Abstract
Metallic nanoparticles (clusters) are collections of metallic atoms that are bound together to form structures between 1 - 500 nm in diameter. Metallic clusters have potential in many practical applications due to their size dependent chemical and physical properties. In order to fully utilise their potential, it is vital that the various structural features that clusters can exhibit are understood. This thesis explores the various ways that the structures of clusters are studied computationally. This includes:
- Elucidating the structures of Au and Pt clusters between 55 and 309 atoms in size and comparing these to experimental results,
- Creating an auxiliary method for improving the explorational ability of the potential energy surface by global optimisation algorithms towards the global minimum,
- Investigating the structures of Au55, Au85, and Au101 clusters over time using dynamical simulation methods, and
- Understanding the structures of Cu clusters between 55 and 147 atoms in size and their electrocatalytic properties towards the reduction of CO2 towards biofuels.