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Communication

Low and Anisotropic Tensile Strength and Thermal Conductivity in the Single-Layer Fullerene Network Predicted by Machine-Learning Interatomic Potentials

by
Bohayra Mortazavi
1,* and
Xiaoying Zhuang
1,2,*
1
Computational Science and Simulation Technology, Department of Mathematics and Physics, Leibniz Universität Hannover, Appelstraße 11, 30167 Hannover, Germany
2
College of Civil Engineering, Department of Geotechnical Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China
*
Authors to whom correspondence should be addressed.
Coatings 2022, 12(8), 1171; https://doi.org/10.3390/coatings12081171
Submission received: 28 July 2022 / Revised: 9 August 2022 / Accepted: 11 August 2022 / Published: 12 August 2022

Abstract

In the latest ground-breaking experimental advancement (Nature (2022), 606, 507), zero-dimensional fullerenes (C60) have been covalently bonded to form single-layer two-dimensional (2D) fullerene network, namely quasi-hexagonal-phase fullerene (qHPC60). Motivated by the aforementioned accomplishment, in this communication, for the first time, we explore the phononic and mechanical properties of the qHPC60 monolayer, employing state-of-the-art machine-learning interatomic potentials. By employing an efficient passive-training methodology, the thermal and mechanical properties were examined with an ab-initio level of accuracy using the classical molecular dynamics simulations. Predicted phonon dispersion confirmed the desirable dynamical stability of the qHPC60 monolayer. Room temperature lattice thermal conductivity is predicted to be ultralow and around 2.9 (5.7) W/m·K along the x(y) directions, which are by three orders of magnitude lower than that of the graphene. Close to the ground state and at room temperature, the ultimate tensile strength of the qHPC60 monolayer along the x(y) directions is predicted to be 7.0 (8.8) and 3.3 (4.2) GPa, respectively, occurring at corresponding strains of around 0.07 and 0.029, respectively. The presented computationally accelerated first-principles results confirm highly anisotropic and remarkably low tensile strength and phononic thermal conductivity of the qHPC60 fullerene network nanosheets.
Keywords: fullerene network; machine learning; mechanical; thermal conductivity fullerene network; machine learning; mechanical; thermal conductivity
Graphical Abstract

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MDPI and ACS Style

Mortazavi, B.; Zhuang, X. Low and Anisotropic Tensile Strength and Thermal Conductivity in the Single-Layer Fullerene Network Predicted by Machine-Learning Interatomic Potentials. Coatings 2022, 12, 1171. https://doi.org/10.3390/coatings12081171

AMA Style

Mortazavi B, Zhuang X. Low and Anisotropic Tensile Strength and Thermal Conductivity in the Single-Layer Fullerene Network Predicted by Machine-Learning Interatomic Potentials. Coatings. 2022; 12(8):1171. https://doi.org/10.3390/coatings12081171

Chicago/Turabian Style

Mortazavi, Bohayra, and Xiaoying Zhuang. 2022. "Low and Anisotropic Tensile Strength and Thermal Conductivity in the Single-Layer Fullerene Network Predicted by Machine-Learning Interatomic Potentials" Coatings 12, no. 8: 1171. https://doi.org/10.3390/coatings12081171

APA Style

Mortazavi, B., & Zhuang, X. (2022). Low and Anisotropic Tensile Strength and Thermal Conductivity in the Single-Layer Fullerene Network Predicted by Machine-Learning Interatomic Potentials. Coatings, 12(8), 1171. https://doi.org/10.3390/coatings12081171

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