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 Gabor Csanyi, Phys. Rev. B 95, 094203 (2017)]. Fullerenes formed from seven different system sizes, ranging from 60 & SIM; 3774 atoms, were considered. The buckyonions are formed by clustering and layering starting from the outermost shell and proceeding inward. Inter-shell cohesion is partly due to interaction between delocalized ������ electrons protruding 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 conjugate gradient energy convergence of the models was achieved with both methods.

Automated summary

This study simulated the formation process of multi-shell Fullerene buckyonions using a density-functional-theory (DFT)-trained machine-learning carbon potential within the Gaussian Approximation Potential (GAP) Framework. Fullerene clusters of seven different sizes were formed through self-organization and layering from the outermost shell to the innermost. The inter-shell cohesion is partially due to interaction between delocalized π electrons protruding into the gallery.