Structural, electronic, thermal and mechanical properties of C60-based fullerene two-dimensional networks explored by first-principles and machine learning

Abstract

Recent experimental reports on the realizations of two-dimensional (2D) networks of the C60-based fullerenes with anisotropic and nanoporous lattices represent a significant advance, and create exciting prospects for the development of a new class of nanomaterials. In this work, we employed theoretical calculations to explore novel C60-based fullerene lattices and subsequently evaluate their stability and key physical properties. After the energy minimization of extensive structures, we could detect novel 2D, 1D and porous carbon C60-based networks, with close energies to that of the isolated C60 cage. Density functional theory results confirm that the C60-based networks can exhibit remarkable thermal stability, and depending on their atomic structure show metallic, semimetallic or semiconducting electronic nature. Using the machine learning interatomic potentials, thermal and mechanical responses of the predicted nanoporous 2D lattices were investigated. The estimated thermal conductivity of the quasi-hexagonal-phase of C60 fullerene is shown to be in an excellent agreement with the experimental measurements. Despite of different atomic structures, the anisotropic room temperature lattice thermal conductivity of the fullerene nanosheets are estimated to be in the order of 10 W/mK. Unlike the majority of carbon-based 2D materials, C60-based counterparts noticeably are predicted to show positive thermal expansion coefficients. Porous carbon C60-based networks are found to exhibit superior mechanical properties, with tensile strengths and elastic modulus reaching extraordinary values of 50 and 300 GPa, respectively. The theoretical results presented in this work provide a comprehensive vision on the structural, energetic, electronic, thermal and mechanical properties of the C60-based fullerene networks.

References in corpus (3)

• Machine-Learning Interatomic Potentials Enable First-Principles Multiscale Modeling of Lattice Thermal Conductivity in Graphene/Borophene Heterostructures
• Exploring thermal expansion of carbon-based nanosheets by machine-learning interatomic potentials
• Thermo-mechanical properties of nitrogenated holey graphene (C2N): A comparison of machine-learning-based and classical interatomic potentials

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