Data-driven identification of connate topological superconductor candidates

One way to achieve topological superconductivity is through identifying a bulk s-wave superconductor that also exhibits topological surface states. The resulting “connate” topological superconductor functions through the self-proximity effect similar to the interfacial proximity effect within topological superconductor heterostructures. While non-trivial electronic structure topology can be predicted through first-principles calculations, superconductivity is difficult to predict a priori, limiting broad screening for materials that combine both phenomena. Here, we present a data-driven approach to compile a catalog of potential connate topological superconductor materials, starting with experimentally-confirmed superconductors and cross-referencing their topological nature via high-throughput computational data. Additionally, subtle pitfalls in the calculation of Z₂ topological invariants for materials without well-defined band gaps (such as superconductors) will be discussed.