6 papers
Why is topology hard to learn?
D. O. Oriekhov, Stan Bergkamp, Guliuxin Jin +5
Much attention has been devoted to the use of machine learning to approximate physical concepts. Yet, due to challenges in interpretability of machine learning techniques, the ques…
Twist-modulated magnetic interactions in bilayer van der Waals materials
Tomas T. Osterholt, D. O. Oriekhov, Lumen Eek +2
The ability to control magnetic interactions at the nanoscale is crucial for the development of next-generation spintronic devices and functional magnetic materials. In this work,…
RKKY quadratic and biquadratic spin-spin interactions in twisted bilayer graphene
D. O. Oriekhov, T. T. Osterholt, R. A. Duine +1
We study the competition between the RKKY quadratic and biquadratic spin-spin interactions of two magnetic impurities in twisted bilayer graphene away from the magic angle. We appl…
Density of states and differential entropy in Dirac materials in crossed magnetic and in-plane electric fields
Andrii A. Chaika, Yelizaveta Kulynych, D. O. Oriekhov +1
The density of states and differential entropy per particle are analyzed for Dirac-like electrons in graphene subjected to a perpendicular magnetic field and an in-plane electric f…
Dynamical localization in 2D topological quantum random walks
D. O. Oriekhov, Guliuxin Jin, Eliska Greplova
We study the dynamical localization of discrete time evolution of topological split-step quantum random walk (QRW) on a single-site defect starting from a uniform distribution. Usi…
Topological finite size effect in one-dimensional chiral symmetric systems
Guliuxin Jin, D. O. Oriekhov, Lukas Johannes Splitthoff +1
Topological phases of matter have been widely studied for their robustness against impurities and disorder. The broad applicability of topological materials relies on the reliable…