Quantum Monte Carlo (QMC) is in principle an exact technique to obtain the ground state properties of a multicomponent quantum system.
We mention here two ways of doing QMC calculation. The first is the so called Variational Monte Carlo (VMC), which is a stochastic way of calculating expectation values of observables with a given trial wavefunction. The second is the Diffusion Monte Carlo (DMC), which is a technique to project out the ground state wavefunction of the system out of a trial wavefunction (provided that the two are not orthogonal).
With VMC one can only obtain exact properties if the trial wavefuction is an exact wavefunction of the system, but for the ground state it is a variational method.
With DMC it is possible, at least in principle, to obtain the exact ground state wavefuction, and therefore the exact ground state properties of the system. In practice, however, it is often necessary to introduce two approximations. The first is the fixed node approximation, which is related to the fermionic nature of the electrons. The wavefunction of a system of fermions changes sign at the nodal surface, and this is usually very difficult to handle as the ground state nodal surface is usually unknown. With the fixed node approximation the nodal surface is fixed to that of the trial wavefuction. In this way the problem is much more manageable, but one only obtains the ground state wavefuction compatible with this constrain. However, the variational principle holds, and the error in the ground state energy is only second order in the error in the nodal surface, being usually very small as very good trial wavefuctions can ofted be produced using Density Functional Theory (DFT) of Hartree Fock (HF) methods. The second approximation is the introduction of pseudopotentials to eliminate the core states from the calculations. There is a pletora of data, mainly based on DFT calculations, showing that the pseudopotential approximation (PP) usually works very well. However, in DMC the use of non-local PP is responsible for an additional error, due to a difficult term present in the evaluation of the energy, which is usually neglected. The error is second order in the error in the trial wavefunction and makes  the DMC energy weakly dependent on it. However, this error is usually very small, and in principle it is possible to estimate its magnitude by using different trial wavefunctions (with the same nodal surface).

DFT methods have had enourmous success since they introduction by Hohenberg and Kohn and Kohn and Sham in the sixties, and in the last few years their applications have been extended also to the calculation of finite temperature properties of liquid and solid. DFT works incredibly well in a large number of cases, and it is particularly suitable when cancellation of errors is expected, like in the comparison of the energy (or the free energy) of similar structures. Our work on the melting temperature of pure iron is a clear example of that, as both solid and liquid iron have a similar close packed structure.
However, the success of DFT is not comprehensive, with known failures for strongly correlated materials like transition metal oxydes for example.  An other example of  non perfect agreement with the experiments is the DFT with the Local Density Approximation calculation of the zero pressure melting point of silicon,  for which agreement with the experiments is only within 20 % (see Sugino and Car, Phys. Rev. Lett., 74, 1823 (1995).)

The goal of our project is to go beyond DFT and start using QMC to calculate the properties of materials, including finite temperature properties with the calculation of free energies. We will concentrate on those problems for which DFT fails. As a test case we have chosen to calculate the melting point of silicon at zero pressure, for which the DFT-LDA error is significantly large.
The melting point will be determined by calculating the free energy of solid and liquid with DFT first, and then adding the small correction QMC - DFT, which will be determined using thermodynamic integration.
This project is being carried out together with Mike Gillan, and involves a collaboration with Mike Towler and Richard Needs at the Cavendish Laboratory in Cambridge. The calculations will be performed with the code CASINO.