High-throughput first-principle prediction of collinear magnetic topological materials

Abstract

The success of topological band theory and symmetry-based topological classification significantly advances our understanding of the Berry phase. Based on the critical concept of topological obstruction, efficient theoretical frameworks, including topological quantum chemistry and symmetry indicator theory, were developed, making a massive characterization of real materials possible. However, the classification of magnetic materials often involves the complexity of their unknown magnetic structures, which are often hard to know from experiments, thus, hindering the topological classification. In this paper, we design a high-throughput workflow to classify magnetic topological materials by automating the search for collinear magnetic structures and the characterization of their topological natures. We computed 1049 chosen transition-metal compounds (TMCs) without oxygen and identified 64 topological insulators and 53 semimetals, which become 73 and 26 when U correction is further considered. Due to the lack of magnetic structure information from experiments, our high-throughput predictions provide insightful reference results and make the step toward a complete diagnosis of magnetic topological materials.