TY - JOUR
T1 - The Role of Delocalized Chemical Bonding in Square-Net-Based Topological Semimetals
AU - Klemenz, Sebastian
AU - Hay, Aurland K.
AU - Teicher, Samuel M.L.
AU - Topp, Andreas
AU - Cano, Jennifer
AU - Schoop, Leslie M.
N1 - Funding Information:
This research was supported by the Arnold and Mabel Beckman Foundation through a Beckman Young Investigator grant awarded to L.M.S. We acknowledge the use of Princeton’s Imaging and Analysis Center, which is partially supported by the Princeton Center for Complex Materials, a National Science Foundation (NSF)-MRSEC program (DMR-1420541). We acknowledge use of the shared computing facilities of the Center for Scientific Computing at UC Santa Barbara, supported by NSF CNS-1725797, and the NSF MRSEC at UC Santa Barbara, NSF DMR-1720256. The UC Santa Barbara MRSEC is a member of the Materials Research Facilities Network ( www.mrfn.org ). S.M.L.T. has been supported by the National Science Foundation Graduate Research Fellowship Program under Grant no. DGE-1650114. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. A.T. was supported by the DFG, proposal no. SCHO 1730/1-1. J.C. is partially supported by the Flatiron Institute, a division of the Simons Foundation.
Publisher Copyright:
Copyright © 2020 American Chemical Society.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - Principles that predict reactions or properties of materials define the discipline of chemistry. In this work, we derive chemical rules, based on atomic distances and chemical bond character, which predict topological materials in compounds that feature the structural motif of a square-net. Using these rules, we identify over 300 potential new topological materials. We show that simple chemical heuristics can be a powerful tool to characterize topological matter. In contrast to previous database-driven materials' categorization, our approach allows us to identify candidates that are alloys, solid-solutions, or compounds with statistical vacancies. While previous material searches relied on density functional theory, our approach is not limited by this method and could also be used to discover magnetic and statistically disordered topological semimetals.
AB - Principles that predict reactions or properties of materials define the discipline of chemistry. In this work, we derive chemical rules, based on atomic distances and chemical bond character, which predict topological materials in compounds that feature the structural motif of a square-net. Using these rules, we identify over 300 potential new topological materials. We show that simple chemical heuristics can be a powerful tool to characterize topological matter. In contrast to previous database-driven materials' categorization, our approach allows us to identify candidates that are alloys, solid-solutions, or compounds with statistical vacancies. While previous material searches relied on density functional theory, our approach is not limited by this method and could also be used to discover magnetic and statistically disordered topological semimetals.
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U2 - 10.1021/jacs.0c01227
DO - 10.1021/jacs.0c01227
M3 - Article
C2 - 32142261
AN - SCOPUS:85083651979
SN - 0002-7863
VL - 142
SP - 6350
EP - 6359
JO - Journal of the American Chemical Society
JF - Journal of the American Chemical Society
IS - 13
ER -