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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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31 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Molecular Dynamics Simulation of a Multi-Walled Carbon Nanotube Based Gear

We used molecular dynamics to investigate the properties of a multi-walled carbon nanotube based gear. Previous work computationally suggested that molecular gears fashioned from (14,0) single-walled carbon nanotubes operate well at 50-100 gigahertz. The gears were formed from nanotubes with teeth added via a benzyne reaction known to occur with C60. A modified, parallelized version of Brenner's potential was used to model interatomic forces within each molecule. A Leonard-Jones 6-12 potential was used for forces between molecules. The gear in this study was based on the smallest multi-walled nanotube supported by some experimental evidence. Each gear was a (52,0) nanotube surrounding a (37,10) nanotube with approximate 20.4 and 16,8 A radii respectively. These sizes were chosen to be consistent with inter-tube spacing observed by and were slightly larger than graphite inter-layer spacings. The benzyne teeth were attached via 2+4 cycloaddition to exterior of the (52,0) tube. 2+4 bonds were used rather than the 2+2 bonds observed by Hoke since 2+4 bonds are preferred by naphthalene and quantum calculations by Jaffe suggest that 2+4 bonds are preferred on carbon nanotubes of sufficient diameter. One gear was 'powered' by forcing the atoms near the end of the outside buckytube to rotate to simulate a motor. A second gear was allowed to rotate by keeping the atoms near the end of its outside buckytube on a cylinder. The ends of both gears were constrained to stay in an approximately constant position relative to each other, simulating a casing, to insure that the gear teeth meshed. The stiff meshing aromatic gear teeth transferred angular momentum from the powered gear to the driven gear. The simulation was performed in a vacuum and with a software thermostat. Preliminary results suggest that the powered gear had trouble turning the driven gear without slip. The larger radius and greater mass of these gears relative to the (14,0) gears previously studied requires a smaller rotation rate and multiple rows of teeth to avoid excessive force on the gear teeth resulting, in slip and failure of the driven gear to turn. We hope that studies such as these will eventually lead to synthesis of components that can be assembled into atomically precise fullerene machines. These machines, in turn, may someday be used in machine-phase fullerene materials with remarkable properties.

Han, Jie↗
NASA NTRS1990

A study of microkinetic adjustments required to match shock wave experiments and Monte Carlo Direct Simulation for a wide Mach number range

Shock wave thickness predictions from Monte Carlo Direct Simulations, using differential scattering and the Maitland-Smith-Aziz interatomic potential, underpredict experiments as shock Mach numbers increase above about 4. Examination of several sources of data has indicated that at relatively high energies the repulsive portion of accepted potentials such as the Maitland-Smith-Aziz may be too steep. An Exponential-6 potential due to Ross, based on high energy molecular beam scattering data and shock velocity measurements in liquid argon, has been combined with the lower energy portion of the Maitland-Smith-Aziz potential. When this hybrid potential is used in Monte Carlo Direct Simulations, agreement with experiments is improved over the previous predictions using the pure Maitland-Smith-Aziz form.

Pham-Van-diep, Gerald C.↗
NASA NTRS2019

AladynPi – Adaptive Neural Network Molecular Dynamics Simulation Code with Physically Informed Potential: Computational Materials Mini-Application

This report provides an overview and description of commands used in the Computational Materials mini-application, AladynPi. AladynPi is an extension of a previously released mini-application, Aladyn (https://github.com/nasa/aladyn; Yamakov, V.I., and Glaessgen, E.H., NASA/TM-2018-220104). Aladyn and AladynPi are basic molecular dynamics codes written in FORTRAN 2003, which are designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory method. An input for the ANN is a set of structure coefficients, characterizing the local atomic environment of each atom, for which the atomic energy is obtained in the ANN inference process. In Aladyn, the ANN gives directly the energy of interatomic interactions. In AladynPi, the ANN gives optimized parameters for a predefined empirical function, known as bond-order-potential (BOP). The parameterized BOP function is then used to calculate the energy. AladynPi code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing manycore architectures based on graphic processing units (GPUs). The effort is supported by the High Performance Computing incubator (HPCi) project at NASA Langley Research Center.

Yamakov, Vesselin I.↗
NASA NTRSDate not supplied

Machine-Learned Committor Functions for Reactive Molecular Dynamics

Reactive molecular dynamics (MD) is a powerful tool for atomistic-scale modeling of a diverse range of chemical processes. However, scaling these simulations to large systems and long times scales remains a challenge because of the complexity of the potential energy function required. The authors previously developed a heuristic approach, called REACTER, that incorporates reactivity in MD simulations in a less general but much more computationally efficient manner. REACTER uses standard, fixed valence force fields as the underlying potentialenergy surface for describing all interatomic interactions but adds a procedure for enforcing user-defined reactions that occur when certain geometric constraints on relative atomic positions are satisfied. Further, these bonding changes can be accepted or rejected with a probability related tothe local thermal energy. This work seeks to generalize this approach by replacing the set of user defined geometric constraints and energetic criteria with a committor function that specifies the probability of a reaction occurring on the basis of the local atomic configuration. The committor function is a useful mathematical tool for modeling rare events but, unfortunately, is very difficult to compute for realistic systems in a general way. This work describes a method for approximating the committor function using a machine learning approach, specifically a deep neural network trained with data from reactive MD and DFT-based dynamics simulations. This network is coupled to the existing REACTER protocol, as implemented in the LAMMPS MD package, and used to make on-the-fly predictions of reaction probabilities without the more extensive user input previously required. The new method is demonstrated using the polymerization of polystyrene as a case study. Although very dependent on the quality and quantity of training data, machine-learned committor functions show promise as a method for incorporating reaction probability from higher level calculations into highly scalable MD simulations.

polymer simulations↗
NASA NTRS2019

Physically-Informed Artificial Neural Networks for Atomistic Modeling of Materials

A new approach is presented for the development of classical interatomic potentials using physically-informed neural networks (PINN) combined with an analytical bond-order atomic interaction model. Due to the strong physical underpinnings, the PINN potentials demonstrate much better transferability than the existing machine-learning potentials while drastically improving the accuracy in comparison with traditional potentials. PINN potentials can be constructed for both metallic and covalent materials in a unified manner. A number of applications of PINN potentials to large-scale molecular dynamics and Monte Carlo simulations and calculation of thermal and mechanical properties of diverse materials are demonstrated. Some of the specific materials systems include silicon and aluminum, as well as alloys and compounds. Computational aspects of PINN potentials are discussed and future developments in this field are outlined.

J Hickman↗
NASA NTRS1978

Solid-liquid phase transition in argon

Starting from the Lennard-Jones interatomic potential, a modified cell theory has been used to describe the solid-liquid phase transition in argon. The cell-size variations may be evaluated by a self-consistent condition. With the inclusion of cell-size variations, the transition temperature, the solid and liquid densities, and the liquid-phase radial-distribution functions have been calculated. These ab initio results are in satisfactory agreement with molecular-dynamics calculations as well as experimental data on argon.

Tsang, T.↗
NASA NTRS2021

Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures

Bottom-up design of electrolyte mixtures for battery systems requires predicting macro thermodynamic properties from molecular constituents. For instance, molten salt electrolyte batteries require conditions far above room temperature to operate. Therefore, discovering mixtures with increasingly lower eutectic melting points is desirable. A model that can approximate chemical activity is a valuable tool to search through the vast compositional design space. Machine learning can predict properties of materials such as vibrational free energies, electronic energy gaps, and thermal conductivities. Moreover, they can learn physical models such as interatomic potentials. The COSMO-SAC model uses theory and empirical parameterization to predict liquid-vapor and liquid-solid properties using first-principles calculations. However, obtaining activity coefficients required for parameterizing the COSMO-SAC model is costly and limited to a select chemical space. In this work, we explored if machine learning methods could improve the COSMO-SAC model and bridge density functional theory calculations to liquid phase thermodynamic properties. Our data-driven approach uses existing databases for sigma-profiles of organic solvents and reconciles their methodological differences via ensemble averaging. First, an optimal machine learning model is constructed for each dataset. Our machine learning algorithms use the sigma-profile as an input feature to predict binary mixtures' activity coefficients using multi-output regression. Each dataset uses different choices of functionals, methods, and basis sets. Therefore, our ensemble model attempts to predict corrected activity coefficients given the combination of all the model outputs. The activity coefficients used for training are generated using the COSMO-SAC model. This approach enables the extraction of meaningful information from the existing datasets to improve the COSMO-SAC model for obtaining thermodynamic properties of electrolyte mixtures. With the liquid phase activities, we can identify electrolyte mixtures that meet desired phase equilibria conditions.

Thermodynamics↗
NASA NTRS2016

Influence of Molecular Simulation Model Accuracy on the Interfacial Properties of an Ionic Liquid: Overview of Recommended Practices

Increasing the energy storage capability of ionic liquid supercapacitors will require better understanding of ion-electrode interactions. We have probed the influence of these interactions on the structure and differential capacitance of of an ionic liquid ([EMIM][BF4]) at an ideal graphite interface as a function of model accuracy. Of note, differential capacitance is determined through newly derived and validated fluctuation formulas. In terms of model accuracy, we test electrostatic techniques, electrode charging techniques, and electrolyte interatomic potentials. For electrostatic summations, we employ high cost, high fidelity techniques as well as less expensive, approximate techniques for summation in slab geometry. For electrode charging, uniform, constant-charge and environmentally responsive, constant-potential conditions are employed. For the ionic liquid, constant charge and atomically polarizable models are employed. We comment on the role of model accuracy on the structure and energetics of the electric double layer as well as on the magnitude and shape of differential capacitance.

simulations↗
NASA NTRS1989

Correlation of molecular valence- and K-shell photoionization resonances with bond lengths

The relationship between the interatomic distance and the positions of valence-shell and K-shell sigma(asterisk) photoionization resonances is investigated theoretically for the molecules C2, F2, N2, O2, CO, NO, C2H2, C2H4, C2H6, HCN, H2CO, N20, CO2, and C2N2. The results of molecular-orbital computations are presented in three-dimensional diagrams, which are shown to be similar to the wave functions of a particle in a cylindrical well, confirming the validity of free-electron molecular-orbital (FEMO) approximations for modeling the potential along the symmetry axis. FEMO orbital energies and resonance positions are found to be in good agreement with previous theoretical and experimental results. Also included is a Feshbach-Fano analysis of the relevance of virtual-valence orbitals to the appearance of single-channel resonances in molecular photoionization cross sections.

Sheehy, J. A.↗
NASA NTRS2000

The Rotational Spectra of IO X(sub 1) (sup 2)pi(sub 3/2), v <= 13 and X(sub 2) (sup 2)pi(sub 1/2), v <= 9

The rotational spectra of IO in vibrational states up to v = 13 in the X(sub 1) (sup 2)pi(sub 3/2) state and up to v = 9 in the X2 (sup 2)pi(sub 1/2) state have been observed in an O2 discharge over molecular I2. In addition, I(18)O has been observed for both the X(sub 1) and X(sub 2) states up to v = 5. All data have been analyzed simultaneously with fixed isotopic ratios among the constants. This extends the data set for the X(sub 1) state described last year at this meeting and provides the first high resolution data for the X(sub 2) state and for I(18)O. An extensive set of parameters has been derived. These will be interpreted in terms of the electronic structure and the interatomic potential.

Miller, Charles E.↗
NASA NTRS2001

Equivalent-Continuum Modeling of Nano-Structured Materials

A method has been developed for modeling structure-property relationships of nano-structured materials. This method serves as a link between computational chemistry and solid mechanics by substituting discrete molecular structures with an equivalent-continuum model. It has been shown that this substitution may be accomplished by equating the vibrational potential energy of a nano-structured material with the strain energy of representative truss and continuum models. As an important example with direct application to the development and characterization of single-walled carbon nanotubes, the model has been applied to determine the effective continuum geometry of a graphene sheet. A representative volume element of the equivalent-continuum model has been developed with an effective thickness. This effective thickness has been shown to be similar to, but slightly smaller than, the interatomic spacing of graphite.

Odegard, Gregory M.↗
NASA NTRS2000

Sputtering Erosion in the Ion Thruster

During the first phase of this research, the sputtering yields of molybdenum by low energy (100 eV and higher) xenon ions were measured by using the methods of secondary neutral mass spectrometry (SNMS) and Rutherford backscattering spectrometry (RBS). However, the measured sputtering yields were found to be far too low to explain the sputtering erosions observed in the long-duration tests of ion thrusters. The only difference between the sputtering yield measurement experiments and the ion thruster tests was that the later are conducted at high ion fluences. Hence, a study was initiated to investigate if any linkage exists between high ion fluence and an enhanced sputtering yield. The objective of this research is to gain an understanding of the causes of the discrepancies between the sputtering rates of molybdenum grids in an ion thruster and those measured from our experiments. We are developing a molecular dynamics simulation technique for studying low-energy xenon ion interactions with molybdenum. It is difficult to determine collision sequences analytically for primary ions below the 200 eV energy range where the ion energy is too low to be able to employ a random cascade model with confidence and it is too high to have to consider only single collision at or near the surface. At these low energies, the range of primary ions is about 1 to 2 nm from the surface and it takes less than 4 collisions on the average to get an ion to degrade to such an energy that it can no longer migrate. The fine details of atomic motion during the sputtering process are revealed through computer simulation schemes. By using an appropriate interatomic potential, the positions and velocities of the incident ion together with a sufficient number of target atoms are determined in small time steps. Hence, it allows one to study the evolution of damages in the target and its effect on the sputtering yield. We are at the preliminary stages of setting up the simulation program.

Ray, Pradosh K.↗
NASA NTRS1991

Ab initio study of the electrostatic multipole nature of torsional potentials in CH3SSCH3, CH3SSH, and HOOH

The origin of torsional potentials in H3CSSCH3, H3CSSH, and HOOH and the anisotropy of the local charge distribution has been analyzed in terms of atomic multipoles calculated from the ab initio LCAO-MO-SCF wave function in the 6-31G* basis set. The results indicate that for longer -S-S-bonds the major contribution to these torsional barriers are electrostatic interactions of the atomic multipoles located on two atoms forming the rotated bond. This finding demonstrates the important role of electrostatic 1-2 interatomic interactions, usually neglected in conformational studies. It also opens the possibility to derive directly from accurate ab initio wave functions a simple nonempirical torsional potential involving atomic multipoles of two bonded atoms defining the torsional angle. For shorter -O-O- bonds, use of more precise models and inclusion of 1-3 interactions seems to be necessary.

NASA Program Exobiology↗