Award Date

12-1-2020

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Physics and Astronomy

First Committee Member

Qiang Q. Zhu

Second Committee Member

Ashkan Salamat

Third Committee Member

Bernard Zygelman

Fourth Committee Member

Aidan Thompson

Fifth Committee Member

Yifei Mo

Sixth Committee Member

Monika Neda

Number of Pages

144

Abstract

Atomistic modeling methods such as molecular dynamics play important roles in investigating time-dependent physical and chemical processes at the microscopic level. In the simulations, energy and forces, sometimes including stress tensor, need to be recalculated iteratively as the atomic configuration evolves. Consequently, atomistic simulations crucially depend on the accuracy of the underlying potential energy surface. Modern quantum mechanical modeling based on density functional theory can consistently generate an accurate description of the potential energy surface. In most cases, molecular dynamics simulations based on density functional theory suffer from highly demanding computational costs. On the other hand, atomistic simulations based on classical force fields have proven to be essential in the computational modeling community due to their unrivaled computational efficiency. However, classical force fields are only useful for inspecting the qualitative insights because they fail to provide confidence in the quantitative results for a lot of cases. In this thesis, I will show the power of machine learning potentials, resolving the predicaments described above. First, the machine learning potential methods will be applied to SiO2 for investigating the implications of different machine learning potentials. Second, the machine learning potentials will be extended to predict physical properties of crystalline silicon, Ni-Mo system, high entropy alloy of NbMoTaW, and Pt for nanoparticle systems. Finally, the diffusion barriers of Pt adsorptions on Pt(111) and Pt(100) surfaces will be examined in detail.

Keywords

Atomistic simulations; Density functional theory; Gaussian process regression; Machine learning interatomic potentials; Neural networks; Nudged elastic band

Disciplines

Engineering Science and Materials | Materials Science and Engineering | Physical Chemistry

File Format

pdf

File Size

2500 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

Available for download on Wednesday, December 15, 2021


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