Emergent topological states via digital (001) oxide superlattices

Abstract

Oxide heterostructures exhibit many intriguing properties. Here we provide design principles for inducing multiple topological states in (001) (AMO3)1/($$AM^{\prime}$$ A M ′ O3)1 oxide superlattices. Aided by first-principles calculations and model analysis, we show that a (SrMO3)1/(Sr$$M^{\prime}$$ M ′ O3)1 superlattice (M = Nb, Ta and $$M^{\prime}$$ M ′ = Rh, Ir) is a strong topological insulator with Z2 index (1;001). More remarkably, a (SrMoO3)1/(SrIrO3)1 superlattice exhibits multiple coexisting topological insulator (TI) and topological Dirac semi-metal (TDS) states. The TDS state has a pair of type-II Dirac points near the Fermi level and symmetry-protected Dirac node lines. The surface TDS Dirac cone is sandwiched by two surface TI Dirac cones in the energy-momentum space. The non-trivial topological properties arise from the band inversion between d orbitals of two dissimilar transition metal atoms and a particular parity property of (001) superlattice geometry. Our work demonstrates how to induce non-trivial topological states in (001) perovskite oxide heterostructures by rational design.

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

“…Transfer learning can also be used in the learning tasks with small data sets, and has been used in materials science. [ 52–56 ] In transfer learning, the source domain and the target domain are defined, which are different but similar. In many cases, the process of transfer learning is also called “domain adaptation”.…”