A spin-adapted Density Matrix Renormalization Group algorithm for   quantum chemistry
  We extend the spin-adapted density matrix renormalization group (DMRG) algorithm of McCulloch and Gulacsi [Europhys. Lett.57, 852 (2002)] to quantum chemical Hamiltonians. This involves two key modifications to the non-spin-adapted DMRG algorithm: the use of a quasi-density matrix to ensure that the renormalised DMRG states are eigenvalues of $S^2$ , and the use of the Wigner-Eckart theorem to greatly reduce the overall storage and computational cost. We argue that the advantages of the spin-adapted DMRG algorithm are greatest for low spin states. Consequently, we also implement the singlet-embedding strategy of Nishino et al [Phys. Rev. E61, 3199 (2000)] which allows us to target high spin states as a component of a mixed system which is overall held in a singlet state. We evaluate our algorithm on benchmark calculations on the Fe$_2$S$_2$ and Cr$_2$ transition metal systems. By calculating the full spin ladder of Fe$_2$S$_2$ , we show that the spin-adapted DMRG algorithm can target very closely spaced spin states. In addition, our calculations of Cr$_2$ demonstrate that the spin-adapted algorithm requires only roughly half the number of renormalised DMRG states as the non-spin-adapted algorithm to obtain the same accuracy in the energy, thus yielding up to an order of magnitude increase in computational efficiency. 
Sandeep Sharma and Garnet Kin-Lic Chan
physics.chem-ph cond-mat.str-el physics.comp-ph