Anisotropic and isotropic elasticity and thermal transport in monolayer C$_{24}$ networks from machine-learning molecular dynamics
Qing Li, Haikuan Dong, Penghua Ying, Zheyong Fan

TL;DR
This study develops a machine-learned potential to investigate elastic and thermal properties of monolayer C24 fullerene networks, revealing how bonding topology influences anisotropic heat transport and mechanical robustness.
Contribution
The paper introduces NEP-C24, a new machine-learning potential for C24 monolayers, enabling systematic analysis of their elastic and thermal transport properties with insights into bonding topology effects.
Findings
C24 phases show enhanced stiffness compared to C60 monolayers.
qTP C24 exhibits nearly isotropic elastic and thermal properties.
Low-frequency acoustic phonons dominate heat transport, with anisotropic behavior in qHP C24.
Abstract
Two-dimensional fullerene networks have recently attracted increasing interest due to their diverse bonding topologies and mechanically robust architectures. In this work, we develop an accurate machine-learned potential NEP-C for both the quasi-hexagonal phase (qHP) and the quasi-tetragonal phase (qTP) C monolayers, based on the neuroevolution potential (NEP) framework. Using this NEP-C model, we systematically investigate the elastic and thermal transport properties. Compared with C monolayers, both C phases exhibit markedly enhanced stiffness, arising from the combination of reduced molecular size and increased density of covalent bonds. The qTP C monolayer shows nearly isotropic elastic properties and thermal conductivities along its two principal axes owing to its four-fold symmetry, whereas the chain-like, misaligned bonding topology of…
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