Data sets and trained neural networks for Cu migration barriers.

Kinetic Monte Carlo (KMC) is an efficient method for studying diffusion. A limiting factor to the accuracy of KMC is the number of different migration events allowed in the simulation. Each event requires its own migration energy barrier. The calculation of these barriers may be unfeasibly expensive. In this article we present a data set of migration barriers on for nearest-neighbour jumps on the Cu surfaces, calculated with the nudged elastic band (NEB) method and the tethering force approach. We used the data to train artificial neural networks (ANN) in order to predict the migration barriers for arbitrary nearest-neighbour Cu jumps. The trained ANNs are also included in the article. The data is hosted by the CSC IDA storage service.

Growth mechanism for nanotips in high electric fields.

In this work we show using atomistic simulations that the biased diffusion in high electric field gradients creates a mechanism whereby nanotips may start growing from small surface asperities. It has long been known that atoms on a metallic surface have biased diffusion if electric fields are applied and that microscopic tips may be sharpened using fields, but the exact mechanisms have not been well understood. Our Kinetic Monte Carlo simulation model uses a recently developed theory for how the migration barriers are affected by the presence of an electric field. All parameters of the model are physically motivated and no fitting parameters are used. The model has been validated by reproducing characteristic faceting patterns of tungsten surfaces that have in previous experiments been observed to only appear in the presence of strong electric fields. The growth effect is found to be enhanced by increasing fields and temperatures.

Data sets of migration barriers for atomistic Kinetic Monte Carlo simulations of Cu self-diffusion via first nearest neighbour atomic jumps.

Atomistic rigid lattice Kinetic Monte Carlo (KMC) is an efficient method for simulating nano-objects and surfaces at timescales much longer than those accessible by molecular dynamics. A laborious and non-trivial part of constructing any KMC model is, however, to calculate all migration barriers that are needed to give the probabilities for any atom jump event to occur in the simulations. We have calculated three data sets of migration barriers for Cu self-diffusion with two different methods. The data sets were specifically calculated for rigid lattice KMC simulations of copper self-diffusion on arbitrarily rough surfaces, but can be used for KMC simulations of bulk diffusion as well.

Data sets of migration barriers for atomistic Kinetic Monte Carlo simulations of Fe self-diffusion.

Atomistic rigid lattice Kinetic Monte Carlo (KMC) is an efficient method for simulating nano-objects and surfaces at timescales much longer than those accessible by molecular dynamics. A laborious and non-trivial part of constructing any KMC model is, however, to calculate all migration barriers that are needed to give the probabilities for any atom jump event to occur in the simulations. We calculated three data sets of migration barriers for Fe self-diffusion: barriers of first nearest neighbour jumps, second nearest neighbours hop-on jumps on the Fe {100} surface and a set of barriers of the diagonal exchange processes for various cases of the local atomic environments within the 2nn coordination shell.

Au nanowire junction breakup through surface atom diffusion.

Metallic nanowires are known to break into shorter fragments due to the Rayleigh instability mechanism. This process is strongly accelerated at elevated temperatures and can completely hinder the functioning of nanowire-based devices like e.g. transparent conductive and flexible coatings. At the same time, arranged gold nanodots have important applications in electrochemical sensors. In this paper we perform a series of annealing experiments of gold and silver nanowires and nanowire junctions at fixed temperatures 473, 673, 873 and 973 K (200 °C, 400 °C, 600 °C and 700 °C) during a time period of 10 min. We show that nanowires are especially prone to fragmentation around junctions and crossing points even at comparatively low temperatures. The fragmentation process is highly temperature dependent and the junction region breaks up at a lower temperature than a single nanowire. We develop a gold parametrization for kinetic Monte Carlo simulations and demonstrate the surface diffusion origin of the nanowire junction fragmentation. We show that nanowire fragmentation starts at the junctions with high reliability and propose that aligning nanowires in a regular grid could be used as a technique for fabricating arrays of nanodots.

Formation Mechanism of Fe Nanocubes by Magnetron Sputtering Inert Gas Condensation.

In this work, we study the formation mechanisms of iron nanoparticles (Fe NPs) grown by magnetron sputtering inert gas condensation and emphasize the decisive kinetics effects that give rise specifically to cubic morphologies. Our experimental results, as well as computer simulations carried out by two different methods, indicate that the cubic shape of Fe NPs is explained by basic differences in the kinetic growth modes of {100} and {110} surfaces rather than surface formation energetics. Both our experimental and theoretical investigations show that the final shape is defined by the combination of the condensation temperature and the rate of atomic deposition onto the growing nanocluster. We, thus, construct a comprehensive deposition rate-temperature diagram of Fe NP shapes and develop an analytical model that predicts the temporal evolution of these properties. Combining the shape diagram and the analytical model, morphological control of Fe NPs during formation is feasible; as such, our method proposes a roadmap for experimentalists to engineer NPs of desired shapes for targeted applications.