Journal
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
Volume 142, Issue 13, Pages 6350-6359Publisher
AMER CHEMICAL SOC
DOI: 10.1021/jacs.0c01227
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Funding
- Arnold and Mabel Beckman Foundation through a Beckman Young Investigator grant
- Princeton Center for Complex Materials, a National Science Foundation (NSF)-MRSEC program [DMR1420541]
- NSF [CNS-1725797]
- NSF MRSEC at UC Santa Barbara [NSF DMR-1720256]
- National Science Foundation Graduate Research Fellowship Program [DGE-1650114]
- DFG [SCHO 1730/1-1]
- Flatiron Institute, a division of the Simons Foundation
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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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