Ultrahigh strength and negative thermal expansion and low thermal conductivity in graphyne nanosheets confirmed by machine-learning interatomic potentials
B Mortazavi and XY Zhuang, FLATCHEM, 36, 100446 (2022).
After several decades of experimental endeavors, most recently large- area gamma-graphyne layered materials have been synthesized via a reversible dynamic alkyne metathesis approach (Nature Synthesis (2022) 1, 449-454). Motivated by the aforementioned accomplishment and highly appealing physics of the nanoporous sp-sp2-hy-bridized carbon allotropes, herein we explore the thermal and mechanical properties of six different graphyne lattices, via machine-learning interatomic potentials (MLIPs). To this aim, by employing a computationally efficient passive-training approach, the thermal and mechanical properties are evaluated with ab-initio level of accuracy using the classical molecular dynamics simulations. Analysis of mechanical properties at room tem-perature reveal that graphyne nanosheets depending on the atomic structure and loading direction can show remarkably high ultimate tensile strengths up to around 90 GPa. The graphyne nanomembranes are however predicted to show by around two orders of magnitude suppressed lattice thermal conductivity than graphene. Graphyne nanoporous networks are also predicted to show ultrahigh negative thermal expansion coefficients, one order of magnitude higher than that of the graphene. The presented MLIP-based results provide a critical vision on the structure and thermo-mechanical property relationships in sp-sp2-hybridized graphyne allotropes, which can be useful for the design of novel nanodevices using this important class of nanomaterials.
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