Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation Potential

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 (GAP) Framework [Volker L. Deringer and Gábor Csányi, Phys. Rev. B 95, 094203 (2017)]. Fullerenes formed seven different system sizes, ranging 60 ∼ 3774 atoms, considered. The buckyonions are by clustering layering outermost shell proceeding inward. Inter-shell cohesion is partly due to interaction between delocalized π electrons protruding into gallery. energies of models validated ex post facto density functional codes, VASP SIESTA, revealing an energy difference range 0.02 - 0.08 eV/atom after conjugate gradient convergence was achieved with both methods.