Two-dimensional topological materials discovery by symmetry-indicator method

Abstract

Two-dimensional (2D) topological materials (TMs) have attracted tremendous attention due to the promise of revolutionary devices with non-dissipative electric or spin currents. Unfortunately, the scarcity of 2D TMs holds back the experimental realization of such devices. In this work, based on our recently developed, highly efficient TM discovery algorithm using symmetry indicators, we explore the possible 2D TMs in all non-magnetic compounds in four recently proposed materials databases for possible 2D materials. We identify hundreds of 2D TM candidates, including 205 topological (crystalline) insulators and 299 topological semimetals. In particular, we highlight MoS, with a mirror Chern number of -4, as a possible experimental platform for studying the interaction-induced modification to the topological classification of materials. Our results winnow out the topologically interesting 2D materials from these databases and provide a TM gene pool which for further experimental studies.

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• All Topological Bands of All Nonmagnetic Stoichiometric Materials
• Topological Materials Discovery from Crystal Symmetry
• Machine learning for materials discovery: two-dimensional topological insulators
• Prediction of phonon-mediated superconductivity with high critical temperature in the two-dimensional topological semimetal W2N3
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• Machine learning spectral indicators of topology
• Electronic correlations in the van der Waals ferromagnet Fe${}_3$GeTe${}_2$ revealed by its charge dynamics
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• Multiple Symmetry-Protected Dirac Nodal Lines in A Quasi-One-Dimensional Semimetal
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