Multiscale computational materials science has reached a stage where many complicated phenomena or properties that are of great importance to manufacturing can be predicted or explained. The word “ab initio study” becomes commonplace as the development of density functional theory has enabled the predictions to be independent of experimental data or empirical parameters. For some crucial phenomena, e.g., precipitation processes in multicomponent alloys, however, challenges exist due to the requirement of an accurate and efficient description of both energetics and kinetics of a complex system. In the present thesis, a systematic methodology has been established for predicting the morphology and realistic formation kinetics of precipitates in multicomponent alloys. Aluminum alloys are chosen as prototype applications of the present methodology, because of the well-known strengthening mechanism—age or precipitation hardening which is a typical and important precipitation process utilized in industrial materials. As one of the main computational approaches, cluster expansion technique is applied to study vacancy properties in concentrated Cu-Ni alloys. Diffusion kinetics in dilute Al-Cu alloys including the role of multiple diffusion barriers has been investigated by kinetic Monte Carlo simulations. At finite temperature, electronic entropy contribution to the free energies of the transition metals is also discussed.
|Award date||27 Nov 2017|
|Publication status||Published - 2017|
- Aluminum alloy
- ab initio
- cluster expansion
- kinetic Monte Carlo simulation