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# Total energy optimisation

The total energy is a functional of the charge density . From equation (10) we see that the charge density is expanded in fine grid delta functions where are the expansion coefficients. Therefore the energy will have a variational dependence on these coefficients provided they form an -representable charge density. Consequently, the energy should also have a variational dependence on the density kernel and the NGWF expansion coefficients since the are constructed from them

 (18)

It is thus sufficient to minimise the energy with respect to and . We must however do this under two constraints. The first is that the number of electrons corresponding to the charge density
 (19)

should remain constant. The second is that the ground state density matrix should be idempotent, or in other words the eigenfunctions of the Kohn-Sham Hamiltonian have to be orthonormal
 (20)

We choose to carry out the total energy minimisation in two nested loops, in a fashion similar to the ensemble DFT method of Marzari et al. [32]. The density kernel will play the role of the generalised occupation numbers and the NWGFs will play the role of the orbitals. So we can reach the minimum energy in two constrained-search stages:
 (21)

with
 (22)

where the minimisation with respect to the density kernel in equation (22) ensures that of equation (21) is a function of the NGWF coefficients only. In practice in equation (22) we do not just minimise the energy with respect to but we also impose the electron number and idempotency constraints (19) and (20). There are a variety of efficient methods for achieving this available in the literature, derived from the need to perform linear-scaling calculations with a localised basis [9,10,33,34,35]. Any of these methods would ensure that the density kernel in (22) adapts to the current NGWFs so that it minimises the energy within the imposed constraints.

In the present work we have used the variant of the Li, Nunes and Vanderbilt (LNV)[9] method that was developed by Millam and Scuseria[36] in calculations with Gaussian basis sets. We emphasise again, though, that any of the other available methods could have been used as well. For simplicity of presentation, our analysis from now on will assume that the energy of equation (22) is minimised without any constraints. In order to take into account the constraints, the formulae we derive will have to be modified according to the density kernel minimisation method one chooses to use. This is a straightforward but tedious exercise [37].

The minimisation of equation (22) can be performed iteratively with the conjugate gradients method [38]. As in the simpler steepest descents method, the essential ingredient is the gradient. It is easy to show [39] that this quantity is equal to twice the matrix elements of the Kohn-Sham Hamiltonian

 (23)

The non-orthogonality of our NGWFs has to be taken into account when computing search directions with the above gradient by transforming it to a contravariant second order tensor [40,41].

The minimisation stage of equation (21) is also performed iteratively with the conjugate gradients method. In this case, one can show by using the properties of the delta function basis set that the gradient is:

 (24)

where is the weight associated with each grid point. Here a contravariant-to-covariant tensor correction is needed when this gradient is used to calculate the search direction during a conjugate gradient step [42]. The functions in general contain contributions from all delta functions of the simulation cell but we wish to keep restricted to its spherical region. For this reason in every minimisation step of (21) we zero all the components of (24) that correspond to delta functions outside the sphere of .

When the minimisation with respect to the density kernel of equation (22) is carried out under the electron number and idempotency constaints, equation (24) contains extra terms as a result of the constraints imposed in (22). These terms ensure that the electron number and idempotency constraints are automatically obeyed in (21) and as a result, the optimisation with respect to the support functions can be carried out in an unconstrained fashion.

Next: The FFT box technique Up: Nonorthogonal generalized Wannier function Previous: Charge density and total
Peter D. Haynes 2002-10-31