Spinodal Superlattices of Topological Insulators

Abstract

Spinodal decomposition is proposed for stabilizing self-assembled interfaces between topological insulators (TIs) by combining layers of iso-structural and iso-valent TlBiX2 (X=S, Se, Te) materials. The composition range for gapless states is addressed concurrently to the study of thermodynamically driven boundaries.By tailoring composition, the TlBiS2-TlBiTe2 system might produce both spinodal superlattices and two dimensional eutectic microstructures, either concurrently or separately. The dimensions and topological nature of the metallic channels are determined by following the spatial distribution of the charge density and the spin-texture. The results validate the proof of concept for obtaining spontaneously forming two-dimensional TI-conducting channels embedded into three-dimensional insulating environments without any vacuum interfaces. Since spinodal decomposition is a controllable kinetic phenomenon, its leverage could become the long-sought enabler for effective TI technological deployment.arXiv:1803.06289v1 [cond-mat.mtrl-sci]

Optical and thermal properties of Sb/Bi-modified mixed Ge-Ga-Se-Te glasses

“…Historically, one of the first discovered materials with topological insulating properties was the BixSb1−x compound [6]. Recently, some chalcogenide stoichiometric crystals, such as Bi2Te3, Bi2Se3, Sb2Te3 and Sb2Se3, as well as mixed/doped derived compounds have been proposed as perspective three-dimensional topological insulators [7][8][9][10][11][12]. Some of them are characterized by extremely simple topological surface states described by a single gapless Dirac cone at the k = 0 Γ point in the surface Brillouin zone [7,8,12].…”

Automated Computation of Materials Properties

“…This concept has also been extended to other nanotechnology applications, e.g. as a means to embed a network of electrically conducting nanowires, in the form of topologically protected interface states, within an insulating matrix [228].…”

Data-driven design of inorganic materials with the Automatic Flow Framework for Materials Discovery

“…The AFLOW formation energy data is also employed to train cluster expansion models to compute the energies of multicomponent alloys, 76 which can be combined with thermodynamic modeling to predict the order-disorder transition temperature for solid solutions in high-entropy alloys. 44 Order-disorder transitions in the form of spinodal decomposition have also been proposed as a mechanism to (1) embed topologically protected conducting interface states in an insulating matrix 77 and (2) self-assemble nanostructures (such as thermoelectric devices 78 ). The boundaries between different layers act as phonon scatterers, suppressing thermal conductivity and thus improving efficiency.…”