  We present a new approach for the generation of stable structures of
nanoclusters using deep learning methods. Our method consists in constructing
an artificial potential energy surface, with local minima corresponding to the
most stable structures and which is much smoother than "real" potential in the
intermediate regions of the configuration space. To build the surface, graph
convolutional networks are used. The method can extrapolates the potential
surface to cases of structures with larger number of atoms than was used in
training. Thus, having a sufficient number of low-energy structures in the
training set, the method allows to generate new candidates for the ground-state
structures, including ones with larger number of atoms. We applied the approach
to silica clusters $(SiO_2)_n$ and for the first time found the stable
structures with n=28...51. The method is universal and does not depend on the
atomic composition and number of atoms.
