High-throughput Investigations of Topological and Nodal Superconductors
Feng Tang, Seishiro Ono, Xiangang Wan, Haruki Watanabe

TL;DR
This paper develops a comprehensive database and computational tools to predict topological and nodal superconductivity in materials based on symmetry indicators, aiding the search for Majorana fermions for quantum computing.
Contribution
It introduces a new approach to predict topological superconductivity using symmetry indicators for materials in the Inorganic Crystal Structure Database.
Findings
Approximately 90% of analyzed materials are topological or nodal superconductors.
Identified positions and shapes of nodes in representation-enforced nodal superconductors.
Provided a user-friendly subroutine for analyzing topological superconductivity in any material.
Abstract
The theory of symmetry indicators has enabled database searches for topological materials in normal conducting phases, which has led to several encyclopedic topological material databases. To date, such a database for topological superconductors is yet to be achieved because of the lack of information about pairing symmetries of realistic materials. In this work, sidestepping this issue, we tackle an alternative problem: the predictions of topological and nodal superconductivity in materials for each single-valued representation of point groups. Based on recently developed symmetry indicators for superconductors, we provide comprehensive mappings from pairing symmetries to topological or nodal superconducting nature for nonmagnetic materials listed in Inorganic Crystal Structure Database. We quantitatively show that around 90\% of computed materials are topological or nodal…
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