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DOE OSTI2019

Structure of Ionic Liquids Under Nanoconfinement in Energy Applications

Room temperature ionic liquids are widely regarded as the most promising next-generation electrolyte for energy storage. However, their structure, which dictates a device’s ability to store electrical charge, at interfaces and under nanoconfinement remains poorly understood. By integrating cutting-edge X-ray characterization tools, advanced nanocarbon synthesis and nanofabrication, and ab initio molecular dynamics simulation, this work probed with spatial and chemical detail the structure of ionic liquids confined in carbon nanopores. This provides mechanistic understanding toward optimizing ionic liquid-solid electrode interfaces and developing design rules that could dramatically increasing capacity and charge/discharge rates of future supercapacitor devices. Nevertheless, the ability to probe ions at interfaces is also critical in other areas such as batteries, desalination, electrocatalysis, and biological and chemical sensors.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Spin dynamics of the antiferromagnetic Heisenberg model on a kagome bilayer

We research the spin dynamics of a classical Heisenberg antiferromagnet with nearest-neighbor interactions on a quasi-two-dimensional kagome bilayer. This geometrically frustrated lattice consists of two kagome layers connected by a triangular-lattice linking layer. By combining Monte Carlo with precessional spin dynamics simulations, we compute the dynamical structure factor of the classical spin liquid in the kagome bilayer and investigate the thermal and dilution effects. While the low-frequency and long-wavelength dynamics of the cooperative paramagnetic phase is dominated by spin diffusion, weak magnon excitations persist at higher energies, giving rise to the half-moon pattern in the dynamical structure factor. In the presence of spin vacancies, the dynamical properties of the diluted system can be understood within the two-population picture. The spin diffusion of the “correlated” spin clusters is mainly driven by the zero-energy weather-vane modes, giving rise to an autocorrelation function that decays exponentially with time. On the other hand, the diffusive dynamics of the quasifree “orphan” spins leads to a distinctive longer-time power-law tail in the autocorrelation function. We discuss the implications of our work for the glassy behaviors observed in the archetypal frustrated magnet SrCr 9p Ga 12-9 pO 19 (SCGO).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1992

LWIR scene simulator developed for end-to-end performance evaluation of focal planes

The development of a long-wave infrared optical simulator facilitates evaluation of the end-to-end performance of long wavelength infrared (LWIR) focal plane arrays (FPAs) in a system-like environment. This simulator provides selectable structured scene inputs to a focal plane module or array. Background irradiances as low as 10 exp 10 photons/sq cm s are achievable when the simulator is cooled with liquid helium. The optical simulator can generate single or multiple targets of controllable intensities, and uniform or structured background irradiances. The infrared scenes can be viewed in a stationary mode or dynamically scanned across the focal plane.

Thompson, Niels A.↗

DOE OSTI2020

Learning Coarse-Grained Potentials for Binary Fluids

Even though atomistic and CG models have been used to simulate liquid nanodroplets in vapor, very few rigorous studies of the liquid-liquid interface structure are available, and most of them are limited to planar interfaces. In this work, we evaluate several existing force fields (FF)s, including two atomistic and three CG FFs, with respect to modeling the interface structure and thermodynamic properties of the water-hexane interface. Both atomistic FFs are able to quantitatively reproduce the interfacial tension and the coexisting densities of the experimentally-observed planar interface. We use the atomistic FFs to model water droplets in hexane and use these simulations to test the CG FFs. We found that the tested CG FFs cannot reproduce the interfacial tensions of planar and/or curved interfaces. Finally, we propose a new approach for learning coarse-grained (CG) potentials within the CG SDK (Shinoda-DeVane- Klein) FF framework from atomistic simulation data. We demonstrate that the new potential significantly improves the prediction of both the interfacial tension and structure of water-hexane planar and curved interfaces.

Gao, Peiyuan↗

DOE OSTI2018

Pressure induced short-range structural changes in supercooled liquid Ge 2 Sb 2 Te 5

Phase-change material such as Ge 2 Sb 2 Te 5 is usually utilized to store data due to the pronounced contrast in optical and electrical properties between crystalline and amorphous phases. As the density differs in the two phases, it is necessary to explore the influence of pressure on the structures of Ge 2 Sb 2 Te 5 , especially for the supercooled liquid which is an inevitable state in the formation of the two phases. The short-range structures in supercooled liquid Ge 2 Sb 2 Te 5 under compression have been investigated by using ab initio molecular dynamics simulation. The supercooled liquid eventually changes to a solid with an increase in pressure. During the process, tetrahedrons decrease slightly, revealing that tetrahedral structures are insensitive to the pressure. Octahedrons increase as the pressure is less than 3.7 GPa and then decrease, suggesting that a moderate pressure can promote the formation of octahedrons. The body-centered-cubic, face-centered-cubic and hexagonal closed-packed structures are observed at 9.9 GPa and then increase gradually. Additionally, it is noticed that Sb- and Te-centered clusters prefer Ge-centered clusters to forming the high-coordinated short-range structures during the compression process. Here our research makes us aware of the effects of pressure on short-range structures in supercooled liquid, which is of great importance in the application of phase-change materials.

36 MATERIALS SCIENCE↗

NASA NTRS2022

Research Campaign: Advanced Manufacturing in Space: Properties, Structure and Simulations to Pioneer New Applications

This topic underpins synthesis of, and in many cases manufacturing of, value-added materials via liquid phase processing. Materials and processes range from cast metals and optical glasses through exploration-relevant technologies such as additive manufacturing, casting and in-situ resource utilization to support human space exploration. A recurring theme is the need for accurate thermophysical property and atomic structure data for relevant materials and the exploitation of reduced convection/sedimentation in microgravity to enhance the quality of data and to develop new and transformative technologies for materials processing.

Michael SanSoucie↗

DOE OSTI2019

Sensitivity to Physical and Numerical Aspects of Large-Eddy Simulation of Stratocumulus

Abstract A series of numerical experiments where both physical and numerical model parameters are varied with respect to a reference setup is used to investigate the physics of a stratocumulus cloud and the performance of a large-eddy simulation (LES) model. The simulations show a delicate balance of physical processes with some sensitivities amplified by numerical model features. A strong feedback between cloud liquid, cloud-top radiative cooling, and turbulence leads to slow grid convergence of the turbulent fluxes. For a methodology that diagnoses cloud liquid from conserved variables, small errors in the total water amount result in large liquid water errors, which are amplified by the cloud-top radiative cooling leading to large variations of buoyancy forcing. In contrast, when the liquid–radiation–buoyancy feedback is not present in simulations without radiation, the turbulence structure of the boundary layer remains essentially identical for grid resolutions between 20 and 1.25 m. The present runs suggest that the buoyancy reversal instability significantly enhances the entrainment rate. Even though cloud-top radiative cooling is regarded as a key attribute of stratocumulus, the present simulations suggest that surface fluxes and surface shear significantly contribute to the total turbulent kinetic energy. Turbulence spectra exhibit inertial range scaling away from the confinement effects of the surface and inversion. Fine grid resolution LESs agree with observations, especially with respect to cloud liquid and vertical velocity variance, and exhibit grid convergence without any model tuning or ad hoc model choices.

Matheou, Georgios↗

DOE OSTI2026

Effects of Dimethylamino Functional Group Substitution on the Physical, Structural and Radiolytic Properties of Pyridinium Ionic Liquids

A diverse range of 4-dimethylaminopyridinium (DMAP) bis(trifluoromethylsulfonyl)-amide ionic liquids with specific functionalities (alkyl, alkoxy, hydroxyalkyl and benzyl) were designed, characterized and compared with their pyridinium analogs in terms of their physical and radiolytic properties. The influence of the dimethylamino group on ionic liquid structure was investigated by X-ray diffraction and molecular dynamics simulations. The influence of the electron-donating ability of the dimethylamino-substituted cation is evident in the differences in the electronic density of states between the DMAP and pyridinium ILs. This leads to substantial changes in the radical transients observed in pulse radiolysis of the neat ILs. It was found that the DMAP salts were higher melting, more viscous and less conducting than their pyridinium analogs. However, the DMAP salts exhibited higher thermal stabilities and could therefore be useful for high-temperature applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Machine learning interatomic potential for silicon-nitride (Si 3 N 4 ) by active learning

Silicon nitride (Si 3 N 4 ) is an extensively used material in the automotive, aerospace, and semiconductor industries. However, its widespread use is in contrast to the scarce availability of reliable interatomic potentials that can be employed to study various aspects of this material on an atomistic scale, particularly its amorphous phase. In this work, we developed a machine learning interatomic potential, using an efficient active learning technique, combined with the Gaussian approximation potential (GAP) method. Our strategy is based on using an inexpensive empirical potential to generate an initial dataset of atomic configurations, for which energies and forces were recalculated with density functional theory (DFT); thereafter, a GAP was trained on these data and an iterative re-training algorithm was used to improve it by learning on-the-fly. When compared to DFT, our potential yielded a mean absolute error of 8 meV/atom in energy calculations for a variety of liquid and amorphous structures and a speed-up of molecular dynamics simulations by 3–4 orders of magnitude, while achieving a first-rate agreement with experimental results. Our potential is publicly available in an open-access repository.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1976

Surface tension propellant control for Viking 75 Orbiter

The paper describes the selection, development and qualification of the surface tension system and includes results of low-g drop tower tests of scale models, 1-g simulation tests of low-g large ullage settling and liquid withdrawal, structural qualification tests, and propellant surface tension/contact angle studies. Subscale testing and analyses were used to evaluate the ability of the system to maintain or recover the desired propellant orientation following possible disturbances during the Viking mission. This effort included drop tower tests to demonstrate that valid wick paths exist for moving any displaced propellant back over the tank outlet. Variations in surface tension resulting from aging, temperature, and lubricant contamination were studied and the effects of surface finish, referee fluid exposure, aging, and lubricant contamination on contact angle were assessed. Results of movies of typical subscale drop tower tests and full scale slosh tests are discussed.

Dowdy, M. W.↗

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 OSTI2025

Strain release by 3D atomic misfit in fivefold twinned icosahedral nanoparticles with amorphization and dislocations

Multiple twinning to form fivefold twinned nanoparticles in crystal growth is common and has attracted broad attention ranging from crystallography research to physical chemistry and materials science. Lattice-misfit strain and defects in multiple twinned nanoparticles (MTP) are key to understand and tailor their electronic properties. However, the structural defects and related strain distributions in MTPs are poorly understood in three dimensions (3D). Here, we show the 3D atomic misfit and strain relief mechanism in fivefold twinned icosahedral nanoparticles with amorphization and dislocations by using atomic resolution electron tomography. We discover a two-sided heterogeneity in variety of structural characteristics. A nearly ideal crystallographic fivefold face is always found opposite to a less ordered face, forming Janus-like icosahedral nanoparticles with two distinct hemispheres. The disordered amorphous domains release a large amount of strain. Molecular dynamics simulations further reveal the Janus-like icosahedral nanoparticles are prevalent in the MTPs formed in liquid-solid phase transition. This work provides insights on the atomistic models for the modelling of formation mechanisms of fivefold twinned structures and computational simulations of lattice distortions and defects. We anticipate it will inspire future studies on fundamental problems such as twin boundary migration and kinetics of structures in 3D at atomic level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Impact of solvent forces and broken symmetry on the assembly of designed proteins at a liquid-solid interface

The era of protein design has enabled the creation of hybrid protein-inorganic interfaces, leading to both surface-directed self-assembly of de novo protein architectures and protein-directed formation of inorganic materials. However, the resulting patterns of protein assembly are often unexpected, implying that essential interactions are not accounted for in current design platforms. Here, we use high-speed atomic force microscopy (AFM) analyzed through machine learning to follow the assembly of protein nanorods in aqueous electrolytes on two types of mica exhibiting disparate symmetry elements, which are imprinted on the overlying hydration structure. Using Monte Carlo simulations, we reproduce the observed phases and show that an observed smectic phase, previously thought to be unstable for non-interacting rods in two dimensions, emerges when crystal symmetry introduces a directional bias. The findings demonstrate the importance of incorporating solvent forces as modulated by the hydration structure inherent to interfacial systems when designing protein assemblies at liquid-crystal interfaces. Coupling physics-based simulations that can account for these factors to de novo protein design algorithms can lead to improved design platforms for bio-inspired, hybrid materials.

bioinspired materials↗

DOE OSTI2024

Capturing thin structures in VOF simulations with two-plane reconstruction

A novel interface reconstruction strategy for volume of fluid (VOF) methods is introduced that represents the liquid-gas interface as two planes that co-exist within a single computational cell. In comparison to the piecewise linear interface calculation (PLIC), this new algorithm greatly improves the accuracy of the reconstruction, in particular when dealing with thin structures such as films. The placement of the two planes requires the solution of a non-linear optimization problem in six dimensions, which has the potential to be overly expensive. Further, an efficient solution to this optimization problem is presented here that exploits two key ideas: an algorithm for extracting multiple plane orientations from transported surface data, and an efficient and mass-conserving distance-finding algorithm that accounts for two planes with arbitrary orientation. Additionally, a simple and robust strategy is presented to accurately represent the surface tension forces produced at the interface of subgrid-thickness films. The performance of this new VOF reconstruction is demonstrated on several test cases that illustrate the capability to handle arbitrarily thin films.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

A deep learning interatomic potential developed for atomistic simulation of carbon materials

Interatomic potentials based on neural-network machine learning method have attracted considerable attention in recent years owing to their outstanding ability to balance the accuracy and efficiency in atomistic simulations. In this work, a neural-network potential (NNP) for carbon is generated to simulate the structural properties of various carbon structures. The potential is trained using a database consisting of crystalline and liquid structures obtained by the first-principles density functional theory (DFT) calculations. The developed potential accurately predicts the energies and forces in crystalline and liquid carbon structures, the energetic stability of defected graphene, and the structures of amorphous carbon as the function of density. As a result, the excellent accuracy and transferability of the NNP provide a promising tool for accurate atomistic simulations of various carbon materials with faster speed and much lower cost.

36 MATERIALS SCIENCE↗

NASA NTRS2016

Li-Doped Ionic Liquid Electrolytes: From Bulk Phase to Interfacial Behavior

Ionic liquids have been proposed as candidate electrolytes for high-energy density, rechargeable batteries. We present an extensive computational analysis supported by experimental comparisons of the bulk and interfacial properties of a representative set of these electrolytes as a function of Li-salt doping. We begin by investigating the bulk electrolyte using quantum chemistry and ab initio molecular dynamics to elucidate the solvation structure of Li(+). MD simulations using the polarizable force field of Borodin and coworkers were then performed, from which we obtain an array of thermodynamic and transport properties. Excellent agreement is found with experiments for diffusion, ionic conductivity, and viscosity. Combining MD simulations with electronic structure computations, we computed the electrochemical window of the electrolytes across a range of Li(+)-doping levels and comment on the role of the liquid environment. Finally, we performed a suite of simulations of these Li-doped electrolytes at ideal electrified interfaces to evaluate the differential capacitance and the equilibrium Li(+) distribution in the double layer. The magnitude of differential capacitance is in good agreement with our experiments and exhibits the characteristic camel-shaped profile. In addition, the simulations reveal Li(+) to be highly localized to the second molecular layer of the double layer, which is supported by additional computations that find this layer to be a free energy minimum with respect to Li(+) translation.

molecular dynamics↗

DOE OSTI2020

Direct observation of 3D atomic packing in monatomic amorphous materials

Liquids and solids are two fundamental states of matter. Although the structure of crystalline solids has long been solved by crystallography, our understanding of the 3D atomic structure of liquids and amorphous materials remained speculative. In 1952, Frank hypothesized that icosahedral order is the prevalent atomic motif in monatomic liquids. Over the past six decades, there have been a great deal of experimental, computational and theoretical studies to understand the structure of liquids and amorphous materials. A polytetrahedral packing model was proposed to explain the 3D atomic structure of monatomic liquids and amorphous materials, in which icosahedral order is a key feature. The icosahedral order has also been found to play an important role in the structure of metallic glasses and quasicrystals. Here we advance atomic electron tomography to determine for the first time the 3D atomic positions in monatomic amorphous materials, including a Ta thin film and two Pd nanoparticles. Despite the different chemical composition and synthesis methods, we observe that pentagonal bipyramids are the most abundant atomic motifs in these amorphous materials. Contrary to the traditional understanding, the pentagonal bipyramids do not assemble full icosahedra, but are closely connected with each other to form networks that extend to medium-range scale. Our molecular dynamic simulations, coupled with experimental results, further reveal that the 3D atomic structure of monatomic liquids is similar to the experimental amorphous material. Moreover, we find that the pentagonal bipyramid networks, prevalent in monatomic liquids, rapidly grow in size and form a fraction of full icosahedra during the quench from a liquid to a metallic glass state, providing a possible new picture of structural evolution through the glass transition.

Yuan, Yakun↗

DOE OSTI2023

Molecular dynamics simulation of Fe-Si alloys using a neural network machine learning potential

Interatomic potential development using machine learning (ML) approaches has attracted a lot of attention in recent years because these potentials can effectively describe the structural and dynamical properties of complex materials at the atomistic level. Here, in this work, we present the development of a neural network (NN) deep ML interatomic potential for Fe-Si alloys, and we demonstrate the effectiveness of the NN-ML potential in predicting the structures and energies of liquid and crystalline phases of Fe-Si alloys in comparison with the results from ab initio molecular dynamics simulations or experimental data. The developed NN-ML potential is also used to perform molecular dynamics simulations to study the structures of Fe-Si alloys with various compositions under rapid solidification conditions. The short-ranged orders in the rapidly solidified Fe-Si alloys are also analyzed by a cluster alignment method.

36 MATERIALS SCIENCE↗