  We present data-driven simulations for gold nanostructures, and develop a
model that links total energy to geometrical features of the particle, with the
ultimate goal of deriving reliable edge energies of gold. Assuming that the
total energy can be decomposed into contributions from the bulk, surfaces,
edges, and vertices, we use machine learning for reliable multi-variant fits of
the associated coefficients. The proposed method of total energy calculations
using machine learning produces almost ab-initio-like accuracy with minimal
computational cost. Furthermore, a clear definition and metric for edge energy
is introduced for edge-energy density calculations that avoid the troublesome
definition of edge length in nanostructures. Our results for edge-energy
density are 0.22 eV/{\AA} for (100)/(100) edges and 0.20 eV/{\AA} for
(111)/(111) edges. Calculated vertex energies are about 1 eV/atom. The present
method can be readily extended to other metals and edge orientations as well as
arbitrary nanoparticle shapes.
