Thermo-mechanical properties of nitrogenated holey graphene (C2N): A comparison of machine-learning-based and classical interatomic potentials

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