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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
This report describes an Engineering Sciences Research Foundation (ESRF) project to characterize and understand fracture processes via molecular dynamics modeling and atom-to-continuum methods. Under this aegis we developed new theory and a number of novel techniques to describe the fracture process at the atomic scale. These developments ranged from a material-frame connection between molecular dynamics and continuum mechanics to an atomic level J integral. Each of the developments build upon each other and culminated in a cohesive zone model derived from atomic information and verified at the continuum scale. This report describes an Engineering Sciences Research Foundation (ESRF) project to characterize and understand fracture processes via molecular dynamics modeling and atom-to-continuum methods. The effort is predicated on the idea that processes and information at the atomic level are missing in engineering scale simulations of fracture, and, moreover, are necessary for these simulations to be predictive. In this project we developed considerable new theory and a number of novel techniques in order to describe the fracture process at the atomic scale. Chapter 2 gives a detailed account of the material-frame connection between molecular dynamics and continuum mechanics we constructed in order to best use atomic information from solid systems. With this framework, in Chapter 3, we were able to make a direct and elegant extension of the classical J down to simulations on the scale of nanometers with a discrete atomic lattice. The technique was applied to cracks and dislocations with equal success and displayed high fidelity with expectations from continuum theory. Then, as a prelude to extension of the atomic J to finite temperatures, we explored the quasi-harmonic models as efficient and accurate surrogates of atomic lattices undergoing thermo-elastic processes (Chapter 4). With this in hand, in Chapter 5 we provide evidence that, by using the appropriate energy potential, the atomic J integral we developed is calculable and accurate at finite/room temperatures. In Chapter 6, we return in part to the fundamental efforts to connect material behavior at the atomic scale to that of the continuum. In this chapter, we devise theory that predicts the onset of instability characteristic of fracture/failure via atomic simulation. In Chapters 7 and 8, we describe the culmination of the project in connecting atomic information to continuum modeling. In these chapters we show that cohesive zone models are: (a) derivable from molecular dynamics in a robust and systematic way, and (b) when used in the more efficient continuum-level finite element technique provide results that are comparable and well-correlated with the behavior at the atomic-scale. Moreover, we show that use of these same cohesive zone elements is feasible at scales very much larger than that of the lattice. Finally, in Chapter 9 we describe our work in developing the efficient non-reflecting boundary conditions necessary to perform transient fracture and shock simulation with molecular dynamics.