Sign Learning Kink-based (SiLK) Quantum Monte Carlo for Molecular Systems

Authors

Xiaoyao Ma, Department of Physics and Astronomy, Louisiana State University, Baton RougeFollow
Randall W. Hall, Department of Natural Sciences and Mathematics, Dominican University of CaliforniaFollow
Frank Loffler, Center for Computation and Technology, Louisiana State University, Baton RougeFollow
Karol Kowalski, Pacific Northwest National LaboratoryFollow
Kiran Bhaskaran-Nair, Department of Physics and Astronomy, Louisiana State University, Baton Rouge
Mark Jarrell, Department of Physics and Astronomy, Louisiana State University, Baton RougeFollow
Juana Moreno, Department of Physics and Astronomy, Louisiana State University, Baton RougeFollow

Document Type

Article

Source

The Journal of Chemical Physics

ISSN

0021-9606

Volume

144

Issue

1

First Page

14101-1

Publication Date

2016

Department

Natural Sciences and Mathematics

Abstract

The Sign Learning Kink (SiLK) based Quantum Monte Carlo (QMC) method is used to calculate the ab initioground state energies for multiple geometries of the H₂O, N₂, and F₂ molecules. The method is based on Feynman’s path integral formulation of quantum mechanics and has two stages. The first stage is called the learning stage and reduces the well-known QMC minus sign problem by optimizing the linear combinations of Slater determinants which are used in the second stage, a conventional QMC simulation. The method is tested using different vector spaces and compared to the results of other quantum chemical methods and to exact diagonalization. Our findings demonstrate that the SiLK method is accurate and reduces or eliminates the minus sign problem.

Rights

Copyright 2016, American Institute of Physics

Publisher Statement

This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics.

PubMed ID

26747795