English

Spectral properties of hyperbolic nano-networks with tunable aggregation of simplexes

Statistical Mechanics 2019-07-31 v1 Computational Physics

Abstract

Cooperative self-assembly can result in complex nano-networks with new hyperbolic geometry. However, the relation between the hyperbolicity and spectral and dynamical features of these structures remains unclear. Using the model of aggregation of simplexes introduced in I [Sci. Rep., 8:1987, 2018], here we study topological and spectral properties of a large class of self-assembled structures consisting of monodisperse building blocks (cliques of size n=3,4,5,6n=3,4,5,6) which self-assemble via sharing the geometrical shapes of a lower order. The size of the shared sub-structure is tunned by varying the chemical affinity ν\nu such that for significant positive ν\nu sharing the largest face is the most probable, while for ν<0\nu < 0, attaching via a single node dominates. Our results reveal that, while the parameter of hyperbolicity remains δmax=1\delta_{max}=1 across the assemblies, their structure and spectral dimension dsd_s vary with the size of cliques nn and the affinity when ν0\nu \geq 0. In this range, we findthat ds>4d_s >4 can be reached for n5n\geq 5 and sufficiently large ν\nu. For the aggregates of triangles and tetrahedra, the spectral dimension remains in the range ds[2,4)d_s\in [2,4), as well as for the higher cliques at vanishing affinity. On the other end, for ν<0\nu < 0, we find ds1.57d_s\eqsim 1.57 independently on nn. Moreover, the spectral distribution of the normalised Laplacian eigenvalues has a characteristic shape with peaks and a pronounced minimum, representing the hierarchical architecture of the simplicial complexes. These findings show how the structures compatible with complex dynamical properties can be assembled by controlling the higher-order connectivity among the building blocks.

Keywords

Cite

@article{arxiv.1905.09579,
  title  = {Spectral properties of hyperbolic nano-networks with tunable aggregation of simplexes},
  author = {Marija Mitrovic Dankulov and Bosiljka Tadic and Roderick Melnik},
  journal= {arXiv preprint arXiv:1905.09579},
  year   = {2019}
}

Comments

9 pages, 7 figures included