AI-guided engineering of nanoscale topological materials

Nanoscale organic materials have long been known to host topologically protected excitations. Inspired by recent progress in classifying topological phases in armchair, cove-edged and chevron graphene nanoribbons, we develop a high-throughput framework based on the computation of the Zak phase and the Z2 invariants using tight-binding and density functional theory to explore the topology of low-symmetry 1D and 2D periodic organic compounds. As of today, we have identified 224,071 new topological nanoribbons using our framework. Training deep neural networks on the graphs of these Hamiltonians, we analyze the graphical features conducive to topological excitations in these systems. We show how this workflow can help the atomic assembly of topologically non-trivial artificial lattices.