TY - JOUR

T1 - Ultrahigh strength and negative thermal expansion and low thermal conductivity in graphyne nanosheets confirmed by machine-learning interatomic potentials

AU - Mortazavi, Bohayra

AU - Zhuang, Xiaoying

N1 - Funding Information: B.M. and X.Z. appreciate the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). Authors are indebted to Prof. Alexander Shapeev for the valuable discussion and technical support of this study.

PY - 2022/11

Y1 - 2022/11

N2 - After several decades of experimental endeavors, most recently large-area γ-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-hybridized 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 temperature 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.

AB - After several decades of experimental endeavors, most recently large-area γ-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-hybridized 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 temperature 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.

KW - Graphyne

KW - Machine learning

KW - Mechanical

KW - Thermal conductivity

KW - Thermal expansion

UR - http://www.scopus.com/inward/record.url?scp=85141556660&partnerID=8YFLogxK

U2 - 10.1016/j.flatc.2022.100446

DO - 10.1016/j.flatc.2022.100446

M3 - Article

AN - SCOPUS:85141556660

VL - 36

JO - FlatChem

JF - FlatChem

M1 - 100446

ER -