摘要
A differential approach for exact fixed-node quantum Monte Carlo calculation was proposed in this paper. This new algorithm can be used to directly compute the energy differential between two systems in exact fixed-node quantum Monte Carlo process, making the statistical error of calculation reduce to order of 10^-2 kJ/mol and recover about more than 90% of the correlation energy. The approach was employed to set up a potential energy surface of a molecule, through a model of rigid move, and Jacobi transformation utilized to make energy calculation for two configurations of a molecule having good positive correlation. So, an accurate energy differential could be obtained, and the potential energy surface with good quality depicted. This novel algorithm was used to study the potential energy curve of the ground state of BH and the potential energy surface of H3, and could be also applied to study other related fields such as molecular spectroscopy and the energy variation of chemical reactions.
A differential approach for exact fixed-node quantum Monte Carlo calculation was proposed in this paper. This new algorithm can be used to directly compute the energy differential between two systems in exact fixed-node quantum Monte Carlo process, making the statistical error of calculation reduce to order of 10^-2 kJ/mol and recover about more than 90% of the correlation energy. The approach was employed to set up a potential energy surface of a molecule, through a model of rigid move, and Jacobi transformation utilized to make energy calculation for two configurations of a molecule having good positive correlation. So, an accurate energy differential could be obtained, and the potential energy surface with good quality depicted. This novel algorithm was used to study the potential energy curve of the ground state of BH and the potential energy surface of H3, and could be also applied to study other related fields such as molecular spectroscopy and the energy variation of chemical reactions.
基金
Project supported by the National Natural Science Foundation of China (No. 20173014) and the Natural Science Foundation of Hunan Province.