

Author: Scemama Anthony Caffarel Michel
Publisher: NRC Research Press
ISSN: 1480-3291
Source: Canadian Journal of Chemistry, Vol.91, Iss.9, 2013-01, pp. : 879-885
Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.
Abstract
Defining accurate and compact trial wavefunctions leading to small statistical and fixed-node errors in quantum Monte Carlo (QMC) calculations is still a challenging problem. Here we propose to make use of selected configuration interaction (CI) expansions obtained by selecting the most important determinants through a perturbative criterion. A major advantage with respect to truncated CASSCF wavefunctions or CI expansions limited to a maximum number of excitations (e.g, CISD) is that much smaller expansions can be considered (many unessential determinants are avoided), an important practical point for efficient QMC calculations. The most important determinants entering first during the selection process (hierarchical construction) the main features of the nodal structure of the wavefunction can be expected to be obtained with a moderate number of determinants. Thanks to this property, the delicate problem of optimizing in a Monte Carlo framework the numerous linear and (or) nonlinear parameters of the determinantal part of the trial wavefunction could be avoided. As a first numerical example, the calculation of the ground-state energy of the oxygen atom is presented. The best DMC value reported so far is obtained.
Related content


By Moon Hyun Ho Lee Jong Joo Choi Sang Yule Cha Jae Sang Kang Jang Mook Kim Jong Tae Shin Myong Chul
Sensors, Vol. 11, Iss. 8, 2011-08 ,pp. :




Evaluation and Comparison of the Configuration Interaction Calculations for Complex Atoms
Atoms, Vol. 2, Iss. 1, 2014-03 ,pp. :


Variational quantum Monte-Carlo method in surface physics
By Schattke W. Bahnsen R. Redmer R.
Progress in Surface Science, Vol. 72, Iss. 5, 2003-08 ,pp. :


A survey on pure sampling in quantum Monte Carlo methods
Canadian Journal of Chemistry, Vol. 91, Iss. 7, 2013-05 ,pp. :