Ph.D. Candidate: Quan Zhou
Research Advisor: Shou-Cheng Zhang
Date: Monday, January 22nd 2018
Location: Room 130, McCullough Building
Title: Searching Materials for Novel Physics, from theory and from data
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 talk. Firstly, I will talk about 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.