  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.
