  Multi-shell fullerenes "buckyonions" were simulated, starting from initially
random configurations, using a density-functional-theory (DFT)-trained
machine-learning carbon potential within the Gaussian Approximation Potential
(ML-GAP) Framework [Volker L. Deringer and Gabor Csanyi, Phys. Rev. B 95,
094203 (2017)]. A large set of such fullerenes were obtained with sizes ranging
from 60 ~ 3774 atoms. The buckyonions are formed by clustering and layering
starts from the outermost shell and proceed inward. Inter-shell cohesion is
partly due to interaction between delocalized $\pi$ electrons into the gallery.
The energies of the models were validated ex post facto using density
functional codes, VASP and SIESTA, revealing an energy difference within the
range of 0.02 - 0.08 eV/atom after conjuagte gradient energy convergence of the
models were achieved with both methods.
