Influence of polarizability on metal oxide properties studied by molecular dynamics simulations

We have studied the dependence of metal oxide properties in molecular dynamics (MD) simulations on the polarizability of oxygen ions. We present studies of both liquid and crystalline structures of silica (SiO2), magnesia (MgO) and alumina (Al2O3). For each of the three oxides, two separately optimized sets of force fields were used: (i) Long-range Coulomb interactions between oxide and metal ions combined with a short-range pair potential. (ii) Extension of force field (i) by adding polarizability to the oxygen ions. We show that while an effective potential of type (i) without polarizable oxygen ions can describe radial distributions and lattice constants reasonably well, potentials of type (ii) are required to obtain correct values for bond angles and the equation of state. The importance of polarizability for metal oxide properties decreases with increasing temperature.Comment: 8 pages, 7 figure

Uncertainty quantification for classical effective potentials: an extension to potfit

Effective potentials are an essential ingredient of classical molecular dynamics (MD) simulations. Little is understood of the consequences of representing the complex energy landscape of an atomic configuration by an effective potential or force field containing considerably fewer parameters. The probabilistic potential ensemble method has been implemented in the potfit force matching code. This introduces uncertainty quantification into the interatomic potential generation process. Uncertainties in the effective potential are propagated through MD to obtain uncertainties in quantities of interest, which are a measure of the confidence in the model predictions. We demonstrate the technique using three potentials for nickel: two simple pair potentials, Lennard-Jones and Morse, and a local density dependent embedded atom method (EAM) potential. A potential ensemble fit to density functional theory (DFT) reference data is constructed for each potential to calculate the uncertainties in lattice constants, elastic constants and thermal expansion. We quantitatively illustrate the cases of poor model selection and fit, highlighted by the uncertainties in the quantities calculated. This shows that our method can capture the effects of the error incurred in quantities of interest resulting from the potential generation process without resorting to comparison with experiment or DFT, which is an essential part to assess the predictive power of MD simulations.Comment: 10 pages, 3 figure

Understanding long-time vacancy aggregation in iron: a kinetic activation-relaxation technique study

Vacancy diffusion and clustering processes in body-centered-cubic (bcc) Fe are studied using the kinetic activation-relaxation technique (k-ART), an off-lattice kinetic Monte Carlo method with on-the-fly catalog building capabilities. For monovacancies and divacancies, k-ART recovers previously published results while clustering in a 50-vacancy simulation box agrees with experimental estimates. Applying k-ART to the study of clustering pathways for systems containing from one to six vacancies, we find a rich set of diffusion mechanisms. In particular, we show that the path followed to reach a hexavacancy cluster influences greatly the associated mean-square displacement. Aggregation in a 50-vacancy box also shows a notable dispersion in relaxation time associated with effective barriers varying from 0.84 to 1.1 eV depending on the exact pathway selected. We isolate the effects of long-range elastic interactions between defects by comparing to simulations where those effects are deliberately suppressed. This allows us to demonstrate that in bcc Fe, suppressing long-range interactions mainly influences kinetics in the first 0.3 ms, slowing down quick energy release cascades seen more frequently in full simulations, whereas long-term behavior and final state are not significantly affected.Comment: 11 pages, 12 figures. Updated to post-review manuscrip

Entwicklung und Test von Wechselwirkungspotenzialen für komplexe intermetallische Verbindungen

Complex metallic alloys and quasicrystals show extraordinary physical properties relevant for technological applications, for example hardness at low density. In the study of these systems, atomistic simulation with classical interaction potentials is a very promising tool. Such simulations require classical effective potentials describing the cohesive energy as a function of the atomic coordinates. The quality of the simulation depends crucially on the accuracy with which this potential describes the real interactions. One way to generate physically relevant potentials is the force matching method, where the parameters of a potential are adjusted to optimally reproduce the forces on individual atoms determined from quantum-mechanical calculation. The programme package potfit developed as part of this thesis implements the force matching method efficiently. Potentials are generated for a number of complex metallic alloy systems. A potential for the decagonal basic Ni-rich Al-Co-Ni quasicrystal is used to simulate diffusion processes and melting. In the CaCd6 system built from multishelled clusters, the shape and orientation of the innermost cluster shell is studied. Finally, phonon dispersion in the Mg-Zn system is determined and compared to experiment. The programme potfit is shown to be an effective tool for generating physically justified effective potentials. Potentials created with potfit can greatly improve the understanding of complex metallic alloys through atomistic simulations.Komplexe intermetallische Verbindungen und Quasikristalle zeigen außergewöhnliche physikalische Eigenschaften, wie z.B. Härte bei geringer Dichte. Bei der Untersuchung dieser Systeme sind atomistische Simulationen mit klassischen Wechselwirkungspotenzialen ein wichtiges Werkzeug. Für solche Simulationen benötigt man klassische effektive Potenziale, die die Bindungsenergie als eine Funktion der Atomkoordinaten beschreiben. Die Qualität der Simulation hängt entscheidend von der Genauigkeit ab, mit der diese Potenziale die echten Wechselwirkungen wiedergeben. Eine Möglichkeit, physikalisch relevante Potenziale zu erzeugen, ist die Force-Matching-Methode. Dabei werden die Parameter eines Potenzials so angepasst, dass die mit quantenmechanischen Methoden bestimmten Kräfte auf die einzelnen Atome bestmöglich reproduziert werden. Das als Teil dieser Arbeit entwickelte Programmpaket potfit implementiert die Force-Matching-Methode effizient. Für einige komplexe intermetallische Verbindungen werden Potenziale bestimmt. In dekagonalen Al-Co-Ni-Quasikristallen werden mit Hilfe eines Potenzials Diffusionsprozesse und Schmelzvorgänge simuliert. In der aus mehrschaligen Clustern bestehenden CaCd6-Verbindung wird die Struktur der innersten Clusterschale untersucht. Schließlich wird die Phononendispersion im Mg-Zn-System bestimmt und mit experimentellen Ergebnissen verglichen. Es wird gezeigt, dass das Programm potfit ein effektives Werkzeug zur Erzeugung physikalisch gerechtfertigter Wechselwirkungen ist. Potenziale, die mit potfit erzeugt werden, können zum Verständnis komplexer metallischer Verbindungen durch atomistische Simulationen viel beitragen

Enhancement of island size by dynamic substrate disorder in simulations of graphene growth

We demonstrate a new mechanism in the early stages of sub-monolayer epitaxial island growth, using Monte Carlo simulations motivated by experimental observations on the growth of graphene on copper foil. In our model, the substrate is “dynamically rough”, by which we mean (i) the interaction strength between Cu and C varies randomly from site to site, and (ii) these variable strengths themselves migrate from site to site. The dynamic roughness provides a simple representation of the near-molten state of the Cu substrate in the case of real graphene growth. Counterintuitively, the graphene island size increases when dynamic roughness is included, compared to a static and smooth substrate. We attribute this effect to destabilisation of small graphene islands by fluctuations in the substrate, allowing them to break up and join larger islands which are more stable against roughness. In the case of static roughness, when process (ii) is switched off, island growth is strongly inhibited and the scale-free behaviour of island size distributions, present in the smooth-static and rough-dynamic cases, is destroyed. The effects of the dynamic substrate roughness cannot be mimicked by parameter changes in the static cases

The Activation-Relaxation Technique : ART nouveau and kinetic ART

The evolution of many systems is dominated by rare activated events that occur on timescale ranging from nanoseconds to the hour or more. For such systems, simulations must leave aside the full thermal description to focus specifically on mechanisms that generate a configurational change. We present here the activation relaxation technique (ART), an open-ended saddle point search algorithm, and a series of recent improvements to ART nouveau and kinetic ART, an ART-based on-the-fly off-lattice self-learning kinetic Monte Carlo method

Diffusion of point defects in crystalline silicon using the kinetic activation-relaxation technique method

We study point-defect diffusion in crystalline silicon using the kinetic activation-relaxation technique (k-ART), an off-lattice kinetic Monte Carlo method with on-the-fly catalog building capabilities based on the activation-relaxation technique (ART nouveau), coupled to the standard Stillinger-Weber potential. We focus more particularly on the evolution of crystalline cells with one to four vacancies and one to four interstitials in order to provide a detailed picture of both the atomistic diffusion mechanisms and overall kinetics. We show formation energies, activation barriers for the ground state of all eight systems, and migration barriers for those systems that diffuse. Additionally, we characterize diffusion paths and special configurations such as dumbbell complex, di-interstitial (IV-pair+2I) superdiffuser, tetrahedral vacancy complex, and more. This study points to an unsuspected dynamical richness even for this apparently simple system that can only be uncovered by exhaustive and systematic approaches such as the kinetic activation-relaxation technique

Bayesian structural identification of a long suspension bridge considering temperature and traffic load effects

© The Author(s) 2018. This article presents a probabilistic structural identification of the Tamar bridge using a detailed finite element model. Parameters of the bridge cables initial strain and bearings friction were identified. Effects of temperature and traffic were jointly considered as a driving excitation of the bridge’s displacement and natural frequency response. Structural identification is performed with a modular Bayesian framework, which uses multiple response Gaussian processes to emulate the model response surface and its inadequacy, that is, model discrepancy. In addition, the Metropolis–Hastings algorithm was used as an expansion for multiple parameter identification. The novelty of the approach stems from its ability to obtain unbiased parameter identifications and model discrepancy trends and correlations. Results demonstrate the applicability of the proposed method for complex civil infrastructure. A close agreement between identified parameters and test data was observed. Estimated discrepancy functions indicate that the model predicted the bridge mid-span displacements more accurately than its natural frequencies and that the adopted traffic model was less able to simulate the bridge behaviour during traffic congestion periods

Kinetic Activation Relaxation Technique

We present a detailed description of the kinetic Activation-Relaxation Technique (k-ART), an off-lattice, self-learning kinetic Monte Carlo algorithm with on-the-fly event search. Combining a topological classification for local environments and event generation with ART nouveau, an efficient unbiased sampling method for finding transition states, k-ART can be applied to complex materials with atoms in off-lattice positions or with elastic deformations that cannot be handled with standard KMC approaches. In addition to presenting the various elements of the algorithm, we demonstrate the general character of k-ART by applying the algorithm to three challenging systems: self-defect annihilation in c-Si (crystalline silicon), self-interstitial diffusion in Fe and structural relaxation in a-Si (amorphous silicon).Comment: 13 pages, 11 figures. Final version as published, Figs. 6 and 7 exchanged, minor typographical and stylistic correction

A KIM-compliant potfit for fitting sloppy interatomic potentials : application to the EDIP model for silicon

Fitted interatomic potentials are widely used in atomistic simulations thanks to their ability to compute the energy and forces on atoms quickly. However, the simulation results crucially depend on the quality of the potential being used. Force matching is a method aimed at constructing reliable and transferable interatomic potentials by matching the forces computed by the potential as closely as possible, with those obtained from first principles calculations. The potfit program is an implementation of the force-matching method that optimizes the potential parameters using a global minimization algorithm followed by a local minimization polish. We extended potfit in two ways. First, we adapted the code to be compliant with the KIM Application Programming Interface (API) standard (part of the Knowledgebase of Interatomic Models Project). This makes it possible to use potfit to fit many KIM potential models, not just those prebuilt into the potfit code. Second, we incorporated the geodesic Levenberg–Marquardt (LM) minimization algorithm into potfit as a new local minimization algorithm. The extended potfit was tested by generating a training set using the KIM Environment-Dependent Interatomic Potential (EDIP) model for silicon and using potfit to recover the potential parameters from different initial guesses. The results show that EDIP is a “sloppy model” in the sense that its predictions are insensitive to some of its parameters, which makes fitting more difficult. We find that the geodesic LM algorithm is particularly efficient for this case. The extended potfit code is the first step in developing a KIM-based fitting framework for interatomic potentials for bulk and two-dimensional materials. The code is available for download via https://www.potfit.net