A new understanding of the damage formation mechanisms in Si is developed and investigated over an extended range of ion energy, dose, and irradiation temperature. A simple model for dealing with ion-induced damage is proposed, which is shown to be applicable over the range of implantation conditions. In particular the concept of defect "excesses" will be discussed. An excess exists in the lattice when there is a local surplus of one particular type of defect, such as an interstitial, over its complimentary defect (i.e., a vacancy). Mechanisms for producing such excesses by implantation will be discussed. The basis of this model specifies that accumulation of stable lattice damage during implantation depends upon the excess defects and not the total number of defects. The excess defect model is validated by fundamental damage studies involving ion implantation over a range of conditions. Confirmation of the model is provided by comparing damage profiles after implantation with computer simulation results. It will be shown that transport of ions in matter (TRIM) can be used effectively to model the ion-induced damage profile, i.e. excess defect distributions, by a simple subtraction process in which the spatially correlated defects are removed, thereby simulating recombination. Classic defect studies illuminate defect interactions from concomitant implantation of high- and medium-energy Si+-self ions. Also, the predictive quality of the excess defect model was tested by applying the model to develop several experiments to engineer excess defect concentrations to substantially change the nature and distribution of the defects. Not only are the excess defects shown to play a dominant role in defect-related processing issues, but their manipulation is demonstrated to be a powerful tool in tailoring the implantation process to achieve design goals. Pre-amorphization and dual implantation of different energetic ions are two primary investigative tools used in this work. Various analyses, including XTEM, RBS/channeling, PAS, and SIMS, provided experimental verification of the excess defect model disseminated within this dissertation.

The irradiation of crystalline materials in environments such as nuclear reactors leads to the accumulation of micro and nano-scale defects with a negative impact on material properties such as strength, corrosion resistance, and dimensional stability. Point defects in the crystal lattice, the vacancy and self-interstitial, form the basis of this damage and are capable of migrating through the lattice to become part of defect clusters and sinks, or to annihilate themselves. Recently, attention has been given to HEAs for fusion and fission components, as some materials of this class have shown resilience to irradiation-induced damage. The ability to predict defect diffusion and accelerate simulations of defect behaviour in HEAs using ML techniques is consequently a subject that has gathered significant interest. The goal of this work was to produce an unsupervised neural network capable of learning the interatomic dynamics within a specific HEA system from MD data in order to create a KMC type predictor of defect diffusion paths for common point defects in crystal systems such as the vacancy and self-interstitial. Self-interstitial defect states were identified and purified from MD datasets using graph-isomorphism, and a proof-of-concept model for the HEA environment was used with several interaction setups to demonstrate the feasibility of training a GCN to predict vacancy defect transition rates in the HEA crystalline environment.