Date of Award

5-2018

Thesis Date

5-2018

Degree Type

Honors Thesis-Unrestricted

Degree Name

B.S.

Department

Physics

Degree Program

Physics

Director

Steven Rick

Abstract

Carleo and Troyer [3] have recently pointed out the possibility of solving quantum many-body problems by using Artificial Neural Networks (ANN). Their work is based on minimizing a variational wave function to obtain the ground states for various spin-dependent systems. This work is primarily focused on developing efficient method using ANN to solve the ground state wave function for atomic systems. We have developed a theoretical groundwork to represent the wave function of a many-electron atom by using artificial neural network while still preserving its antisymmetric property. By using the Metropolis algorithm, Variational Monte Carlo (VMC), and Stochastic Reconfiguration (SR) methods for minimization, we were able to obtain a highly accurate ground state wave function for the He atom. To verify our optimization algorithm, we reproduced the results for the ground state of a three dimensional Simple Harmonic Oscillator (SHO) given by Teng [18].

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this honors thesis in whole or part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the honors thesis.

Available for download on Sunday, May 31, 2020

Included in

Physics Commons

Share

COinS