Publications

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The advent of fabrication techniques like additive manufacturing has focused attention on the considerable variability of material response due to defects and other micro-structural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. Lastly, we demonstrate that the new method provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.

Reducing defects in InGaN films deposited on GaN substrates has been critical to fill the “green” gap for solid-state lighting applications. To enable researchers to use molecular dynamics vapor deposition simulations to explores ways to reduce defects in InGaN films, we have developed and characterized a Stillinger-Weber potential for InGaN. We show that this potential reproduces the experimental atomic volume, cohesive energy, and bulk modulus of the equilibrium wurtzite / zinc-blende phases of both InN and GaN. Most importantly, the potential captures the stability of the correct phase of InGaN compounds against a variety of other elemental, alloy, and compound configurations. Lastly, this is validated by the potential’s ability to predict crystalline growth of stoichiometric wurtzite and zinc-blende In_xGa_1-xN compounds during vapor deposition simulations where adatoms are randomly injected to the growth surface.

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Metal organic frameworks (MOFs) are extended, nanoporous crystalline compounds consisting of metal ions interconnected by organic ligands. Their synthetic versatility suggest a disruptive class of opto - electronic materials with a high degree of electrical tunability and without the property - degrading disorder of organic conductors. In this project we determined the factors controlling charge and energy transport in MOFs and evaluated their potential for thermoelectric energy conversion. Two strategies for a chieving electronic conductivity in MOFs were explored: 1) using redox active 'guest' molecules introduced into the pores to dope the framework via charge - transfer coupling (Guest@MOF), 2) metal organic graphene analogs (MOGs) with dispersive band structur es arising from strong electronic overlap between the MOG metal ions and its coordinating linker groups. Inkjet deposition methods were developed to facilitate integration of the guest@MOF and MOG materials into practical devices.

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We investigate the formation of extended defects during molecular-dynamics (MD) simulations of GaN and InGaN growth on (0001) and ( 11 2 ¯ 0 ) wurtzite-GaN surfaces. The simulated growths are conducted on an atypically large scale by sequentially injecting nearly a million individual vapor-phase atoms towards a fixed GaN surface; we apply time-and-position-dependent boundary constraints that vary the ensemble treatments of the vapor-phase, the near-surface solid-phase, and the bulk-like regions of the growing layer. The simulations employ newly optimized Stillinger-Weber In-Ga-N-system potentials, wherein multiple binary and ternary structures are included in the underlying density-functional-theory training sets, allowing improved treatment of In-Ga-related atomic interactions. To examine the effect of growth conditions, we study a matrix of >30 different MD-growth simulations for a range of InxGa1-xN-alloy compositions (0 ≤ x ≤ 0.4) and homologous growth temperatures [0.50 ≤ T/T*m(x) ≤ 0.90], where T*m(x) is the simulated melting point. Growths conducted on polar (0001) GaN substrates exhibit the formation of various extended defects including stacking faults/polymorphism, associated domain boundaries, surface roughness, dislocations, and voids. In contrast, selected growths conducted on semi-polar ( 11 2 ¯ 0 ) GaN, where the wurtzite-phase stacking sequence is revealed at the surface, exhibit the formation of far fewer stacking faults. We discuss variations in the defect formation with the MD growth conditions, and we compare the resulting simulated films to existing experimental observations in InGaN/GaN. While the palette of defects observed by MD closely resembles those observed in the past experiments, further work is needed to achieve truly predictive large-scale simulations of InGaN/GaN crystal growth using MD methodologies.

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Surface energies of silicates influence crack propagation during brittle fracture and decrease with surface relaxation caused by annealing and hydroxylation. Molecular-level simulations are particularly suited for the investigation of surface processes. In this work, classical MD simulations of silica surfaces are performed with two force fields (ClayFF and ReaxFF) to investigate the effect of force field reactivity on surface structure and energy as a function of surface hydroxylation. An unhydroxylated fracture surface energy of 5.1 J/m2 is calculated with the ClayFF force field, and 2.0 J/m2 is calculated for the ReaxFF force field. The ClayFF surface energies are consistent with the experimental results from double cantilever beam fracture tests (4.5 J/m2), whereas ReaxFF underestimated these surface energies. Surface relaxation via annealing and hydroxylation was performed by creating a low-energy equilibrium surface. Annealing condensed neighboring siloxane bonds increased the surface connectivity, and decreased the surface energies by 0.2 J/m2 for ClayFF and 0.8 J/m2 for ReaxFF. Posthydroxylation surface energies decreased further to 4.6 J/m2 with the ClayFF force field and to 0.2 J/m2 with the ReaxFF force field. Experimental equilibrium surface energies are ∼0.35 J/m2, consistent with the ReaxFF force field. Although neither force field was capable of replicating both the fracture and equilibrium surface energies reported from experiment, each was consistent with one of these conditions. Therefore, future computational investigations that rely on accurate surface energy values should consider the surface state of the system and select the appropriate force field.

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Li-air or Li-oxygen batteries promise significantly higher energies than existing commercial battery technologies, yet their development has been hindered by a lack of suitable electrolytes. In this article, we evaluate the physical properties of varied electrolyte compositions to form generalized criteria for electrolyte design. We show that oxygen transport through non-aqueous electrolytes has a critical impact on the discharge rate and capacity of Li-air batteries. Through experiments and molecular dynamics simulations, we highlight that the choice of salt species and concentration have an outsized influence on oxygen solubility, while solvent choice is the major influence on oxygen diffusivity. The stability of superoxide reaction intermediates, key to the oxygen reduction mechanism, is also affected by variations in salt concentration and the choice of solvent. The importance of reactant transport is confirmed through Li-air cell discharge, which demonstrates good agreement between the observed and calculated mass transport-limited currents. These results showcase the impact of electrolyte composition on transport in metal-air batteries and provide guiding principles and simulation-based tools for future electrolyte design.

In this work we use estimates of ionic transport properties obtained from molecular dynamics to rank lithium electrolytes of different compositions. We develop linear response methods to obtain the Onsager diffusivity matrix for all chemical species, its Fickian counterpart, and the mobilities of the ionic species. We apply these methods to the well-studied propylene carbonate/ethylene carbonate solvent with dissolved LiBF4 and O2. The results show that, over a range of lithium concentrations and carbonate mixtures, trends in the transport coefficients can be identified and optimal electrolytes can be selected for experimental focus; however, refinement of these estimation techniques is necessary for a reliable ranking of a large set of electrolytes.

In atomistic simulations, diffusion energy barriers are usually calculated for each atomic jump path using a nudged elastic band method. Practical materials often involve thousands of distinct atomic jump paths that are not known a priori. Hence, it is often preferred to determine an overall diffusion energy barrier and an overall pre-exponential factor from the Arrhenius equation constructed through molecular dynamics simulations of mean square displacement of the diffusion species at different temperatures. This approach has been well established for interstitial diffusion, but not for substitutional diffusion at the same confidence. Using In 0.1 Ga 0.9 N as an example, we have identified conditions where molecular dynamics simulations can be used to calculate highly converged Arrhenius plots for substitutional alloys. As a result, this may enable many complex diffusion problems to be easily and reliably studied in the future using molecular dynamics, provided that moderate computing resources are available.

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Motivated by low cost, low toxicity, mechanical flexibility, and conformability over complex shapes, organic semiconductors are currently being actively investigated as thermoelectric (TE) materials to replace the costly, brittle, and non-eco-friendly inorganic TEs for near-ambient-temperature applications. Metal-organic frameworks (MOFs) share many of the attractive features of organic polymers, including solution processability and low thermal conductivity. A potential advantage of MOFs and MOFs with guest molecules (Guest@MOFs) is their synthetic and structural versatility, which allows both the electronic and geometric structure to be tuned through the choice of metal, ligand, and guest molecules. This could solve the long-standing challenge of finding stable, high-TE-performance n-type organic semiconductors, as well as promote high charge mobility via the long-range crystalline order inherent in these materials. In this article, we review recent advances in the synthesis of MOF and Guest@MOF TEs and discuss how the Seebeck coefficient, electrical conductivity, and thermal conductivity could be tuned to further optimize TE performance.

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In many scientific fields, empirical models are employed to facilitate computational simulations of engineering systems. For example, in fluid mechanics, empirical Reynolds stress closures enable computationally-efficient Reynolds Averaged Navier Stokes simulations. Likewise, in solid mechanics, constitutive relations between the stress and strain in a material are required in deformation analysis. Traditional methods for developing and tuning empirical models usually combine physical intuition with simple regression techniques on limited data sets. The rise of high performance computing has led to a growing availability of high fidelity simulation data. These data open up the possibility of using machine learning algorithms, such as random forests or neural networks, to develop more accurate and general empirical models. A key question when using data-driven algorithms to develop these empirical models is how domain knowledge should be incorporated into the machine learning process. This paper will specifically address physical systems that possess symmetry or invariance properties. Two different methods for teaching a machine learning model an invariance property are compared. In the first method, a basis of invariant inputs is constructed, and the machine learning model is trained upon this basis, thereby embedding the invariance into the model. In the second method, the algorithm is trained on multiple transformations of the raw input data until the model learns invariance to that transformation. Results are discussed for two case studies: one in turbulence modeling and one in crystal elasticity. It is shown that in both cases embedding the invariance property into the input features yields higher performance at significantly reduced computational training costs.

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Given the unique optical properties of LiF, it is often used as an observation window in high-temperature and -pressure experiments; hence, estimates of its transmission properties are necessary to interpret observations. Since direct measurements of the thermal conductivity of LiF at the appropriate conditions are difficult, we resort to molecular simulation methods. Using an empirical potential validated against ab initio phonon density of states, we estimate the thermal conductivity of LiF at high temperatures (1000-4000 K) and pressures (100-400 GPa) with the Green-Kubo method. We also compare these estimates to those derived directly from ab initio data. To ascertain the correct phase of LiF at these extreme conditions, we calculate the (relative) phase stability of the B1 and B2 structures using a quasiharmonic ab initio model of the free energy. We also estimate the thermal conductivity of LiF in an uniaxial loading state that emulates initial stages of compression in high-stress ramp loading experiments and show the degree of anisotropy induced in the conductivity due to deformation.

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Accurate simulation of the plastic deformation of ductile metals is important to the design of structures and components to performance and failure criteria. Many techniques exist that address the length scales relevant to deformation processes, including dislocation dynamics (DD), which models the interaction and evolution of discrete dislocation line segments, and crystal plasticity (CP), which incorporates the crystalline nature and restricted motion of dislocations into a higher scale continuous field framework. While these two methods are conceptually related, there have been only nominal efforts focused at the global material response that use DD-generated information to enhance the fidelity of CP models. To ascertain to what degree the predictions of CP are consistent with those of DD, we compare their global and microstructural response in a number of deformation modes. After using nominally homogeneous compression and shear deformation dislocation dynamics simulations to calibrate crystal plasticity ow rule parameters, we compare not only the system-level stress-strain response of prismatic wires in torsion but also the resulting geometrically necessary dislocation density fields. To establish a connection between explicit description of dislocations and the continuum assumed with crystal plasticity simulations we ascertain the minimum length-scale at which meaningful dislocation density fields appear. Furthermore, our results show that, for the case of torsion, that the two material models can produce comparable spatial dislocation density distributions.

We develop and demonstrate a method to efficiently use density functional calculations to drive classical dynamics of complex atomic and molecular systems. The method has the potential to scale to systems and time scales unreachable with current ab initio molecular dynamics schemes. It relies on an adapting dataset of independently computed Hellmann–Feynman forces for atomic configurations endowed with a distance metric. The metric on configurations enables fast database lookup and robust interpolation of the stored forces. Here, we discuss mechanisms for the database to adapt to the needs of the evolving dynamics, while maintaining accuracy, and other extensions of the basic algorithm.

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In this report we explore the sensitivities of the insulation resistance between two loops of wire embedded in insulating materials with a simple, approximate model. We discuss limita- tions of the model and ideas for improvements.

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TlBr is promising for g- and x- radiation detection, but suffers from rapid performance degradation under the operating external electric fields. To enable molecular dynamics (MD) studies of this degradation, we have developed a Stillinger-Weber type of TlBr interatomic potential. During this process, we have also addressed two problems of wider interests. First, the conventional Stillinger-Weber potential format is only applicable for tetrahedral structures (e.g., diamond-cubic, zinc-blende, or wurtzite). Here we have modified the analytical functions of the Stillinger-Weber potential so that it can now be used for other crystal structures. Second, past modifications of interatomic potentials cannot always be applied by a broad community because any new analytical functions of the potential would require corresponding changes in the molecular dynamics codes. Here we have developed a polymorphic potential model that simultaneously incorporates Stillinger-Weber, Tersoff, embedded-atom method, and any variations (i.e., modified functions) of these potentials. As a result, we have implemented this polymorphic model in MD code LAMMPS, and demonstrated that our TlBr potential enables stable MD simulations under external electric fields.

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We present a methodology to assess the predictive fidelity of multiscale simulations by incorporating uncertainty in the information exchanged between the components of an atomisticto-continuum simulation. We account for both the uncertainty due to finite sampling in molecular dynamics (MD) simulations and the uncertainty in the physical parameters of the model. Using Bayesian inference, we represent the expensive atomistic component by a surrogate model that relates the long-term output of the atomistic simulation to its uncertain inputs. We then present algorithms to solve for the variables exchanged across the atomistic-continuum interface in terms of polynomial chaos expansions (PCEs). We consider a simple Couette flow where velocities are exchanged between the atomistic and continuum components, while accounting for uncertainty in the atomistic model parameters and the continuum boundary conditions. Results show convergence of the coupling algorithm at a reasonable number of iterations. The uncertainty in the obtained variables significantly depends on the amount of data sampled from the MD simulations and on the width of the time averaging window used in the MD simulations.

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We present a Green-Kubo method to spatially resolve transport coefficients in compositionally heterogeneous mixtures. We develop the underlying theory based on well-known results from mixture theory, Irving-Kirkwood field estimation, and linear response theory. Then, using standard molecular dynamics techniques, we apply the methodology to representative systems. With a homogeneous salt water system, where the expectation of the distribution of conductivity is clear, we demonstrate the sensitivities of the method to system size, and other physical and algorithmic parameters. Then we present a simple model of an electrochemical double layer where we explore the resolution limit of the method. In this system, we observe significant anisotropy in the wall-normal vs. transverse ionic conductances, as well as near wall effects. Finally, we discuss extensions and applications to more realistic systems such as batteries where detailed understanding of the transport properties in the vicinity of the electrodes is of technological importance.

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Advances in technology for electrochemical energy storage require increased understanding of electrolyte/electrode interfaces, including the electric double layer structure, and processes involved in charging of the interface, and the incorporation of this understanding into quantitative models. Simplified models such as Helmholtz's electric double-layer (EDL) concept don't account for the molecular nature of ion distributions, solvents, and electrode surfaces and therefore cannot be used in predictive, high-fidelity simulations for device design. This report presents theoretical results from models that explicitly include the molecular nature of the electrical double layer and predict critical electrochemical quantities such as interfacial capacitance. It also describes development of experimental tools for probing molecular properties of electrochemical interfaces through optical spectroscopy. These optical experimental methods are designed to test our new theoretical models that provide descriptions of the electric double layer in unprecedented detail.

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Atomistic-scale behavior drives performance in many micro- and nano-fluidic systems, such as mircrofludic mixers and electrical energy storage devices. Bringing this information into the traditionally continuum models used for engineering analysis has proved challenging. This work describes one such approach to address this issue by developing atomistic-to-continuum multi scale and multi physics methods to enable molecular dynamics (MD) representations of atoms to incorporated into continuum simulations. Coupling is achieved by imposing constraints based on fluxes of conserved quantities between the two regions described by one of these models. The impact of electric fields and surface charges are also critical, hence, methodologies to extend finite-element (FE) MD electric field solvers have been derived to account for these effects. Finally, the continuum description can have inconsistencies with the coarse-grained MD dynamics, so FE equations based on MD statistics were derived to facilitate the multi scale coupling. Examples are shown relevant to nanofluidic systems, such as pore flow, Couette flow, and electric double layer.

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Thermal boundary resistance (inverse of conductance) between different material layers can dominate the overall thermal resistance in nanostructures and therefore impact the performance of the thermal property limiting nano devices. Because relationships between material properties and thermal boundary conductance have not been fully understood, optimum devices cannot be developed through a rational selection of materials. Here we develop generic interatomic potentials to enable material properties to be continuously varied in extremely large molecular dynamics simulations to explore the dependence of thermal boundary conductance on the characteristic properties of materials such as atomic mass, stiffness, and interfacial crystallography. To ensure that our study is not biased to a particular model, we employ different types of interatomic potentials. In particular, both a Stillinger-Weber potential and a hybrid embedded-atom-method + Stillinger-Weber potential are used to study metal-on-semiconductor compound interfaces, and the results are analyzed considering previous work based upon a Lennard-Jones (LJ) potential. These studies, therefore, reliably provide new understanding of interfacial transport phenomena particularly in terms of effects of material properties on thermal boundary conductance. Our most important finding is that thermal boundary conductance increases with the overlap of the vibrational spectra between metal modes and the acoustic modes of the semiconductor compound, and increasing the metal stiffness causes a continuous shift of the metal modes. As a result, the maximum thermal boundary conductance occurs at an intermediate metal stiffness (best matched to the semiconductor stiffness) that maximizes the overlap of the vibrational modes. © 2013 the Owner Societies.

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In this work we apply a Lagrangian kernel-based estimator of continuum fields to atomic data to estimate the J-integral for the emission dislocations from a crack tip. Face-centered cubic (fcc) gold and body-centered cubic (bcc) iron modeled with embedded atom method (EAM) potentials are used as example systems. The results of a single crack with a K-loading compare well to an analytical solution from anisotropic linear elastic fracture mechanics. We also discovered that in the post-emission of dislocations from the crack tip there is a loop size-dependent contribution to the J-integral. For a system with a finite width crack loaded in simple tension, the finite size effects for the systems that were feasible to compute prevented precise agreement with theory. However, our results indicate that there is a trend towards convergence. © 2013 IOP Publishing Ltd.

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We present a methodology to assess the predictive fidelity of multiscale simulations by incorporating uncertainty in the information exchanged between the atomistic and continuum simulation components. Focusing on uncertainty due to finite sampling in molecular dynamics (MD) simulations, we present an iterative stochastic coupling algorithm that relies on Bayesian inference to build polynomial chaos expansions for the variables exchanged across the atomistic-continuum interface. We consider a simple Couette flow model where velocities are exchanged between the atomistic and continuum components. To alleviate the burden of running expensive MD simulations at every iteration, a surrogate model is constructed from which samples can be efficiently drawn as data for the Bayesian inference. Results show convergence of the coupling algorithm at a reasonable number of iterations. The uncertainty associated with the exchanged variables significantly depends on the amount of data sampled from the MD simulations and on the width of the time averaging window used in the MD simulations. Sequential Bayesian updating is also implemented in order to enhance the accuracy of the stochastic algorithm predictions. © 2012 Society for Industrial and Applied Mathematics.

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