Modeling and numerical study of the diffusion of point defects in α-iron
Iron and iron-based alloys are of considerable interest to the materials modelling community because of the immense technological importance of steel. Iron-based ferritic alloys are widely used in aeronautic and nuclear industries due to their high mechanical strength, low expansion at high temperatures, and corrosion resistance. These properties are affected by intrinsic and extrinsic point defects, however. In this thesis, we describe in detail the kinetics of point defects in α-iron using the kinetic activation-relaxation technique (k-ART), an off-lattice kinetic Monte Carlo method with on-the-fly catalog building. More specifically, we focus on the diffusion mechanisms of carbon and vacancy clusters in α-iron. First, we study the pressure effect on carbon diffusion in the grain boundary (GB) of α-iron. We find that the effect of pressure can strongly modify the C stability and diffusivity in the GB in ways that depend closely on the local environment and the nature of the deformation. This can have a major impact on the evolution of heterogeneous materials, with variations of local pressure that would strongly alter diffusion across the material. We also study the structural evolution of vacancy clusters containing two to eight vacancies in α-iron. We describe in detail the energy landscape, overall kinetics, and diffusion mechanisms associated with these defects. Our results show complex scattering mechanisms even for defects as simple as small vacancy clusters. Finally, in the last chapter, we discuss a local basin approach to managing low-barrier events in the k-ART. Kinetic Monte Carlo simulations become inefficient in systems where the energy landscape consists of basins with numerous states connected by very low energy barriers compared to those needed to leave these basins. As the system evolves state by state, it is much more likely to perform repeated events (so-called flickers) inside the trapping energy basin than to escape the basin. Such flickers do not progress the simulation and provide little insight beyond the first identification of those states. Our local basin algorithm detects, on the fly, groups of flickering states and consolidates them into local basins, which we treat with the basin-auto-constructing Mean Rate Method (bac-MRM), a master equation-like approach based on the mean-rate method
