Sign Learning Kink based Quantum Monte Carlo Applied to Multiple Large Systems H₂O, N₂, F₂

Identifier

etd-07102015-163850

Author

Xiaoyao Ma, Louisiana State University and Agricultural and Mechanical CollegeFollow

Degree

Doctor of Philosophy (PhD)

Department

Physics and Astronomy

Document Type

Dissertation

Abstract

A Sign Learning Kink (SiLK) based Quantum Monte Carlo (QMC) method is used to calculate the ground state energies for H₂O, N₂ and F₂ molecules. This method has two stages. The first (learning stage) reduces the minus sign problem by optimizing the states which are used in the second (QMC stage). I test the method in Single, Double excitations (SD), Single, Double, and Triple excitations (SDT), and Full Configuration Interaction (FCI) vector spaces. I also perform exact diagonalization in those vector spaces as a benchmark. In each vector space and for each molecule, I perform SiLK QMC for different bond lengths demonstrating that the SiLK QMC is applicable to many systems.

Date

2014

Document Availability at the Time of Submission

Release the entire work immediately for access worldwide.

Recommended Citation

Ma, Xiaoyao, "Sign Learning Kink based Quantum Monte Carlo Applied to Multiple Large Systems H₂O, N₂, F₂" (2014). LSU Doctoral Dissertations. 1728.
https://repository.lsu.edu/gradschool_dissertations/1728

Committee Chair

Jarrell, Mark

DOI

10.31390/gradschool_dissertations.1728