Browsing by Subject "Vacancy diffusion"

High-entropy alloys (HEAs), esteemed for their exceptional resistance to radiation damage, carry considerable potential for deployment within fusion reactors. Nonetheless, due to their compositional complexity, comprehending the diffusion behaviour in these multifaceted alloys continues to be a daunting task. This thesis proposes a novel approach to modelling vacancy diffusion in body-centred cubic (BCC) HEAs, particularly Mo-Nb-Ta-V-W. The methodology involves the tactical application of collective variable-driven hyperdynamics (CVHD) to procure data for training a Gaussian process regression (GPR) and feed-forward neural network (FNN) model. The trained FNN model is subsequently employed within kinetic Monte Carlo (KMC) simulations for accurately predicting jump rates, whereas the GPR model is used to elucidate experimental findings related to the behaviour of vacancies in \mbox{Mo-Nb-Ta-V-W}. The robustness of the FNN model is manifested by its capacity to generalise to unseen data, whilst the efficacy of the overall method is corroborated by Monte Carlo molecular dynamics (MCMD) simulations. The CVHD methodology, uniquely capable of functioning at finite temperatures, can capture the entropic contribution to the free energy and model kinematics explicitly. This singular ability facilitates a more comprehensive understanding of the system's behaviour under authentic conditions. The findings presented in this thesis signify a considerable stride forward in the study of HEAs, providing a robust framework for the design of advanced materials. These results underscore the potential of the CVHD-trained FNN-KMC methodology in exploring complex environments, thereby establishing a firm foundation for future investigations and emphasising the need for its continued refinement and augmentation.