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Australian Journal of Chemistry Australian Journal of Chemistry Society
An international journal for chemical science
RESEARCH ARTICLE (Open Access)

Comparative Study of Carbon Force Fields for the Simulation of Carbon Onions

Alireza Aghajamali https://orcid.org/0000-0003-0932-0513 A B and Amir Karton https://orcid.org/0000-0002-7981-508X A B
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- Author Affiliations

A School of Molecular Sciences, The University of Western Australia, Perth, WA 6009, Australia.

B Corresponding authors. Email: alireza.aghajamali@uwa.edu.au; amir.karton@uwa.edu.au

Australian Journal of Chemistry 74(10) 709-714 https://doi.org/10.1071/CH21172
Submitted: 21 July 2021  Accepted: 31 August 2021   Published: 7 October 2021

Journal Compilation © CSIRO 2021 Open Access CC BY-NC-ND

Abstract

We evaluate the performance of ten common carbon force fields for the interaction energies in double and triple layered carbon onions. In particular, we consider the C20@C60, C20@C80, C20@C180, C80@C240, C60@C240 and C240@C540 double-layer carbon onions and C60@C240@C540 and C80@C240@C540 triple-layered carbon onions. We consider the following carbon force fields: Tersoff, REBO-II, AIREBO, AIREBO-M, screened versions of Tersoff and REBO-II, LCBOP-I, 2015 and 2020 versions of ReaxFF, and the machine-learning GAP force field. We show that the ReaxFF force fields give the best performance for the interaction energies of the cabon onions relative to density functional theory interaction energies obtained at the PBE0-D3/def2-TZVP level of theory. We proceed to use the ReaxFF-15 force field to explore the interaction energies in a giant ten-layered carbon onion with a C60 core and show that the interaction energy between the outer layer and the inner layers increases linearly with the number of layers in the carbon onion (with a squared correlation coefficient of R2 = 0.9996). This linear increase in the stabilization energy with each consecutive layer may have important thermodynamic consequences for describing the formation and growth of large carbon onions.

Keywords: carbon nano-onions, carbon nanomaterials, fullerenes, machine-learning force fields, empirical force fields, interaction energy.


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