Theory of Condensed Matter
About this community
Carries out world leading research, broadly categorised under: collective quantum phenomena, first principles quantum mechanical methods, and soft matter
The Theory of Condensed Matter Group carries out world leading research broadly categorised under the headings of:
- Collective quantum phenomena
- First principles quantum mechanical methods
- Soft matter
Our research interests constantly evolve to address new theoretical challenges, some of which arise from novel experiments performed in the Cavendish and elsewhere. For instance, Payne and co-workers have developed ONETEP - a quantum mechanical code that offers state of the art accuracy but whose cost scales only linearly with system size - which allows quantum mechanical simulations of biological systems.
Our group has an outstanding track record for nurturing and training young researchers and sixteen members of the group have taken up permanent academic appointments within the last five years.
The group is part of the Condensed Matter Theory Portfolio Partnership activity between chemists and physicists in modelling molecular behaviour that underpins technologies as various as chemical catalysis, semiconductor lasers, and drug design.
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(APS, 2018-04-16)We study the fate of quantum correlations at finite temperature in the two dimensional toric code using the logarithmic entanglement negativity. We are able to obtain exact results that give us insight into how thermal ...
(American Physical Society, 2018-01-29)We analyze scaling behaviors of simulated annealing carried out on various classical systems with topological order, obtained as appropriate limits of the toric code in two and three dimensions. We first consider the ...
(APS, 2018-04-03)We describe the theory of the dynamics of atoms in two-dimensional quasicrystalline optical lattices. We focus on a regime of shallow lattice depths under which the applied force can cause Landau-Zener tunnelling past ...
(Elsevier, 2018-05-01)We apply an artificial neural network to model and verify material properties. The neural network algorithm has a unique capability to handle incomplete data sets in both training and predicting, so it can regard properties ...