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Kinetic Monte Carlo (KMC) is a powerful method for simulation of diffusion processes in various systems. The accuracy of the method, however, relies on the extent of details used for the parameterization of the model. Migration barriers are often used to describe diffusion on atomic scale, but the full set of these barriers may become easily unmanageable in materials with increased chemical complexity or a large number of defects. This work is a feasibility study for applying a machine learning approach for Cu surface diffusion. We train an artificial neural network on a subset of the large set of 2 26 barriers needed to correctly describe the surface diffusion in Cu. Our KMC simulations using the obtained barrier predictor show sufficient accuracy in modelling processes on the low-index surfaces and display the correct thermodynamical stability of these surfaces.

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. Keywords: Copper, Iron, Kinetic Monte Carlo, Surface diffusion, Rigid lattice, Migration barriers, Atomic jumps

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.

We have calculated the migration barriers for surface diffusion on Tungsten. Our results form a self-sufficient parameterization for Kinetic Monte Carlo simulations of arbitrarily rough atomic tungsten surfaces, as well as nanostructures such as nanotips and nanoclusters. The parameterization includes first- and second-nearest neighbour atom jump processes, as well as a third-nearest neighbour exchange process. The migration energy barriers of all processes are calculated with the Nudged Elastic Band method. The same attempt frequency for all processes is found sufficient and the value is fitted to Molecular Dynamics simulations. The model is validated by correctly simulating with Kinetic Monte Carlo the energetically favourable W nanocluster shapes, in good agreement with Molecular Dynamics simulations.

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.

Timber industry and export are an important part of Estonian economy, making affordable industrial scale equipment an important investment for small or starting companies. These companies often develop on-site equipment for wood processing and drying, utilizing pre-existing infrastructure to minimize cost and risk. However, under these conditions custom design of the wood drying kilns is often required.In the present study, a finite element simulation based approach is used to simulate and optimize the industrial wood drying process and the design of the custom-made kilns in a multiscale-multiphysics modeling framework. Air flow is calculated by the Navier-Stokes equations or ?-e turbulence model followed by heat transport in the solid and gas phase and moisture dynamics in wood and air. The dense packing of the processed materials is handled by utilizing a porous media approach and homogenization procedure, leading to effective simulations of the moisture and heat balance.Multiphysics-multiscale simulations are successfully adapted to optimize the industrial design of wood drying kilns. The optimization of the kiln design is achieved by estimating the necessary ventilating power and ensuring homogeneous drying of the processed material.

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 degrees C, 400 degrees C, 600 degrees C and 700 degrees 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.

Atomistic rigid lattice Kinetic Monte Carlo is an efficient method for simulating nano-objects and surfaces at timescales much longer than those accessible by molecular dynamics. A laborious part of constructing any Kinetic Monte Carlo 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. One of the common methods of barrier calculations is Nudged Elastic Band. The number of barriers needed to fully describe simulated systems is typically between hundreds of thousands and millions. Calculations of such a large number of barriers of various processes is far from trivial. In this paper, we will discuss the challenges arising during barriers calculations on a surface and present a systematic and reliable tethering force approach to construct a rigid lattice barrier parameterization of face-centred and body-centred cubic metal lattices. We have produced several different barrier sets for Cu and for Fe that can be used for KMC simulations of processes on arbitrarily rough surfaces. The sets are published as Data in Brief articles and available for the use.

Onset of vacuum arcing near a metal surface is often associated with nanoscale asperities, which may dynamically appear due to different processes ongoing in the surface and subsurface layers in the presence of high electric fields. Thermally activated processes, as well as plastic deformation caused by tensile stress due to an applied electric field, are usually not accessible by atomistic simulations because of the long time needed for these processes to occur. On the other hand, finite element methods, able to describe the process of plastic deformations in materials at realistic stresses, do not include surface properties. The latter are particularly important for the problems where the surface plays crucial role in the studied process, as for instance, in the case of plastic deformations at a nanovoid. In the current study by means of molecular dynamics (MD) and finite element simulations we analyse the stress distribution in single crystal copper containing a nanovoid buried deep under the surface. We have developed a methodology to incorporate the surface effects into the solid mechanics framework by utilizing elastic properties of crystals, pre-calculated using MD simulations. The method leads to computationally efficient stress calculations and can be easily implemented in commercially available finite element software, making it an attractive analysis tool.

High electric fields in particle accelerators cause vacuum breakdowns in the accelerating structures. The breakdowns are thought to be initiated by the modification of material surface geometry under high electric fields. These modifications in the shape of surface protrusions enhance the electric field locally due to the increased surface curvature. Using molecular dynamics, we simulate the behaviour of Cu containing a near-surface Fe precipitate under a high electric field. We find that the presence of a precipitate under the surface can cause the nucleation of dislocations in the material, leading to the appearance of atomic steps on the surface. Steps resulting from several precipitates in close proximity can also form protruding plateaus. Under very high external fields, in some cases, we observed the formation of voids above or below the precipitate, providing additional dislocation nucleation sites.

We present a credible mechanism of spontaneous field emitter formation in high electric field applications, such as Compact Linear Collider in CERN (The European Organization for Nuclear Research). Discovery of such phenomena opens new pathway to tame the highly destructive and performance limiting vacuum breakdown phenomena. Vacuum breakdowns in particle accelerators and other devices operating at high electric fields is a common problem in the operation of these devices. It has been proposed that the onset of vacuum breakdowns is associated with appearance of surface protrusions while the device is in operation under high electric field. Moreover, the breakdown tolerance of an electrode material was correlated with the type of lattice structure of the material. Although biased diffusion under field has been shown to cause growth of significantly field-enhancing tips starting from initial nm-size protrusions, the mechanisms and the dynamics of the growth of the latter have not been studied yet. In the current paper we conduct molecular dynamics simulations of nanocrystalline copper surfaces and show the possibility of protrusion growth under the stress exerted on the surface by an applied electrostatic field. We show the importance of grain boundaries on the protrusion formation and establish a linear relationship between the necessary electrostatic stress for protrusion formation and the temperature of the system. Finally, we show that the time for protrusion formation decreases with the applied electrostatic stress, we give the Arrhenius extrapolation to the case of lower fields, and we present a general discussion of the protrusion formation mechanisms in the case of polycrystalline copper surfaces.