Peter Brommer

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.

Classical interaction potentials for diverse materials from ab initio data : a review of potfit

Force matching is an established technique to generate effective potentials for molecular dynamics simulations from first-principles data. This method has been implemented in the open source code potfit. Here, we present a review of the method and describe the main features of the code. Particular emphasis is placed on the features added since the initial release: interactions represented by analytical functions, differential evolution as optimization method, and a greatly extended set of interaction models. Beyond the initially present pair and embedded-atom method potentials, potfit can now also optimize angular dependent potentials, charge and dipolar interactions, and electron-temperature-dependent potentials. We demonstrate the functionality of these interaction models using three example systems: phonons in type I clathrates, fracture of {\alpha}-alumina, and laser-irradiated silicon.

Embedded atom method potentials for Al-Pd-Mn phases

A novel embedded atom method (EAM) potential for the Ξ phases of Al-Pd-Mn has been determined with the force-matching method. Different combinations of analytic functions were tested for the pair and transfer part. The best results are obtained if one allows for oscillations on two different length scales. These potentials stabilize structure models of the Ξ phases and describe their energy with high accuracy. Simulations at temperatures up to 1200 K show very good agreement with ab initio results with respect to stability and dynamics of the system.

Vibrational properties of MgZn2

Abstract We present here simulation results on the dynamical structure factor of the C14 Laves Phase of MgZn2, the simplest of the Mg–(Al,Zn) Frank-Kasper alloy phases. The dynamical structure factor was determined in two ways. Firstly, the dynamical matrix was obtained in harmonic approximation from ab-initio forces. The dynamical structure factor can then be computed from the eigenvalues of the dynamical matrix. Alternatively, Molecular Dynamics simulations of a larger sample were used to measure the correlation function corresponding to the dynamical structure factor. Both results are compared to data from neutron scattering experiments. This comparison also includes the intensity distribution, which is a very sensitive test. We find that the dynamical structure factor determined with either method agrees reasonably well with the experiment. In particular, the intensity transfer from acoustic to optic phonon modes can be reproduced correctly. This shows that simulation studies can complement phonon dispersion measurements.

Molecular Dynamics Simulations with Long-Range Interactions

The Wolf summation (Wolf et al., J. Chem. Phys. 110, 8254 (1999)), an order O(N) method for the calculation of long-range interactions, has been adapted successfully to the simulation of metal oxides. We present the combination of the method with the Tangney–Scandolo model for polarizable oxygen and the Streitz–Mintmire model for variable charges at metal oxide–metal interfaces. The methods have been implemented successfully in our molecular dynamics package IMD and applied to the structure of several metal oxides. The new methods allow for the simulation of cracks in oxides and the study of the flexoelectricity effect.

Probing Potential Energy Surface Exploration Strategies for Complex Systems

The efficiency of minimum-energy configuration searching algorithms is closely linked to the energy landscape structure of complex systems, yet these algorithms often include a number of steps of which the effect is not always clear. Decoupling these steps and their impacts can allow us to better understand both their role and the nature of complex energy landscape. Here, we consider a family of minimum-energy algorithms based, directly or indirectly, on the well-known Bell-Evans-Polanyi (BEP) principle. Comparing trajectories generated with BEP-based algorithms to kinetically correct off-lattice kinetic Monte Carlo schemes allow us to confirm that the BEP principle does not hold for complex systems since forward and reverse energy barriers are completely uncorrelated. As would be expected, following the lowest available energy barrier leads to rapid trapping. This is why BEP-based methods require also a direct handling of visited basins or barriers. Comparing the efficiency of these methods with a thermodynamical handling of low-energy barriers, we show that most of the efficiency of the BEP-like methods lie first and foremost in the basin management rather than in the BEP-like step.

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

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.

Epitaxial growth on a dynamically rough substrate: A Monte Carlo model of graphene / Cu(111)

A new mechanism in sub-monolayer epitaxial growth is demonstrated using Monte Carlo simulation. The model describes well the growth of graphene on nearly-molten Cu(111) surfaces, as typically used in chemical vapour deposition (CVD). The interaction strength between C and Cu varies randomly, and the variations are allowed to migrate from site to site. Counter-intuitively, this “dynamic roughness” actually enhances graphene island sizes, in a way which cannot be mimicked by changing other model parameters. In contrast, static roughness strongly inhibits island growth. This new mechanism functions through a destabilisation of small islands, allowing them to break up and join larger islands which are themselves minimally affected by the underlying roughness. A comparison of the model with experimental CVD trends will be given along with its potential for application to compound semiconductor epitaxial growth.