An efficient sampling algorithm for variational Monte Carlo.

Anthony Scemama (1) , Tony Lelièvre (2, 1) , Gabriel Stoltz (2, 1) , Eric Cancès (2, 1) , Michel Caffarel (3)

1 CERMICS - Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique
2 MICMAC - Methods and engineering of multiscale computing from atom to continuum
3 LPQ - Laboratoire de Physique Quantique

We propose a new algorithm for sampling the N-body density mid R:Psi(R)mid R:(2)R(3N)mid R:Psimid R:(2) in the variational Monte Carlo framework. This algorithm is based upon a modified Ricci-Ciccotti discretization of the Langevin dynamics in the phase space (R,P) improved by a Metropolis-Hastings accept/reject step. We show through some representative numerical examples (lithium, fluorine, and copper atoms and phenol molecule) that this algorithm is superior to the standard sampling algorithm based on the biased random walk (importance sampling).

Domaines: Chimie théorique et/ou physique

Anthony Scemama, Tony Lelièvre, Gabriel Stoltz, Eric Cancès, Michel Caffarel. An efficient sampling algorithm for variational Monte Carlo.. Journal of Chemical Physics, 2006, 125 (11), pp.114105. ⟨10.1063/1.2354490⟩. ⟨hal-00875620⟩