  Thermal and mechanical properties of two-dimensional nanomaterials are
commonly studied by calculating force constants using the density functional
theory (DFT) and classical molecular dynamics (MD) simulations. Although DFT
simulations offer accurate estimations, the computational cost is high. On the
other hand, MD simulations strongly depend on the accuracy of interatomic
potentials. Here, we investigate thermal conductivity and elastic modulus of
nitrogenated holey graphene (C2N) using passively fitted machine-learning
interatomic potentials (MLIPs), which depend on computationally inexpensive
ab-initio molecular dynamics trajectories. Thermal conductivity of C2N is
investigated via MLIP-based non-equilibrium molecular dynamics simulations
(NEMD). At room temperature, the lattice thermal conductivity of 85.5 W/m-K and
effective phonon mean free path of 37.16 nm are found. By carrying out uniaxial
tension simulations, the elastic modulus, ultimate strength, and fractural
strain of C2N are predicted to be 390 GPa, 42 GPa, and 0.29, respectively. It
is shown that the passively fitted MLIPs can be employed as an efficient
interatomic potential to obtain the thermal conductivity and elastic modulus of
C2N utilizing classical MD simulations. Moreover, the possibility of employing
MLIPs to simulate C2N with point defects has been investigated. By training
MLIP with point defect configurations, the mechanical properties of defective
structures were studied. Although using the MLIP is more costly than classical
interatomic potentials, it could efficiently predict the thermal and mechanical
properties of 2D nanostructures.
