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I am a PhD student at TCM in Dr G Conduit's group, and I have been here since January 2017. I am working in the fields of quantum physics, statistical mechanics, collective phenomenam, materials design, and magnetotransport.
Since Feburary 2017, Dr V Narayan, J Dann, Dr G Conduit and I have started to work together on novel magneto-transport properties that have been observed in thin-films with strong spin-orbit coupling.
In my collaboration with Dr L Schonenberg and Dr G Conduit since January 2017, I work on the theory of the BEC-BCS cross-over in strongly-interacting Fermions, most prominently investigated in cold-atom physics, where the interactions can be tuned using the Feshbach resonance. In our work, we use Quantum Monte-Carlo simulations to compute a number of properties of the ground-state wave-function for varying effective range, which is a parameter for the interaction potential.
Since October 2016, I have joined Dr G Conduit's work in applying machine learning techniques to large materials databases, and use it to make predictions for new materials.
Before starting my PhD, I have been working on my Master thesis at TCM under the supervision of Dr C Castelnovo. Since October 2015, we have been working together on the three-colouring model with colour-dependent interactions (see image). We are trying to find the central charge of the conformal field theory describing the long-wavelength limit of the system for the whole phase diagram. We obtain the central charge from finite-size scaling of the free energy, which can be determined using the transfer-matrix method. The three-colouring model can be used to describe the frustrated ground-states of a 3-state anti-ferromagnetic Potts-model, as well as arrays of Josephson junctions, and networks of superconducters on a kagomé lattice.
In Plain English
While physicists have been able to gain a deep understanding of the theories which describe the basic principles of physics in the world, and which determine the behaviour of solid-state objects that surround us in our everyday life (e.g. electro dynamics, quantum mechanics), drawing conclusions from these theories, and making predictions for solid-state objects turns out to be anything but simple.
In my PhD, I work on understanding solid-state physics and related topics using a broad variety of methods. I am working on techniques using artificial intelligence (i.e. neural networks) to capture correlations between materials properties, and make predictions for new materials. I also contribute to theortical work in cold-atom physics, where atoms are trapped using laser beams, and are studied as a toy-model to make predictions for real solids. Finally, I have work going on in analysing the electric transport properties of thin films subject to strong magnetic fields. All the work that I do is either based on pen-and-paper derivations, or requires the excessive use of numerical simulations on high-performance computer clusters.