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Title: Computational Search for Strong Topological Insulators: An Exercise in Data Mining and Electronic Structure

In this paper, we report a data-mining investigation for the search of topological insulators by examining individual electronic structures for over 60,000 materials. Using a data-mining algorithm, we survey changes in band inversion with and without spin-orbit coupling by screening the calculated electronic band structure for a small gap and a change concavity at high-symmetry points. Overall, we were able to identify a number of topological candidates with varying structures and composition. Lastly, our overall goal is expand the realm of predictive theory into the determination of new and exotic complex materials through the data mining of electronic structure.
 [1] ;  [2] ;  [3]
  1. Uppsala Univ. (Sweden)
  2. James Madison Univ., Harrisonburg, VA (United States); Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); NORDITA, Stockholm (Sweden)
Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 1916-9639
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Applied Physics Research
Additional Journal Information:
Journal Volume: 6; Journal Issue: 4; Journal ID: ISSN 1916-9639
Canadian Center of Science and Education
Research Org:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
Country of Publication:
United States
36 MATERIALS SCIENCE; 97 MATHEMATICS AND COMPUTING electronic structure; data mining; topological insulators; predictive theory