Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation Potential
C. Ugwumadu,
K. Nepal,
R. Thapa,
Y.G. Lee,
Y. Al Majali,
J. Trembly,
D.A. Drabold
Affiliations
C. Ugwumadu
Department of Physics and Astronomy, Nanoscale and Quantum Phenomena Institute (NQPI), Ohio University, Athens, Ohio 45701, USA; Corresponding authors.
K. Nepal
Department of Physics and Astronomy, Nanoscale and Quantum Phenomena Institute (NQPI), Ohio University, Athens, Ohio 45701, USA
R. Thapa
Department of Physics and Astronomy, Nanoscale and Quantum Phenomena Institute (NQPI), Ohio University, Athens, Ohio 45701, USA
Y.G. Lee
Department of Physics and Astronomy, Nanoscale and Quantum Phenomena Institute (NQPI), Ohio University, Athens, Ohio 45701, USA
Y. Al Majali
Russ College of Engineering and Technology, Ohio University, Athens, Ohio 45701, USA
J. Trembly
Russ College of Engineering and Technology, Ohio University, Athens, Ohio 45701, USA
D.A. Drabold
Department of Physics and Astronomy, Nanoscale and Quantum Phenomena Institute (NQPI), Ohio University, Athens, Ohio 45701, USA; Corresponding authors.
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 from seven different system sizes, ranging from 60 ∼ 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 π 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.
