Carla Verdi

Thermodynamic properties of atomic defects for quantum technologies

Atomic defects in solids are one of the most promising single-photon sources or 'quantum emitters', an important building block for many quantum technologies. In order to design and engineer better quantum emitters, a fundamental understanding of their optical and electronic properties, as well as defect formation and migration, is essential. In this project, first-principles quantum mechanical calculations combined with machine-learning techniques are used in order to uncover key properties such as defect dynamics, formation mechanisms, free energies and stabilities at room and elevated temperatures. The theoretical insights gained in the project aim to inform the design of atomic defects systems for tailored applications as quantum emitters. The student will gain experience with high-performance computing and materials simulation methods, in particular first-principles methods and machine-learned potentials.

Atomistic modelling of solid surfaces and 2D structures

Density functional theory (DFT) is a prominent tool that enables the simulation of materials and molecules at the atomic scale 'from first principles', i.e., without relying on empirical data. To underscore its importance in modern materials physics and beyond, it should suffice to mention that 12 papers on the top-100 list of the most-cited papers of all time, including 2 of the top 10, are all related to DFT. In this project, first-principles DFT calculations will be used to investigate and characterise the structural and electronic properties of 2D structures and solid surfaces. These properties can be directly compared to experimental data, such as scanning tunneling microscopy (STM) experiments conducted in SMP. Target systems include solvated molecules on alkali halide structures, perovskite materials for next-gen solar cells, and oxide structures on metal superconductors.

The student will gain experience with widely used first-principles materials modelling software and high-performance computing.

Superconductivity in hyperdoped germanium from first principles

Semiconductors like silicon and germanium are fundamental to electronic devices. Superconductivity can be realised in these semiconductors through heavy doping (or 'hyperdoping'), which involves introducing a high concentration of dopants into the material. This emerging class of 'superconducting semiconductors' presents an exciting new platform for integrated quantum electronics. However, to engineer their properties effectively, an atomistic understanding of the origin of superconductivity is essential. This project aims to elucidate the superconducting mechanisms in hyperdoped germanium crystals using first-principles calculations based on density-functional theory and Migdal-Eliashberg theory. The student will gain expertise in state-of-the-art materials modelling software, electron-phonon physics, and high-performance computing.

Thermodynamic properties of atomic defects for quantum technologies

Atomic defects in solids are one of the most promising single-photon sources or 'quantum emitters', an important building block for many quantum technologies. In order to design and engineer better quantum emitters, a fundamental understanding of their optical and electronic properties, as well as defect formation and migration, is essential. In this project, first-principles quantum mechanical calculations combined with machine-learning techniques are used in order to uncover key properties such as defect dynamics, formation mechanisms, free energies and stabilities at room and elevated temperatures. The theoretical insights gained in the project aim to inform the design of atomic defects systems for tailored applications as quantum emitters. The student will gain experience with high-performance computing and materials simulation methods, in particular first-principles methods and machine-learned potentials.