Structural prediction of carbon cluster isomers with machine-learning potential

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Authors

DOI:

https://doi.org/10.15625/0868-3166/20609

Keywords:

Carbon clusters, Low-energy isomers, Machine-learning potential

Abstract

Structural prediction of low-energy isomers of carbon twelve-atom clusters is carried out using the recently developed machine-learning potential GAP-20. The GAP-20 agrees with density-functional theory calculations regarding geometric structures and average C-C bond lengths for most isomers. However, the GAP-20 substantially lowers the energies of cage-like structures, resulting in a wrong ground state. A comparison of the cohesive energies with the density-functional theory points out that the GAP-20 only gives good results for monocyclic rings. Two multicyclic rings appear as new low-energy isomers, which have yet to be discovered in previous research.

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References

H. W. Kroto, J. R. Heath, S. C. O’Brien, R. F. Curl, and R. E. Smalley, C60: Buckminsterfullerene, Nature 318 (1985) 162.

K. Kaiser, L. M. Scriven, F. Schulz, P. Gawel, L. Gross, and H. L. Anderson, An sp-hybridized molecular carbon allotrope, cyclo[18]carbon, Science 365 (2019) 1299.

L. M. Scriven, K. Kaiser, F. Schulz, A. J. Sterling, S. L. Woltering, P. Gawel et al., Synthesis of Cyclo[18]carbon via Debromination of C18Br6, J. Am. Chem. Soc. 142 (2020) 12921.

H. W. Kroto, The stability of the fullerenes Cn, with n = 24, 28, 32, 36, 50, 60 and 70, Nature 329 (1987) 529.

E. Barborini, P. Piseri, A. Li Bassi, A. C. Ferrari, C. E. Bottani, and P. Milani, Synthesis of carbon films with controlled nanostructure by separation of neutral clusters in supersonic beams, Chem. Phys. Lett. 300 (1999) 633.

C. Lifshitz, Carbon clusters, Int. J. Mass Spectrom. 200 (2000) 423.

A. Hu, Q. B. Lu, W. W. Duley and M. Rybachuk, Spectroscopic characterization of carbon chains in nanostructured tetrahedral carbon films synthesized by femtosecond pulsed laser deposition, J. Chem. Phys. 126 (2007) 154705.

H. Kietzmann, R. Rochow, G. Ganteför, and W. Eberhardt, Electronic Structure of Small Fullerenes: Evidence for the High Stability of C32, Phys. Rev. Lett. 81 (1998) 5378..

H. Shinohara, H. Sato, Y. Saito, A. Izuoka, T. Sugawara, H. Ito et al., Extraction and mass spectroscopic characterization of giant fullerences up to C500, Rapid Commun. Mass Spectrom. 6 (1992) 413.

J. Hous̆ka, N. R. Panyala, E. M. Peña-Méndez, and J. Havel, Mass spectrometry of nanodiamonds, Rapid Commun. Mass Spectrom. 23 (2009) 1125.

L. T. Shi, Z. Q. Wang, C. E Hu, Y. Cheng, J. Zhu, and G. F. Ji, Possible lower energy isomer of carbon clusters Cn (n = 11, 12) via particle swarm optimization algorithm: Ab initio investigation, Chem. Phys. Lett. 721 (2019) 74..

D. Manna and J. M. L. Martin, What Are the Ground State Structures of C20 and C24? An Explicitly Correlated Ab Initio Approach, J. Phys. Chem. A 120 (2016) 153.

D. M Cleland, E. K Fletcher, A. Kuperman, and M. C Per, Electron correlation effects in isomers of C20, J. Phys. Mater. 3 (2020) 025006.

D. P. Kosimov, A. A. Dzhurakhalov, and F. M. Peeters, Carbon clusters: From ring structures to nanographene, Phys. Rev. B 81 (2010) 195414.

C. Mauney, M. B. Nardelli, and D. Lazzati, Formation and Properties of Astrophysical Carbonaceous Dust. I. Ab-Initio Calculations of the Configuration and Binding Energies of Small Carbon Clusters, ApJ 800 (2015) 30.

B. Karasulu, J. M. Leyssale, P. Rowe, C. Weber, and C. de Tomas, Accelerating the prediction of large carbon clusters via structure search: Evaluation of machine-learning and classical potentials, Carbon 191 (2022) 255.

P. Rowe, V. L. Deringer, P. Gasparotto, G. Csányi, and A. Michaelide, An accurate and transferable machine learning potential for carbon, J. Chem. Phys. 153 (2020) 034702.

C. Qian, B. McLean, D. Hedman, and F. Ding, A comprehensive assessment of empirical potentials for carbon materials, APL Mater. 9 (2021) 061102.

Y. Wang, J. Lv, L. Zhu, and Y. Ma, Crystal structure prediction via particle-swarm optimization, Phys. Rev. B 82 (2010) 094116.

J. Lv, Y. Wang, L. Zhu, and Y. M. Ma, Particle-swarm structure prediction on clusters, J. Chem. Phys. 137 (2012) 084104. https://doi.org/10.1063/1.4746757

G. Jana, A. Mitra, S. Pan, S. Sural, and P. K. Chattaraj, Modified Particle Swarm Optimization Algorithms for the Generation of Stable Structures of Carbon Clusters, Cn (n = 3–6, 10), Front. Chem 7 (2019) 485.

S. Plimpton, Fast Parallel Algorithms for Short-Range Molecular Dynamics, J. Chem. Phys. 117 (1995) 1.

E. Bitzek, P. Koskinen, F. Gähler, M. Moseler, and P. Gumbsch, Structural Relaxation Made Simple, Phys. Rev. Lett. 97 (2006) 170201.

P. Giannozzi, S. Baroni, N. Bonini, M. Calandra, R. Car, C. Cavazzoni et al., QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials, J. Phys. : Condens. Matter 21 (2009) 395502.

P. Giannozzi, O. Andreussi, T. Brumme, O. Bunau, M. B. Nardelli, M. Calandra et al., Advanced capabilities for materials modelling with Quantum ESPRESSO, J. Phys. : Condens. Matter 29 (2017) 465901.

G. Prandini, A. Marrazzo, I. E. Castelli, N. Mounet, and N. Marzari, Precision and efficiency in solid-state pseudopotential calculations, Npj Comput. Mater. 4 (2018) 72.. http://materialscloud.org/sssp

P. E. Blöchl, Projector augmented-wave method, Phys. Rev. B 50 (1994) 17953.

R. G. Shirazi, D. A. Pantazis, and F. Neese, Performance of density functional theory and orbital-optimised second-order perturbation theory methods for geometries and singlet–triplet state splittings of aryl-carbenes, Mol. Phys. 118 (2020) e1764644.

J. P. Perdew, K. Burke, and M. Ernzerhof, Generalized Gradient Approximation Made Simple, Phys. Rev. Lett. 77 (1996) 3865.

J. M. L. Martin, J. El-Yazal, and J.-P. Frarqois, Structure and vibrational spectra of carbon clusters Cn (n = 2–10, 12, 14, 16, 18) using density functional theory including exact exchange contributions, Chem. Phys. Lett. 242 (1995) 570. https://doi.org/10.1016/0009-2614(95)00801-A

T. W. Yen and S. K. Lai, Use of density functional theory method to calculate structures of neutral carbon clusters Cn (3 ≤ n ≤ 24) and study their variability of structural forms, J. Chem. Phys. 142 (2015) 084313. https://doi.org/10.1063/1.4908561

C. Xu, G. R. Burton, T, R. Taylor, and D. M. Neumark, Photoelectron spectroscopy of C4, C6, and C8, J. Chem. Phys. 107 (1997) 3428.

A. V. Orden and R. J. Saykally, Small Carbon Clusters: Spectroscopy, Structure, and Energetics, Chem. Rev. 98 (1998) 2313.

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Published

11-06-2024

How to Cite

[1]
. D. H. Nguyen, “Structural prediction of carbon cluster isomers with machine-learning potential”, Comm. Phys., vol. 34, no. 3, p. 259, Jun. 2024.

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Papers
Received 19-04-2024
Accepted 28-05-2024
Published 11-06-2024