Searching materials for novel physics from theory and from data
Abstract/Contents
- Abstract
- Materials search and discovery is crucially important in condensed matter physics. Besides experimental trial-and-errors, there exist two types of methods to guide materials explorations: "from theory" that starts from theoretic analysis and numerical simulations, and "from data" that leverages massive materials data via statistical machine learning. I will present one work for each of both methods of materials discovery in this dissertation. Firstly, I will discuss the theoretic proposal and materials realization of anti-ferromagnetic Dirac semimetal. I will specifically show how a non-symmorphic crystal symmetry stabilizes a four-fold degenerate point in the electronic band structure of an anti-ferromagnetic system that is invariant under the combination of time-reversal and inversion symmetry, thus realizing massless Dirac fermions as low energy excitations. Secondly, I will talk about how to learn atoms' properties from extensive materials data, inspired by ideas from computational linguistics. I will present analysis of the constructed atom vectors, as well as their applications in data-based materials prediction using machine learning.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Zhou, Quan |
|---|---|
| Associated with | Stanford University, Department of Physics. |
| Primary advisor | Zhang, Shoucheng |
| Thesis advisor | Zhang, Shoucheng |
| Thesis advisor | Fan, Shanhui, 1972- |
| Thesis advisor | Kivelson, Steven |
| Advisor | Fan, Shanhui, 1972- |
| Advisor | Kivelson, Steven |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Quan Zhou. |
|---|---|
| Note | Submitted to the Department of Physics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Quan Zhou
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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