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

Ugwumadu, C.; Nepal, K.; Thapa, R.; Lee, Y. G.; Al Majali, Y.; Trembly, J.; Drabold, D. A.

doi:10.1016/j.cartre.2022.100239

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

Ugwumadu, C.·Nepal, K.·Thapa, R.·Lee, Y. G.·Al Majali, Y.·Trembly, J.·Drabold, D. A.

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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 (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.

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74 ATOMIC AND MOLECULAR PHYSICS Buckyonion Carbon Fullerenes Gaussian Approximation Potential Machine learning

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Ugwumadu, C., Nepal, K., Thapa, R., Lee, Y. G., Al Majali, Y., Trembly, J., Drabold, D. A.. 2022-12-14. Simulation of multi-shell fullerenes using Machine-Learning Gaussian Approximation Potential. https://doi.org/10.1016/j.cartre.2022.100239

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