Advisory Committee Chair
Cheng-Chien Chen
Advisory Committee Members
Kannatassen Appavoo
Ilias E Perakis
Yogesh K Vohra
Mary Ellen Zvanut
Document Type
Dissertation
Date of Award
2021
Degree Name by School
Doctor of Philosophy (PhD) College of Arts and Sciences
Abstract
In this dissertation, we focus on the computational prediction for new superhard materials and topological phase transitions in quantum materials. To search for new superhard materials, we employed machine learning techniques, evolutionary crystal structure prediction, and density functional theory to explore new superhard materials in ternary B-C-N and B-N-O compounds. For the B-C-N systems, our machine learning prediction shows that a 1:1 B-N ratio can lead to various superhard compositions. Our subsequent evolutionary algorithm searches indicated that BC10N has an extremely high hardness ~ 87 GPa based on density functional theory. For BN- O systems, we adopted an self-improvement computational procedure, and found that ternary compounds with a chemical formula Bx+2NxO3 (e.g. B5N3O3, B6N4O3, etc) are thermodynamically favorable, and several superhard B-N-O compounds can be found when x ≥ 3. Our studies for new ternary superhard materials show that machine learning is a powerful tool to accelerate material discovery. In our studies for topological materials, we found that ZrTe5 is in the vicinity of topological phase transition. By means of the uniaxial tensile strain, ZrTe5 undergoes a Z2 transition from a strong topological insulator phase to a weak topological insulator phase. In the case of LaN, we found that LaN has a low-temperature, ferroelectric phase with P1 symmetry, and it would undergo a structural phase transition to rock-salt phase (Fm3m symmetry) when the temperature is increased. Besides, a hydrostatic pressure ~ 18 GPa can lead to a structural phase transition to tetragonal iii structure (P4=nmm symmetry). Our topological studies show that high-temperature rock-salt LaN can change from a topologically trivial phase to a topologically nontrivial phase at P ~ 14 GPa, and its topological phase transition can also be induced by the temperature at a pressure between 14 and 18 GPa. These results show that strain engineering and hydrostatic pressure can effectively drive topological phase transition in quantum materials.
Recommended Citation
Chen, Wei-Chih, "Predictive Modeling of Superhard and Topological Materials by Density Functional Theory and Machine Learning" (2021). All ETDs from UAB. 618.
https://digitalcommons.library.uab.edu/etd-collection/618
