  Topological order in solid state systems is often calculated from the
integration of an appropriate curvature function over the entire Brillouin
zone. At topological phase transitions where the single particle spectral gap
closes, the curvature function diverges and changes sign at certain high
symmetry points in the Brillouin zone. These generic properties suggest the
introduction of a supervised machine learning scheme that uses only the
curvature function at the high symmetry points as input data. We apply this
scheme to a variety of interacting topological insulators in different
dimensions and symmetry classes, and demonstrate that an artificial neural
network trained with the noninteracting data can accurately predict all
topological phases in the interacting cases with very little numerical effort.
Intriguingly, the method uncovers a ubiquitous interaction-induced topological
quantum multicriticality in the examples studied.
