A Hamiltonian model of the Fibonacci quasicrystal using non-local interactions: simulations and spectral analysis★,★★
School of Mathematics, Thapar Institute of Engineering & Technology,
2 Centre for Theoretical and Physical Sciences, Clark Atlanta University, Atlanta, Georgia, USA
3 Ronin Institute, 127 Haddon Pl., Montclair, NJ 07043, USA
a e-mail: email@example.com
Received in final form: 4 February 2020
Published online: 8 April 2020
This article presents a Hamiltonian architecture based on vertex types and empires for demonstrating the emergence of aperiodic order in one dimension by a suitable prescription for breaking translation symmetry. At the outset, the paper presents different algorithmic, geometrical, and algebraic methods of constructing empires of vertex configurations of a given lattice. These empires have non-local scope and form the building blocks of the proposed lattice model. This model is tested via Monte Carlo simulations beginning with randomly arranged N tiles. The simulations clearly establish the Fibonacci configuration, which is a one-dimensional quasicrystal of length N, as the final relaxed state of the system. The Hamiltonian is promoted to a matrix operator form by performing dyadic tensor products of pairs of interacting empire vectors followed by a summation over all permissible configurations. A spectral analysis of the Hamiltonian matrix is performed and a theoretical method is presented to find the exact solution of the attractor configuration that is given by the Fibonacci chain as predicted by the simulations. Finally, a precise theoretical explanation is provided which shows that the Fibonacci chain is the most probable ground state. The proposed Hamiltonian is a mathematical model of the one dimensional Fibonacci quasicrystal.
Supplementary material in the form of three mpeg files and one mp4 file available from the Journal web page at https://doi.org/10.1140/epjb/e2020-100544-y.
© EDP Sciences / Società Italiana di Fisica / Springer-Verlag GmbH Germany, part of Springer Nature, 2020