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

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