Browsing by Author "Makkonen, Eetu Petter"

Vacuum breakdown is a limiting factor in the design of powerful and cost-efficient particle accelerators. Modern models have suggested that the rate of breakdowns is driven by dislocation dynamics in the electrode materials suffering from breakdowns. In order to understand why specifically the copper-2wt%beryllium alloy outperforms other electrode materials in vacuum breakdown rate and maximum electric fields in breakdown experiments at CERN, a new machine-learning interatomic potential (ML-IAP) for the CuBe alloy was developed. Density functional theory (DFT) was used in calculating a dataset of atomic forces, energies, and virials for a set of CuBe structures. This dataset was performed a fit on with Gaussian process regression, producing an IAP with close-to-DFT accuracy in its intended use cases. With the developed IAP, the interactions between single interstitial beryllium atoms and edge dislocations in a face-centered cubic (FCC) copper matrix were studied with molecular dynamics (MD). It was found that beryllium atoms bind to the edge dislocations, inhibiting their mobility under shear stress. Furthermore, beryllium atoms were found to increase the intrinsic stacking fault energy of FCC copper, possibly leading to an increase in dislocation mobility. These two findings suggest that beryllium atoms could increase copper's resistance to vacuum breakdown mainly via trapping dislocations. Future studies could look at how precipitates of beryllium, or other alloys of copper, play a role in dislocation dynamics.