Date of Award
2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Materials Science
First Advisor
Michael T. Ruggiero
Abstract
This research investigation delves into the realm of crystal structure prediction (CSP), a vital tool for materials discovery that enables researchers to theoretically forecast and pre-evaluate new materials’ properties. While various methods have been developed, first-principle calculations are the most widely applicable. However, these methods are often computationally expensive and time-consuming due to the vast potential structural search space, limiting their efficiency. This work proposes a novel, efficient first-principle DFT-based CSP method that leverages crystal symmetries for rapid searching. The effectiveness of this method was validated through a comprehensive comparison of predicted crystallographic descriptions with experimentally validated structures, demonstrating their efficacy in predicting crystal structures and properties. The findings of this study would contribute meaningfully to the advancement of CSP, and pave the way for breakthroughs in materials science and related fields.
Furthermore, a significant part of this research is dedicated to unraveling the structural intricacies and dynamics of porous materials, through the study of metal-organic frameworks (MOFs) and clathrates. By integrating MD simulations and THz spectroscopy as a formidable investigational tool, this study uncovers new insights into the structure-property relationships of porous materials, shedding light on the complex interplay between pore architecture, molecular interactions, and material response. The findings of this research have significant implications for the design and optimization of porous materials for various applications, including energy storage age, and environmental sustainability.
Overall, the outcomes of this research represent advancements with far-reaching implications, enabling the design and discovery of novel materials with tailored properties, and shedding light on the complex relationships between crystal structure and material behavior.
Language
en
Number of Pages
348 p.
Recommended Citation
Ajibade, Saheed Akanji, "A Strategic and Efficient Approach to First Principles Crystal Structures Prediction" (2024). Graduate College Dissertations and Theses. 1895.
https://scholarworks.uvm.edu/graddis/1895