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

Abstract

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.

References in corpus (5)

• Machine-learning based interatomic potential for amorphous carbon
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• Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon
• Ab initio simulation of amorphous graphite
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Cited by in corpus (3)

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