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This group is led by Mike Payne,
with Volker Heine, Danny Cole
and Nick Hine.
The accurate modelling of atomistic processes requires the solution
of the equations of quantum mechanics. However, for systems of many
interacting particles, these equations are far too difficult to solve
exactly, even using the most powerful supercomputers. Methods based on
density-functional theory (DFT) employ well-controlled approximations
which make the calculations tractable but without the need for a
priori assumptions which would compromise the first-principles
approach.
CASTEP, one
of the earliest DFT codes, was originally developed in
TCM. It has since been completely rewritten and is now
maintained by a group associated with TCM. More recent research has focused on
ONETEP, which is a modern
linear-scaling DFT code, using localised orbitals to describe the single-particle
density matrix and achieving computational effort that scales only linearly
with system size.
CASTEP and
ONETEP are both freely available to all UK academics, and are also
commercially available from
Accelrys as part of the
Materials Studio
Package. Other research in the group focuses on developing new
tools for use in combination with DFT, such as the calculation of NMR
spectra and hybrid schemes coupling DFT and molecular mechanics.
As well as providing insight into complex processes occuring in
technologically important materials and
biological molecules, DFT
calculations can also predict the properties of new or hypothetical
systems. We are committed to pushing back the boundaries of
applications, making use of large scale simulations enabled by
supercomputing resources such as the Cambridge
High Performance
Computing Service and the UK
National Supercomputing Service (HECToR). The TCM DFT Users and
Developers Group meets fortnightly throughout the year.
Recent examples of our work include studies of low-speed fracture in silicon
crystals (pictured right), defects and structural properties of carbon nanotubes
and electronic properties of DNA (pictured below).
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