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An investigation into self assembled super-lattices of strongly correlated adatoms on metallic surfaces through

Student thesis: Doctoral ThesisDoctor of Philosophy

We use dynamical mean field theory to investigate the nature of self assembled
super-lattices of strong correlated adatoms on metallic surfaces, motivated
by the realisation of a Ce super-lattice on an Ag(111) surface, which
invites interest as a significant step in our understanding and manipulation
of complex nano-scale systems and in the development of technological
applications such as atomic scale memory devices.
We build upon previous tight binding studies by using the Anderson impurity
model to describe a collection of one band strongly correlated impurities
on a surface, which exhibit an on-site Coulomb repulsion U when doubly
occupied and can hybridise with the adsorbed surface states. We set out
the DMFT framework used, explaining how we employ the Hubbard 1 approximation and exact diagonalisation impurity solvers to self consistently
include the strong Hubbard U interaction. We discuss the mathematical
methods used in the course of our calculations and the computational techniques which make our simulations more time and memory efficient.
Our results explore the effect that changing numerous model parameters
such as U, total electron density and adatom separation has on several
impurity quantities, including occupation, magnetic moment and effective
mass/Z function. We consider spectral functions and band structures to
gain insight into changes in system behaviour due to these variations. We
find that the system contains many competing interactions which produce
a complex array of phenomena.
We conclude that our work gives insight into the behaviour of self assembled
super-lattices, suggesting the inclusion of U is important for understanding
the rarity of such systems. We discuss the myriad ways in which this topic
should be further studied and outline the future work to be done in improving
this method and applying it to diverse problems such as disorder and
cluster effects such as RKKY.
Original languageEnglish
Awarding Institution
Award date2017


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