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Principles that predict reactions or properties of materials define thediscipline of chemistry. In this work we derive chemical rules, based on atomicdistances and chemical bond character, which predict topological materials incompounds that feature the structural motif of a square-net. Using these ruleswe identify over 300 potential new topological materials. We show that simplechemical heuristics can be a powerful tool to characterize topological matter.In contrast to previous database-driven materials categorization our approachallows us to identify candidates that are alloys, solid-solutions, or compoundswith statistical vacancies. While previous material searches relied on densityfunctional theory, our approach is not limited by this method and could also beused to discover magnetic and statistically-disordered topological semimetals.

The role of delocalized chemical bonding in square-net-based topological semimetals