Misfit layer compounds as ultra-tunable field effect transistors: from charge transfer control to emergent superconductivity
Ludovica Zullo, Giovanni Marini, Tristan Cren, Matteo Calandra

TL;DR
This study reveals how misfit layer compounds function as ultra-tunable field effect transistors with controllable charge transfer, enabling the design of emergent superconductivity through alloying strategies.
Contribution
The paper provides a first-principles understanding of charge transfer mechanisms in misfit compounds and demonstrates their potential as highly tunable electronic devices.
Findings
Rocksalt units act as donors, dichalcogenides as acceptors.
Charge transfer can reach 6×10^{14} e^-cm^{-2} and is tunable via alloying.
Strategy for designing emergent superconductivity demonstrated.
Abstract
Misfit layer compounds are heterostructures composed of rocksalt units stacked with few layers transition metal dichalcogenides. They host Ising superconductivity, charge density waves and good thermoelectricity. The design of misfits emergent properties is, however, hindered by the lack of a global understanding of the electronic transfer among the constituents. Here, by performing first principles calculations, we unveil the mechanism controlling the charge transfer and demonstrate that rocksalt units are always donor and dichalcogenides acceptors. We show that misfits behave as a periodic arrangement of ultra-tunable field effect transistors where a charging as large as 6\times10^{14} e^-cm^{-2} can be reached and controlled efficiently by the La-Pb alloying in the rocksalt. Finally, we identify a strategy to design emergent superconductivity and demonstrate its applicability in…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
Topics2D Materials and Applications · Machine Learning in Materials Science · Chalcogenide Semiconductor Thin Films
