT calculations, which data can be generated in just 1200 core hours on today's typical processors. The MTP potential is faster than many machine learning potentials and about one order of magnitude slower than widely used empirical molten salt potentials such as Tosi/Fumi.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Jet shapes and fragmentation functions in Au+Au collisions at s NN = 200 GeV in STAR

The STAR Collaboration reports measurements of differential jet shapes and semi-inclusive jet fragmentation functions in Au+Au collisions at with the STAR detector at RHIC. Additionally, jet shapes, which represent the radial distribution of momentum carried by constituents, are measured differentially for (1) the charged particles transverse momentum and (2) the jet azimuthal angle relative to the second-order event plane. Based on the semi-inclusive population of jets recoiling from a high transverse momentum trigger hadron, jet fragmentation functions in 40-60% central heavy-ion collisions are measured, and compared to those in PYTHIA simulations for pp collisions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Chemical and Structural Alterations in the Amorphous Structure of Obsidian due to Nanolites

Obsidian is volcanic glass that results from the rapid cooling of silica-rich melt. Nanoscale crystallites precipitate out of the melt prior to solidification and remain embedded in the amorphous matrix. These crystallites provide information on the flow kinetics and composition of the melt. Due to the sparsity and size of nanolites, studies often focus on supramicron crystallites. This research takes advantage of the conchoidal fracture of obsidian by knapping samples with nanometer-thin edges for transmission electron microscopy characterization. Nanolites in the amorphous matrix are studied using energy-dispersive spectroscopy (EDS) and electron diffraction. Certain alkali and alkaline-earth cations exhibit patterns of depletion near Fe-oxide nanolites. EDS is used to identify nanolites and variations in the composition of the matrix. Parallel beam diffraction and radial distribution function analysis of nearest-neighbor distances determine average bond lengths in the matrix near nanolites, showing that nanolites influence the nearby short-range ordering and atomic character of the matrix. Analysis reveals decreased mean nearest-neighbor distances in the matrix adjacent to nanolites compared to the bulk. Our methods exhibit the required sensitivity to detect variations in the composition and structure near nanolites, and our findings indicate that obsidian nanolites contribute to quantifiable localized changes in the amorphous structure.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Minimal Experimental Bias on the Hydrogen Bond Greatly Improves Ab Initio Molecular Dynamics Simulations of Water

Experiment Directed Simulations (EDS) is a method within a class of techniques seeking to improve molecular simulations by minimally biasing the system Hamiltonian to reproduce certain experimental observables. In a previous application of EDS to ab initio molecular dynamics (AIMD) simulation based on electronic density functional theory (DFT), the AIMD simulations of water were biased to reproduce its experimentally derived solvation structure. In particular, by solely biasing the O-O pair correlation functions, other structural and dynamical properties that were not biased were improved. In this work, the hypothesis is tested that directly biasing the OH pair correlation (and hence the H-O∙∙∙H hydrogen bonding), will provide an even better improvement of DFT-based water properties in AIMD simulations. The logic behind this hypothesis is that for most electronic DFT descriptions of water the hydrogen bonding is known to be deficient due to anomalous charge transfer and over polarization in the DFT. Using recent advances to the EDS learning algorithm, we thus train a minimal bias on AIMD water that reproduces the O-H radial distribution function derived from the highly accurate MB-pol model of water. Finally, it is then confirmed that biasing the O-H pair correlation alone can lead to improved AIMD water properties, with structural and dynamical properties in even closer to experiment than the previous EDS-AIMD model.

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

Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment

Methodologies for training machine learning potentials (MLPs) with quantum-mechanical simulation data have recently seen tremendous progress. Experimental data have a very different character than simulated data, and most MLP training procedures cannot be easily adapted to incorporate both types of data into the training process. Here, we investigate a training procedure based on iterative Boltzmann inversion that produces a pair potential correction to an existing MLP using equilibrium radial distribution function data. By applying these corrections to an MLP for pure aluminum based on density functional theory, we observe that the resulting model largely addresses previous overstructuring in the melt phase. Interestingly, the corrected MLP also exhibits improved performance in predicting experimental diffusion constants, which are not included in the training procedure. The presented method does not require autodifferentiating through a molecular dynamics solver and does not make assumptions about the MLP architecture. Our results suggest a practical framework for incorporating experimental data into machine learning models to improve the accuracy of molecular dynamics simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Furthermore, fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

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

Relation between the Hydrated Electron Solvation Structure and Its Partial Molar Volume

It is now generally accepted that the hydrated electron occupies a cavity in water, but the size of the cavity and the arrangements of the solvating water molecules are not fully characterized. Here, we use the Kirkwood-Buff (KB) approach to examine how the partial molar volume (V M ) provides insight into these issues. The KB method relates V M to an integral of the electron-water radial distribution function, a key measure of the hydrated electron structure. Here we have applied it to three widely-used pseudopotentials and the results show that V M is a sensitive measure of the fidelity of hydrated electron descriptions. Thus, the measured V M places constraints on the hydrated electron structure that are important in developing and evaluating model descriptions. Importantly, we find that V M does not reflect only the cavity size (and thus should not be used to infer the cavity radius), but is strongly dependent on the extended solvation structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Modeling the Behavior of Complex Aqueous Electrolytes Using Machine Learning Interatomic Potentials: The Case of Sodium Sulfate

Understanding the structure and thermodynamics of solvated ions is essential for advancing applications in electrochemistry, water treatment, and energy storage. While ab initio molecular dynamics methods are highly accurate, they are limited by short accessible time and length scales whereas classical force fields struggle with accuracy. Herein, we explore the structure and thermodynamics of complex monovalent-divalent ion pairs using Na 2 SO 4 (aq) as a case study by applying a machine learning interatomic potential (MLIP) trained on density functional theory (DFT) data. Our MLIP-based approach reproduces key bulk properties such as density and radial distribution functions of water. We provide the hydration structure of the sodium and sulfate ions in the 0.1–2 M concentration range and the one-dimensional and two-dimensional potentials of mean force for the sodium–sulfate ion pairing at the low concentration limit (0.1 M), which are inaccessible to DFT. At low concentrations, the sulfate ion is strongly solvated, leading to the stabilization of solvent-separated ion pairs over contact ion pairs. Minimum energy pathway analysis revealed that coordinating two sodium ions with a sulfate ion is a multistep process whereby the sodium ions coordinate to the sulfate ion sequentially. Finally, we demonstrate that MLIPs allow the study of solvated ions beyond simple monovalent pairs with DFT-level accuracy in their low concentration limit (0.1 M) via statistically converged properties from ns-long simulations.

anions↗
DOE OSTI2025

Atomistic Insights into Lithium–Glyme Solvate Ionic Liquids: Effects of Chain Length and Anion Coordination

For this study, mixtures of lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) in diglyme (G2), triglyme (G3) and tetraglyme (G4) at solvate ionic liquid (SIL) concentrations were investigated using classical molecular dynamics (cMD) simulations with a physically motivated force-field specifically developed for modeling these systems. The structural and dynamical properties of the mixtures were computed and analyzed. Lithium solvation shells, radial distribution functions, and X-ray structure factors were studied across the different SIL systems. Translational diffusion and rotational relaxation times were also evaluated, exhibiting similar trends with increasing glyme chain length. The results are consistent with experimental data and in good agreement with previous computational studies on G3 and G4. These findings validate the accuracy of the force field in modeling glyme systems and its use for describing the [Li(G2) 4/3 ][TFSI] mixture. Additionally, the thermal and electrochemical stability of these electrolytes were systematically examined. The thermal stability appears to be governed by cooperative interactions among glyme molecules, while the electrochemical stability is primarily influenced by Li + -anion interactions, which vary significantly with glyme chain length. Overall, the study sheds light on the crucial role of the anion in these glyme-based SILs and offers valuable insights into Li + -glyme systems at SIL concentrations, highlighting their promise as potential Li-ion battery electrolytes.

anions↗
DOE OSTI2020

Ex Machina Determination of Structural Correlation Functions

Determining the structural properties of condensed-phase systems is a fundamental problem in theoretical statistical mechanics. In this study, we present a machine learning method that is able to predict structural correlation functions with significantly improved accuracy in comparison with traditional approaches. The usefulness of this ex machina (from the machine) approach is illustrated by predicting the radial distribution functions of two paradigmatic condensed-phase systems, a Lennard-Jones fluid and a hard-sphere fluid, and then comparing those results to the results obtained using both integral equation methods and empirically motivated analytical functions. We find that application of the developed ex machina method typically decreases the predictive error by more than an order of magnitude in comparison with traditional theoretical methods.

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

An Efficient Reactive Force Field without Explicit Coordination Dependence for Studying Caustic Aluminum Chemistry

Reactive force fields (RFFs) are an expedient approach to sample chemical reaction paths in complex systems, relative to density functional theory. However, there is continued need to improve efficiencies, specifically in systems that have slow transverse degrees of freedom, as in highly viscous and superconcentrated solutions. Here, we present an RFF that is differentiated from current models (e.g., ReaxFF) by omitting explicit dependence on the atom coordination and employing a small parameter set based on Lennard-Jones, Gaussian, and Stillinger–Weber potentials. In this study, the model was parametrized from AIMD simulation data and is used to model aluminate reactivity in sodium hydroxide solutions with extensive validation against experimental radial distribution functions, computed free energy profiles for oligomerization, and formation energies. In conclusion, the model enables simulation of early stage Al(OH) 3 nucleation which has significant relevance to industrial processing of aluminum and has a computational cost that is reduced by 1 order of magnitude relative to ReaxFF.

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

Conductivity and Transference Numbers in Lithium Salt-Doped Block Copolymeric Ionic Liquid Electrolytes

We present multiscale molecular dynamics simulation results comparing the conductivity and transference numbers in lithium salt-doped polymeric ionic liquids (PolyILs) and the lamellar phases of block copolymeric ionic liquids (coPolyILs). In both systems, the anion mobilities decreased with salt loading. Lithium ions exhibited negative mobilities in both systems, but the magnitudes decreased with an increase in salt concentrations. More interestingly, the anion mobilities were lower in the lamellar systems compared to homopolymers in magnitude, but the lithium ion mobilities and transference numbers were less negative in such systems. We examine the anion–cation and lithium–anion interactions in terms of radial distribution functions, coordination characteristics, and ion-pair relaxation timescales. Based on such analyses, we rationalize the salt concentration dependencies as a result of the interfacial interactions in lamellar systems and the competition between anion–cation and lithium–anion interactions in both PolyILs and coPolyILs. Altogether, the findings presented in this study demonstrate that the modified anion–cation and lithium–anion interactions in the microphase-separated coPolyILs may provide a strategy for realizing higher lithium ion transference numbers relative to the homopolymeric counterparts.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Twisted Nonlinear Optics in Monolayer van der Waals Crystals

In addition to a plethora of emergent phenomena, the spatial topology of optical vortices enables an array of applications in optical communications and quantum information science. Multibeam nonlinear optical processes, augmented by optical vortices, are essential in this context, providing robust access to an infinitely large set of quantum states associated with the orbital angular momentum of light. Here, we push the boundaries of vortex nonlinear optics to the ultimate limits of material dimensionality. By exploiting multipulse difference frequency, sum frequency, and four-wave mixing in monolayer quantum materials, we demonstrate their ability to independently control the orbital angular momentum and radial distribution of vortex light-fields in addition to their wavelength. Due to the atomically thin nature of the host crystal, this control spans a broad spectral bandwidth in a highly integrable platform that is unconstrained by the traditional limits of bulk nonlinear optical materials. Our work heralds an innovative path for ultracompact and scalable hybrid nanophotonic technologies empowered by twisted nonlinear light–matter interactions in van der Waals nanomaterials.

36 MATERIALS SCIENCE↗