Theses and Dissertations

Advisor

Dickel, Doyl E.

Committee Member

Barrett, Christopher K.

Committee Member

Oppedal, Andrew

Date of Degree

5-16-2025

Original embargo terms

Immediate Worldwide Access

Document Type

Graduate Thesis - Open Access

Major

Mechanical Engineering

Degree Name

Master of Science (M.S.)

College

James Worth Bagley College of Engineering

Department

Michael W. Hall School of Mechanical Engineering

Abstract

To study molecular dynamics in bismuth, Quantum Espresso was used to create a density functional theory (DFT) database which was then used as the input for rapid artificial neural network (RANN). The RANN interatomic potential that was developed using this database was then validated. Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) was used to create simulations and gather data using the RANN potential. The properties of bismuth, including elastic constants, melting points, and volume change, were calculated and compared to DFT and experimentally observed data. The RANN potential coincided well with these values. The RANN potential shows good predictive capabilities for A7 bismuth as well as phases two, C2M, four, CMCE, and five, IM3M. The potential developed by this research can make accurate predictions for the properties of pure bismuth.

Download

Share

COinS